{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T13:18:53Z","timestamp":1774876733927,"version":"3.50.1"},"reference-count":199,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T00:00:00Z","timestamp":1768003200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T00:00:00Z","timestamp":1771804800000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Ethics"],"DOI":"10.1186\/s12910-025-01372-5","type":"journal-article","created":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T12:04:00Z","timestamp":1768046640000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["\u201cFirst, do no harm\u201d in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence- A systematic literature review"],"prefix":"10.1186","volume":"27","author":[{"given":"Margarida","family":"Mateus","sequence":"first","affiliation":[]},{"given":"Irina","family":"Alho","sequence":"additional","affiliation":[]},{"given":"Ana Lu\u00edsa","family":"Neves","sequence":"additional","affiliation":[]},{"given":"Henrique","family":"Lopes","sequence":"additional","affiliation":[]},{"given":"M\u00f3nica","family":"Correia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,10]]},"reference":[{"key":"1372_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1093\/mind\/LIX.236.433","volume":"49","author":"AM Turing","year":"1950","unstructured":"Turing AM. Computing machinery and intelligence. Mind. 1950;49:433\u201360. https:\/\/doi.org\/10.1093\/mind\/LIX.236.433.","journal-title":"Mind"},{"key":"1372_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/life14050557","author":"R Hirani","year":"2024","unstructured":"Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, et al. Artificial intelligence and healthcare: a journey through history, present innovations, and future possibilities. Life. 2024. https:\/\/doi.org\/10.3390\/life14050557.","journal-title":"Life"},{"issue":"10","key":"1372_CR3","doi-asserted-by":"publisher","first-page":"e71625","DOI":"10.7759\/cureus.71625","volume":"16","author":"M Khanam","year":"2024","unstructured":"Khanam M, Akther S, Mizan I, Islam F, Chowdhury S, Ahsan NM, et al. The potential of artificial intelligence in unveiling healthcare\u2019s future. Cureus. 2024;16(10):e71625. https:\/\/doi.org\/10.7759\/cureus.71625.","journal-title":"Cureus"},{"issue":"3","key":"1372_CR4","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1007\/s11030-021-10217-3","volume":"25","author":"R Gupta","year":"2021","unstructured":"Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers. 2021;25(3):1315\u201360. https:\/\/doi.org\/10.1007\/s11030-021-10217-3.","journal-title":"Mol Divers"},{"issue":"2","key":"1372_CR5","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1080\/13645706.2019.1575882","volume":"28","author":"Y Mintz","year":"2019","unstructured":"Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73\u201381. https:\/\/doi.org\/10.1080\/13645706.2019.1575882.","journal-title":"Minim Invasive Ther Allied Technol"},{"key":"1372_CR6","doi-asserted-by":"publisher","DOI":"10.2174\/0115680266339394250225112747","author":"K Singh","year":"2025","unstructured":"Singh K, Prabhu A, Kaur N. The impact and role of artificial intelligence (AI) in healthcare: systematic review. Curr Top Med Chem. 2025. https:\/\/doi.org\/10.2174\/0115680266339394250225112747.","journal-title":"Curr Top Med Chem"},{"issue":"1","key":"1372_CR7","doi-asserted-by":"publisher","DOI":"10.1186\/s12909-023-04698-z","volume":"23","author":"SA Alowais","year":"2023","unstructured":"Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23(1):689. https:\/\/doi.org\/10.1186\/s12909-023-04698-z.","journal-title":"BMC Med Educ"},{"issue":"5","key":"1372_CR8","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1002\/hcs2.115","volume":"3","author":"A Thacharodi","year":"2024","unstructured":"Thacharodi A, Singh P, Meenatchi R, Tawfeeq Ahmed ZH, Kumar RRS. Revolutionizing healthcare and medicine: the impact of modern technologies for a healthier future-A comprehensive review. Health Care Sci. 2024;3(5):329\u201349. https:\/\/doi.org\/10.1002\/hcs2.115.","journal-title":"Health Care Sci"},{"issue":"1","key":"1372_CR9","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1097\/RCT.0000000000001247","volume":"46","author":"AW Moawad","year":"2022","unstructured":"Moawad AW, Fuentes DT, ElBanan MG, Shalaby AS, Guccione J, Kamel S, et al. Artificial intelligence in diagnostic radiology: where do we stand, challenges, and opportunities. J Comput Assist Tomogr. 2022;46(1):78\u201390. https:\/\/doi.org\/10.1097\/RCT.0000000000001247.","journal-title":"J Comput Assist Tomogr"},{"issue":"9 Pt B","key":"1372_CR10","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1016\/j.jacr.2019.05.047","volume":"16","author":"T Martin Noguerol","year":"2019","unstructured":"Martin Noguerol T, Paulano-Godino F, Martin-Valdivia MT, Menias CO, Luna A. Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology. J Am Coll Radiol. 2019;16(9 Pt B):1239\u201347. https:\/\/doi.org\/10.1016\/j.jacr.2019.05.047.","journal-title":"J Am Coll Radiol"},{"issue":"8","key":"1372_CR11","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500\u201310. https:\/\/doi.org\/10.1038\/s41568-018-0016-5.","journal-title":"Nat Rev Cancer"},{"key":"1372_CR12","doi-asserted-by":"publisher","first-page":"638","DOI":"10.3233\/SHTI240494","volume":"316","author":"TA Ouattara","year":"2024","unstructured":"Ouattara TA, Barro SG, Staccini P. Artificial intelligence system for automated breast cancer detection in pathology in Burkina Faso: methodology overview. Stud Health Technol Inform. 2024;316:638\u201342. https:\/\/doi.org\/10.3233\/SHTI240494.","journal-title":"Stud Health Technol Inform"},{"issue":"1","key":"1372_CR13","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-024-13190-w","volume":"24","author":"J Koyama","year":"2024","unstructured":"Koyama J, Morise M, Furukawa T, Oyama S, Matsuzawa R, Tanaka I, et al. Artificial intelligence-based personalized survival prediction using clinical and radiomics features in patients with advanced non-small cell lung cancer. BMC Cancer. 2024;24(1):1417. https:\/\/doi.org\/10.1186\/s12885-024-13190-w.","journal-title":"BMC Cancer"},{"key":"1372_CR14","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241253757","volume":"10","author":"MF Almufareh","year":"2024","unstructured":"Almufareh MF, Tariq N, Humayun M, Khan FA. Melanoma identification and classification model based on fine-tuned convolutional neural network. DIGITAL HEALTH. 2024;10:20552076241253757. https:\/\/doi.org\/10.1177\/20552076241253757.","journal-title":"DIGITAL HEALTH"},{"issue":"1","key":"1372_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-79111-w","volume":"14","author":"L Tang","year":"2024","unstructured":"Tang L, Hu Q, Wang X, Liu L, Zheng H, Yu W, et al. A multimodal fusion network based on a cross-attention mechanism for the classification of Parkinsonian tremor and essential tremor. Sci Rep. 2024;14(1):28050. https:\/\/doi.org\/10.1038\/s41598-024-79111-w.","journal-title":"Sci Rep"},{"key":"1372_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s00259-024-06961-x","author":"L Lopes","year":"2024","unstructured":"Lopes L, Jiao F, Xue S, Pyka T, Krieger K, Ge J, et al. Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach. Eur J Nucl Med Mol Imaging. 2024. https:\/\/doi.org\/10.1007\/s00259-024-06961-x.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"1372_CR17","doi-asserted-by":"publisher","DOI":"10.2174\/0115748871330861241030143321","author":"S Tripathi","year":"2024","unstructured":"Tripathi S, Sharma Y, Kumar D. Unraveling the mysteries of Alzheimer\u2019s disease using artificial intelligence. Rev Recent Clin Trials. 2024. https:\/\/doi.org\/10.2174\/0115748871330861241030143321.","journal-title":"Rev Recent Clin Trials"},{"issue":"2","key":"1372_CR18","doi-asserted-by":"publisher","DOI":"10.5837\/bjc.2024.015","volume":"31","author":"S Brown","year":"2024","unstructured":"Brown S. Heartificial intelligence: in what ways will artificial intelligence lead to changes in cardiology over the next 10 years. Br J Cardiol. 2024;31(2):015. https:\/\/doi.org\/10.5837\/bjc.2024.015.","journal-title":"Br J Cardiol"},{"key":"1372_CR19","doi-asserted-by":"publisher","DOI":"10.23736\/S2724-5683.24.06288-4","author":"MR Milne","year":"2024","unstructured":"Milne MR, Ahmad HK, Buchlak QD, Esmaili N, Tang C, Seah J, et al. Applications and potential of machine learning augmented chest X-ray interpretation in cardiology. Minerva Cardiol Angiol. 2024. https:\/\/doi.org\/10.23736\/S2724-5683.24.06288-4.","journal-title":"Minerva Cardiol Angiol"},{"issue":"1","key":"1372_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s12933-024-02503-9","volume":"23","author":"X Liu","year":"2024","unstructured":"Liu X, Xie Z, Zhang Y, Huang J, Kuang L, Li X, et al. Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study. Cardiovasc Diabetol. 2024;23(1):407. https:\/\/doi.org\/10.1186\/s12933-024-02503-9.","journal-title":"Cardiovasc Diabetol"},{"key":"1372_CR21","doi-asserted-by":"publisher","DOI":"10.1038\/s41433-024-03460-z","author":"S Frank-Publig","year":"2024","unstructured":"Frank-Publig S, Birner K, Riedl S, Reiter GS, Schmidt-Erfurth U. Artificial intelligence in assessing progression of age-related macular degeneration. Eye Lond. 2024. https:\/\/doi.org\/10.1038\/s41433-024-03460-z.","journal-title":"Eye Lond"},{"key":"1372_CR22","doi-asserted-by":"publisher","DOI":"10.1111\/jep.14237","author":"A Crew","year":"2024","unstructured":"Crew A, Reidy C, van der Westhuizen HM, Graham M. A narrative review of ethical issues in the use of artificial intelligence enabled diagnostics for diabetic retinopathy. J Eval Clin Pract. 2024. https:\/\/doi.org\/10.1111\/jep.14237.","journal-title":"J Eval Clin Pract"},{"issue":"3","key":"1372_CR23","doi-asserted-by":"publisher","first-page":"207","DOI":"10.22336\/rjo.2023.37","volume":"67","author":"SI Popescu Patoni","year":"2023","unstructured":"Popescu Patoni SI, Musat AAM, Patoni C, Popescu MN, Munteanu M, Costache IB, et al. Artificial intelligence in ophthalmology. Rom J Ophthalmol. 2023;67(3):207\u201313. https:\/\/doi.org\/10.22336\/rjo.2023.37.","journal-title":"Rom J Ophthalmol"},{"key":"1372_CR24","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics14212442","author":"SS Alharbi","year":"2024","unstructured":"Alharbi SS, Alhasson HF. Exploring the applications of artificial intelligence in dental image detection: a systematic review. Diagnostics. 2024. https:\/\/doi.org\/10.3390\/diagnostics14212442.","journal-title":"Diagnostics"},{"key":"1372_CR25","doi-asserted-by":"publisher","DOI":"10.1089\/ten.teb.2024.0216","author":"NH Mohd Nor","year":"2024","unstructured":"Mohd Nor NH, Mansor NI, Hasim NA. Artificial neural networks: a new frontier in dental tissue regeneration. Tissue Eng Part B Rev. 2024. https:\/\/doi.org\/10.1089\/ten.teb.2024.0216.","journal-title":"Tissue Eng Part B Rev"},{"key":"1372_CR26","doi-asserted-by":"publisher","DOI":"10.2196\/50451","volume":"7","author":"R Barlow","year":"2024","unstructured":"Barlow R, Bewley A, Gkini MA. AI in psoriatic disease: scoping review. JMIR Dermatol. 2024;7:e50451. https:\/\/doi.org\/10.2196\/50451.","journal-title":"JMIR Dermatol"},{"key":"1372_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16213592","author":"AM Witkowski","year":"2024","unstructured":"Witkowski AM, Burshtein J, Christopher M, Cockerell C, Correa L, Cotter D, et al. Clinical utility of a digital dermoscopy image-based artificial intelligence device in the diagnosis and management of skin cancer by dermatologists. Cancers (Basel). 2024. https:\/\/doi.org\/10.3390\/cancers16213592.","journal-title":"Cancers (Basel)"},{"issue":"1","key":"1372_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.neurad.2024.101231","volume":"52","author":"M Isikbay","year":"2024","unstructured":"Isikbay M, Caton MT, Narvid J, Talbott J, Cha S, Calabrese E. Deep learning segmentation-based bone removal from computed tomography of the brain improves subdural hematoma detection. J Neuroradiol. 2024;52(1):101231. https:\/\/doi.org\/10.1016\/j.neurad.2024.101231.","journal-title":"J Neuroradiol"},{"issue":"11","key":"1372_CR29","doi-asserted-by":"publisher","DOI":"10.1002\/brb3.70163","volume":"14","author":"Z Mo","year":"2024","unstructured":"Mo Z, Sui H, Lv Z, Huang X, Li G, Shen D, et al. Accelerating brain MR imaging with multisequence and convolutional neural networks. Brain Behav. 2024;14(11):e70163. https:\/\/doi.org\/10.1002\/brb3.70163.","journal-title":"Brain Behav"},{"issue":"1","key":"1372_CR30","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-79610-w","volume":"14","author":"C Kim","year":"2024","unstructured":"Kim C, Kang M, Yuh WT, Lee SL, Lee JJ, Hou JU, et al. Comparative efficacy of anteroposterior and lateral X-ray based deep learning in the detection of osteoporotic vertebral compression fracture. Sci Rep. 2024;14(1):28388. https:\/\/doi.org\/10.1038\/s41598-024-79610-w.","journal-title":"Sci Rep"},{"key":"1372_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s11604-024-01702-4","author":"A Lo Mastro","year":"2024","unstructured":"Lo Mastro A, Grassi E, Berritto D, Russo A, Reginelli A, Guerra E, et al. Artificial intelligence in fracture detection on radiographs: a literature review. Jpn J Radiol. 2024. https:\/\/doi.org\/10.1007\/s11604-024-01702-4.","journal-title":"Jpn J Radiol"},{"issue":"1","key":"1372_CR32","doi-asserted-by":"publisher","DOI":"10.1186\/s12887-024-05204-0","volume":"24","author":"XH Lan","year":"2024","unstructured":"Lan XH, Zhang YX, Yuan WH, Shi F, Guo WL. Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays. BMC Pediatr. 2024;24(1):720. https:\/\/doi.org\/10.1186\/s12887-024-05204-0.","journal-title":"BMC Pediatr"},{"issue":"8","key":"1372_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.labinv.2023.100176","volume":"103","author":"TJ DuCote","year":"2023","unstructured":"DuCote TJ, Naughton KJ, Skaggs EM, Bocklage TJ, Allison DB, Brainson CF. Using artificial intelligence to identify tumor microenvironment heterogeneity in non-small cell lung cancers. Lab Invest. 2023;103(8):100176. https:\/\/doi.org\/10.1016\/j.labinv.2023.100176.","journal-title":"Lab Invest"},{"issue":"22","key":"1372_CR34","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.70383","volume":"13","author":"KWL So","year":"2024","unstructured":"So KWL, Leung EMC, Ng T, Tsui R, Cheung JPY, Choi SW. Machine learning models to predict bone metastasis risk in patients with lung cancer. Cancer Med. 2024;13(22):e70383. https:\/\/doi.org\/10.1002\/cam4.70383.","journal-title":"Cancer Med"},{"issue":"1","key":"1372_CR35","doi-asserted-by":"publisher","first-page":"102187","DOI":"10.1016\/j.labinv.2024.102187","volume":"105","author":"L Xia","year":"2024","unstructured":"Xia L, Xu T, Zheng Y, Li B, Ao Y, Li X, et al. Lymph node metastasis prediction from in situ lung squamous cell carcinoma histopathology images using deep learning. Lab Invest. 2024;105(1):102187. https:\/\/doi.org\/10.1016\/j.labinv.2024.102187.","journal-title":"Lab Invest"},{"issue":"1","key":"1372_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-79363-6","volume":"14","author":"S Iniyan","year":"2024","unstructured":"Iniyan S, Raja MS, Poonguzhali R, Vikram A, Ramesh JVN, Mohanty SN, et al. Enhanced breast cancer diagnosis through integration of computer vision with fusion based joint transfer learning using multi modality medical images. Sci Rep. 2024;14(1):28376. https:\/\/doi.org\/10.1038\/s41598-024-79363-6.","journal-title":"Sci Rep"},{"issue":"1","key":"1372_CR37","doi-asserted-by":"publisher","DOI":"10.1186\/s12905-024-03264-z","volume":"24","author":"WH Zhang","year":"2024","unstructured":"Zhang WH, Tan Y, Huang Z, Tan QX, Zhang YM, Wei CY. Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study. BMC Womens Health. 2024;24(1):442. https:\/\/doi.org\/10.1186\/s12905-024-03264-z.","journal-title":"BMC Womens Health"},{"issue":"1","key":"1372_CR38","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-28079-0","volume":"13","author":"J Devasia","year":"2023","unstructured":"Devasia J, Goswami H, Lakshminarayanan S, Rajaram M, Adithan S. Deep learning classification of active tuberculosis lung zones wise manifestations using chest X-rays: a multi label approach. Sci Rep. 2023;13(1):887. https:\/\/doi.org\/10.1038\/s41598-023-28079-0.","journal-title":"Sci Rep"},{"issue":"1","key":"1372_CR39","doi-asserted-by":"publisher","DOI":"10.1186\/s12880-022-00904-4","volume":"22","author":"L Visuna","year":"2022","unstructured":"Visuna L, Yang D, Garcia-Blas J, Carretero J. Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning. BMC Med Imaging. 2022;22(1):178. https:\/\/doi.org\/10.1186\/s12880-022-00904-4.","journal-title":"BMC Med Imaging"},{"issue":"1","key":"1372_CR40","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-74135-4","volume":"10","author":"Z Zhao","year":"2020","unstructured":"Zhao Z, Pi Y, Jiang L, Xiang Y, Wei J, Yang P, et al. Deep neural network based artificial intelligence assisted diagnosis of bone scintigraphy for cancer bone metastasis. Sci Rep. 2020;10(1):17046. https:\/\/doi.org\/10.1038\/s41598-020-74135-4.","journal-title":"Sci Rep"},{"issue":"12","key":"1372_CR41","doi-asserted-by":"publisher","DOI":"10.1007\/s00432-024-06002-y","volume":"150","author":"G Wang","year":"2024","unstructured":"Wang G, Jia M, Zhou Q, Xu S, Zhao Y, Wang Q, et al. Multi-classification of breast cancer pathology images based on a two-stage hybrid network. J Cancer Res Clin Oncol. 2024;150(12):505. https:\/\/doi.org\/10.1007\/s00432-024-06002-y.","journal-title":"J Cancer Res Clin Oncol"},{"key":"1372_CR42","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1429817","volume":"15","author":"X Wang","year":"2024","unstructured":"Wang X, Hu H, Yan G, Zheng B, Luo J, Fan J. Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning. Front Immunol. 2024;15:1429817. https:\/\/doi.org\/10.3389\/fimmu.2024.1429817.","journal-title":"Front Immunol"},{"key":"1372_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.103063","volume":"93","author":"DJ Geijs","year":"2024","unstructured":"Geijs DJ, Dooper S, Aswolinskiy W, Hillen LM, Amir AL, Litjens G. Detection and subtyping of basal cell carcinoma in whole-slide histopathology using weakly-supervised learning. Med Image Anal. 2024;93:103063. https:\/\/doi.org\/10.1016\/j.media.2023.103063.","journal-title":"Med Image Anal"},{"issue":"3Part\u2013II","key":"1372_CR44","doi-asserted-by":"publisher","first-page":"271","DOI":"10.12669\/pjms.40.3.8454","volume":"40","author":"L Liu","year":"2024","unstructured":"Liu L, Ni Z, Zhang J, Zhao J, Shen J. Application of artificial intelligence-based dual source CT scanning in the differentiation of lung adenocarcinoma in situ and minimally invasive adenocarcinoma. Pak J Med Sci. 2024;40(3Part\u2013II):271\u20136. https:\/\/doi.org\/10.12669\/pjms.40.3.8454.","journal-title":"Pak J Med Sci"},{"issue":"1","key":"1372_CR45","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","volume":"28","author":"W Bulten","year":"2022","unstructured":"Bulten W, Kartasalo K, Chen PC, Strom P, Pinckaers H, Nagpal K, et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med. 2022;28(1):154\u201363. https:\/\/doi.org\/10.1038\/s41591-021-01620-2.","journal-title":"Nat Med"},{"issue":"1","key":"1372_CR46","doi-asserted-by":"publisher","first-page":"27545","DOI":"10.1038\/s41598-024-79391-2","volume":"14","author":"A Sarker","year":"2024","unstructured":"Sarker A, Aziz MA, Hossen MB, Mollah MMH, Al A, Mollah MNH. Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches. Sci Rep. 2024;14(1):27545. https:\/\/doi.org\/10.1038\/s41598-024-79391-2.","journal-title":"Sci Rep"},{"key":"1372_CR47","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1365524","volume":"11","author":"S Zeng","year":"2024","unstructured":"Zeng S, Qing Q, Xu W, Yu S, Zheng M, Tan H, et al. Personalized anesthesia and precision medicine: a comprehensive review of genetic factors, artificial intelligence, and patient-specific factors. Front Med (Lausanne). 2024;11:1365524. https:\/\/doi.org\/10.3389\/fmed.2024.1365524.","journal-title":"Front Med (Lausanne)"},{"issue":"1","key":"1372_CR48","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/bs.pmbts.2022.03.002","volume":"190","author":"M Sahu","year":"2022","unstructured":"Sahu M, Gupta R, Ambasta RK, Kumar P. Artificial intelligence and machine learning in precision medicine: a paradigm shift in big data analysis. Prog Mol Biol Transl Sci. 2022;190(1):57\u2013100. https:\/\/doi.org\/10.1016\/bs.pmbts.2022.03.002.","journal-title":"Prog Mol Biol Transl Sci"},{"key":"1372_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejphar.2024.177103","volume":"985","author":"MK Yadav","year":"2024","unstructured":"Yadav MK, Dahiya V, Tripathi MK, Chaturvedi N, Rashmi M, Ghosh A, et al. Unleashing the future: the revolutionary role of machine learning and artificial intelligence in drug discovery. Eur J Pharmacol. 2024;985:177103. https:\/\/doi.org\/10.1016\/j.ejphar.2024.177103.","journal-title":"Eur J Pharmacol"},{"issue":"1","key":"1372_CR50","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-79799-w","volume":"14","author":"F Noor","year":"2024","unstructured":"Noor F, Junaid M, Almalki AH, Almaghrabi M, Ghazanfar S, Tahir Ul Qamar M. Deep learning pipeline for accelerating virtual screening in drug discovery. Sci Rep. 2024;14(1):28321. https:\/\/doi.org\/10.1038\/s41598-024-79799-w.","journal-title":"Sci Rep"},{"issue":"1","key":"1372_CR51","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-024-01346-5","volume":"7","author":"R Yang","year":"2024","unstructured":"Yang R, Zhou H, Wang F, Yang G. DigFrag as a digital fragmentation method used for artificial intelligence-based drug design. Commun Chem. 2024;7(1):258. https:\/\/doi.org\/10.1038\/s42004-024-01346-5.","journal-title":"Commun Chem"},{"key":"1372_CR52","doi-asserted-by":"publisher","DOI":"10.1136\/bmjophth-2024-001824","author":"M Azzopardi","year":"2024","unstructured":"Azzopardi M, Ng B, Logeswaran A, Loizou C, Cheong RCT, Gireesh P, et al. Artificial intelligence chatbots as sources of patient education material for cataract surgery: ChatGPT-4 versus Google Bard. BMJ Open Ophthalmol. 2024. https:\/\/doi.org\/10.1136\/bmjophth-2024-001824.","journal-title":"BMJ Open Ophthalmol"},{"issue":"9","key":"1372_CR53","doi-asserted-by":"publisher","first-page":"e69996","DOI":"10.7759\/cureus.69996","volume":"16","author":"BJ Behers","year":"2024","unstructured":"Behers BJ, Stephenson-Moe CA, Gibons RM, Vargas IA, Wojtas CN, Rosario MA, et al. Assessing the quality of patient education materials on cardiac catheterization from artificial intelligence chatbots: an observational Cross-Sectional study. Cureus. 2024;16(9):e69996. https:\/\/doi.org\/10.7759\/cureus.69996.","journal-title":"Cureus"},{"key":"1372_CR54","doi-asserted-by":"publisher","DOI":"10.1002\/jso.27966","author":"K Khabaz","year":"2024","unstructured":"Khabaz K, Newman-Hung NJ, Kallini JR, Kendal J, Christ AB, Bernthal NM, et al. Assessment of artificial intelligence chatbot responses to common patient questions on bone sarcoma. J Surg Oncol. 2024. https:\/\/doi.org\/10.1002\/jso.27966.","journal-title":"J Surg Oncol"},{"issue":"2","key":"1372_CR55","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1002\/pros.24814","volume":"85","author":"CJ Warren","year":"2025","unstructured":"Warren CJ, Payne NG, Edmonds VS, Voleti SS, Choudry MM, Punjani N, et al. Quality of chatbot information related to benign prostatic hyperplasia. Prostate. 2025;85(2):175\u201380. https:\/\/doi.org\/10.1002\/pros.24814.","journal-title":"Prostate"},{"issue":"1","key":"1372_CR56","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-024-02125-4","volume":"48","author":"A Malak","year":"2024","unstructured":"Malak A, Sahin MF. How useful are current chatbots regarding urology patient information? Comparison of the ten most popular chatbots\u2019 responses about female urinary incontinence. J Med Syst. 2024;48(1):102. https:\/\/doi.org\/10.1007\/s10916-024-02125-4.","journal-title":"J Med Syst"},{"issue":"10","key":"1372_CR57","doi-asserted-by":"publisher","first-page":"e71467","DOI":"10.7759\/cureus.71467","volume":"16","author":"SS Janti","year":"2024","unstructured":"Janti SS, Saluja R, Tiwari N, Kolavai RR, Mali K, Arora AJ, et al. Evaluation of the clinical impact of a smartphone application for cataract detection. Cureus. 2024;16(10):e71467. https:\/\/doi.org\/10.7759\/cureus.71467.","journal-title":"Cureus"},{"issue":"10","key":"1372_CR58","doi-asserted-by":"publisher","DOI":"10.1177\/1357633X241291222","volume":"31","author":"K Moulaei","year":"2024","unstructured":"Moulaei K, Parhizkar Roudsari P, Shahrokhi Sardoo A, Hosseini M, Anabestani M, Moulaei R, et al. Assessing the impact of telemedicine interventions on systolic and diastolic blood pressure reduction: a systematic review and meta-analysis. J Telemed Telecare. 2024;1357633X241291222. https:\/\/doi.org\/10.1177\/1357633X241291222.","journal-title":"J Telemed Telecare"},{"issue":"2","key":"1372_CR59","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1016\/j.nurpra.2020.09.013","volume":"17","author":"SN Gajarawala","year":"2021","unstructured":"Gajarawala SN, Pelkowski JN. Telehealth benefits and barriers. J Nurse Pract. 2021;17(2):218\u201321. https:\/\/doi.org\/10.1016\/j.nurpra.2020.09.013.","journal-title":"J Nurse Pract"},{"issue":"11","key":"1372_CR60","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1089\/tmj.2020.0492","volume":"27","author":"S Saiyed","year":"2021","unstructured":"Saiyed S, Nguyen A, Singh R. Physician perspective and key satisfaction indicators with rapid telehealth adoption during the coronavirus disease 2019 pandemic. Telemed J E-Health. 2021;27(11):1225\u201334. https:\/\/doi.org\/10.1089\/tmj.2020.0492.","journal-title":"Telemed J E-Health"},{"key":"1372_CR61","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2020-100302","author":"S Mantena","year":"2021","unstructured":"Mantena S, Keshavjee S. Strengthening healthcare delivery with remote patient monitoring in the time of COVID-19. BMJ Health Care Inform. 2021. https:\/\/doi.org\/10.1136\/bmjhci-2020-100302.","journal-title":"BMJ Health Care Inform"},{"key":"1372_CR62","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2020.567413","volume":"11","author":"F Motolese","year":"2020","unstructured":"Motolese F, Magliozzi A, Puttini F, Rossi M, Capone F, Karlinski K, et al. Parkinson\u2019s disease remote patient monitoring during the COVID-19 lockdown. Front Neurol. 2020;11:567413. https:\/\/doi.org\/10.3389\/fneur.2020.567413.","journal-title":"Front Neurol"},{"issue":"1","key":"1372_CR63","doi-asserted-by":"publisher","DOI":"10.1186\/s12984-023-01149-0","volume":"20","author":"L Li","year":"2023","unstructured":"Li L, Foo MJ, Chen J, Tan KY, Cai J, Swaminathan R, et al. Mobile Robotic Balance Assistant (MRBA): a gait assistive and fall intervention robot for daily living. J Neuroeng Rehabil. 2023;20(1):29. https:\/\/doi.org\/10.1186\/s12984-023-01149-0.","journal-title":"J Neuroeng Rehabil"},{"issue":"5","key":"1372_CR64","doi-asserted-by":"publisher","first-page":"e181617","DOI":"10.1001\/jamanetworkopen.2018.1617","volume":"1","author":"KL Margolis","year":"2018","unstructured":"Margolis KL, Asche SE, Dehmer SP, Bergdall AR, Green BB, Sperl-Hillen JM, et al. Long-term outcomes of the effects of home blood pressure telemonitoring and pharmacist management on blood pressure among adults with uncontrolled hypertension: Follow-up of a cluster randomized clinical trial. JAMA Netw Open. 2018;1(5):e181617. https:\/\/doi.org\/10.1001\/jamanetworkopen.2018.1617.","journal-title":"JAMA Netw Open"},{"key":"1372_CR65","doi-asserted-by":"publisher","DOI":"10.1002\/adsr.202300009","volume":"3","author":"M Chen","year":"2024","unstructured":"Chen M, Cui D, Haick H, Tang N. Artificial Intelligence-Based Medical Sensors for Healthcare System. Adv Sensor Res. 2024;3:2300009. https:\/\/doi.org\/10.1002\/adsr.202300009.","journal-title":"Adv Sensor Res"},{"key":"1372_CR66","doi-asserted-by":"publisher","DOI":"10.3390\/s23239498","author":"S Shajari","year":"2023","unstructured":"Shajari S, Kuruvinashetti K, Komeili A, Sundararaj U. The emergence of AI-based wearable sensors for digital health technology: a review. Sensors. 2023. https:\/\/doi.org\/10.3390\/s23239498.","journal-title":"Sensors"},{"issue":"3","key":"1372_CR67","doi-asserted-by":"publisher","first-page":"3557","DOI":"10.1021\/acsnano.1c00085","volume":"15","author":"H Haick","year":"2021","unstructured":"Haick H, Tang N. Artificial intelligence in medical sensors for clinical decisions. ACS Nano. 2021;15(3):3557\u201367. https:\/\/doi.org\/10.1021\/acsnano.1c00085.","journal-title":"ACS Nano"},{"key":"1372_CR68","doi-asserted-by":"publisher","DOI":"10.3389\/frobt.2024.1325143","volume":"11","author":"J Schneider","year":"2024","unstructured":"Schneider J, Brunett M, Gebert A, Gisa K, Hermann A, Lengenfelder C, et al. HoLLiECares - Development of a multi-functional robot for professional care. Front Robot AI. 2024;11:1325143. https:\/\/doi.org\/10.3389\/frobt.2024.1325143.","journal-title":"Front Robot AI"},{"key":"1372_CR69","doi-asserted-by":"publisher","DOI":"10.3390\/biomedicines12102415","author":"A Calderone","year":"2024","unstructured":"Calderone A, Latella D, Bonanno M, Quartarone A, Mojdehdehbaher S, Celesti A, et al. Towards transforming neurorehabilitation: the impact of artificial intelligence on diagnosis and treatment of neurological disorders. Biomedicines. 2024. https:\/\/doi.org\/10.3390\/biomedicines12102415.","journal-title":"Biomedicines"},{"key":"1372_CR70","doi-asserted-by":"publisher","first-page":"e52443","DOI":"10.2196\/52443","volume":"7","author":"E Otaka","year":"2024","unstructured":"Otaka E, Osawa A, Kato K, Obayashi Y, Uehara S, Kamiya M, et al. Positive emotional responses to socially assistive robots in people with dementia: pilot study. JMIR Aging. 2024;7:e52443. https:\/\/doi.org\/10.2196\/52443.","journal-title":"JMIR Aging"},{"issue":"8","key":"1372_CR71","doi-asserted-by":"publisher","first-page":"1027","DOI":"10.1159\/000529849","volume":"69","author":"CJ Hsieh","year":"2023","unstructured":"Hsieh CJ, Li PS, Wang CH, Lin SL, Hsu TC, Tsai CT. Socially assistive robots for people living with dementia in long-term facilities: a systematic review and meta-analysis of randomized controlled trials. Gerontology. 2023;69(8):1027\u201342. https:\/\/doi.org\/10.1159\/000529849.","journal-title":"Gerontology"},{"issue":"1","key":"1372_CR72","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-024-02117-4","volume":"48","author":"YW Lim","year":"2024","unstructured":"Lim YW, Tan SW, Tan CYB, Lee DHM, Siow WT, Heng DGN, et al. An assessment of an inpatient robotic nurse assistant: a mixed-method study. J Med Syst. 2024;48(1):99. https:\/\/doi.org\/10.1007\/s10916-024-02117-4.","journal-title":"J Med Syst"},{"issue":"5","key":"1372_CR73","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1093\/bfgp\/elae013","volume":"23","author":"D Acharya","year":"2024","unstructured":"Acharya D, Mukhopadhyay A. A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology. Brief Funct Genomics. 2024;23(5):549\u201360. https:\/\/doi.org\/10.1093\/bfgp\/elae013.","journal-title":"Brief Funct Genomics"},{"key":"1372_CR74","doi-asserted-by":"publisher","DOI":"10.3390\/ijms20194781","author":"M Olivier","year":"2019","unstructured":"Olivier M, Asmis R, Hawkins GA, Howard TD, Cox LA. The need for multi-omics biomarker signatures in precision medicine. Int J Mol Sci. 2019. https:\/\/doi.org\/10.3390\/ijms20194781.","journal-title":"Int J Mol Sci"},{"issue":"5","key":"1372_CR75","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1038\/nrg.2018.4","volume":"19","author":"KJ Karczewski","year":"2018","unstructured":"Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet. 2018;19(5):299\u2013310. https:\/\/doi.org\/10.1038\/nrg.2018.4.","journal-title":"Nat Rev Genet"},{"key":"1372_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.canlet.2025.217821","volume":"627","author":"S Sarvepalli","year":"2025","unstructured":"Sarvepalli S, Vadarevu S. Role of artificial intelligence in cancer drug discovery and development. Cancer Lett. 2025;627:217821. https:\/\/doi.org\/10.1016\/j.canlet.2025.217821.","journal-title":"Cancer Lett"},{"key":"1372_CR77","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1404338","volume":"11","author":"L Cortial","year":"2024","unstructured":"Cortial L, Montero V, Tourlet S, Del Bano J, Blin O. Artificial intelligence in drug repurposing for rare diseases: a mini-review. Front Med (Lausanne). 2024;11:1404338. https:\/\/doi.org\/10.3389\/fmed.2024.1404338.","journal-title":"Front Med (Lausanne)"},{"issue":"9","key":"1372_CR78","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.69405","volume":"16","author":"M Marques","year":"2024","unstructured":"Marques M, Almeida A, Pereira H. The medicine revolution through artificial intelligence: ethical challenges of machine learning algorithms in decision-making. Cureus. 2024;16(9):e69405. https:\/\/doi.org\/10.7759\/cureus.69405.","journal-title":"Cureus"},{"issue":"8","key":"1372_CR79","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.43262","volume":"15","author":"M Jeyaraman","year":"2023","unstructured":"Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the ethical enigma: artificial intelligence in healthcare. Cureus. 2023;15(8):e43262. https:\/\/doi.org\/10.7759\/cureus.43262.","journal-title":"Cureus"},{"issue":"8","key":"1372_CR80","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.67503","volume":"16","author":"AB Mudey","year":"2024","unstructured":"Mudey AB, Dhonde AS, Chandrachood MV. Artificial intelligence in healthcare with an emphasis on public health. Cureus. 2024;16(8):e67503. https:\/\/doi.org\/10.7759\/cureus.67503.","journal-title":"Cureus"},{"key":"1372_CR81","doi-asserted-by":"publisher","DOI":"10.3390\/tropicalmed9100228","author":"A Sarantopoulos","year":"2024","unstructured":"Sarantopoulos A, Mastori Kourmpani C, Yokarasa AL, Makamanzi C, Antoniou P, Spernovasilis N, et al. Artificial intelligence in infectious disease clinical practice: an overview of gaps, opportunities, and limitations. Trop Med Infect Dis. 2024. https:\/\/doi.org\/10.3390\/tropicalmed9100228.","journal-title":"Trop Med Infect Dis"},{"issue":"9","key":"1372_CR82","doi-asserted-by":"publisher","first-page":"e44658","DOI":"10.7759\/cureus.44658","volume":"15","author":"J Iqbal","year":"2023","unstructured":"Iqbal J, Cortes Jaimes DC, Makineni P, Subramani S, Hemaida S, Thugu TR, et al. Reimagining healthcare: unleashing the power of artificial intelligence in medicine. Cureus. 2023;15(9):e44658. https:\/\/doi.org\/10.7759\/cureus.44658.","journal-title":"Cureus"},{"issue":"4","key":"1372_CR83","doi-asserted-by":"publisher","first-page":"e388","DOI":"10.1097\/MJT.0000000000001693","volume":"31","author":"V Astarastoae","year":"2024","unstructured":"Astarastoae V, Rogozea LM, Leasu F, Ioan BG. Ethical dilemmas of using artificial intelligence in medicine. Am J Ther. 2024;31(4):e388\u201397. https:\/\/doi.org\/10.1097\/MJT.0000000000001693.","journal-title":"Am J Ther"},{"issue":"1","key":"1372_CR84","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-024-01221-6","volume":"7","author":"J Schmidt","year":"2024","unstructured":"Schmidt J, Schutte NM, Buttigieg S, Novillo-Ortiz D, Sutherland E, Anderson M, et al. Mapping the regulatory landscape for artificial intelligence in health within the European Union. NPJ Digit Med. 2024;7(1):229. https:\/\/doi.org\/10.1038\/s41746-024-01221-6.","journal-title":"NPJ Digit Med"},{"key":"1372_CR85","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthpol.2024.105152","volume":"149","author":"H van Kolfschooten","year":"2024","unstructured":"van Kolfschooten H, van Oirschot J. The EU Artificial Intelligence Act (2024): implications for healthcare. Health Policy. 2024;149:105152. https:\/\/doi.org\/10.1016\/j.healthpol.2024.105152.","journal-title":"Health Policy"},{"key":"1372_CR86","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.927721","volume":"13","author":"J Meszaros","year":"2022","unstructured":"Meszaros J, Minari J, Huys I. The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Front Genet. 2022;13:927721. https:\/\/doi.org\/10.3389\/fgene.2022.927721.","journal-title":"Front Genet"},{"key":"1372_CR87","unstructured":"European Parliament and Council of the European Union. Regulation (EU) 2024\/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending Regulations (EC) No 300\/2008, (EU) No 167\/2013, (EU) No 168\/2013, (EU) 2018\/858, (EU) 2018\/1139 and (EU) 2019\/2144 and Directives 2014\/90\/EU, (EU) 2016\/797 and (EU) 2020\/1828. Official Journal of the European Union. 2024;L1689. https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj"},{"issue":"11","key":"1372_CR88","doi-asserted-by":"publisher","first-page":"3053","DOI":"10.1038\/s41591-024-03246-6","volume":"30","author":"A Ganna","year":"2024","unstructured":"Ganna A, Carracedo A, Christiansen CF, Di Angelantonio E, Dykstra PA, Dzhambov AM, et al. The European Health Data Space can be a boost for research beyond borders. Nat Med. 2024;30(11):3053\u20136. https:\/\/doi.org\/10.1038\/s41591-024-03246-6.","journal-title":"Nat Med"},{"key":"1372_CR89","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthpol.2023.104861","volume":"135","author":"L Marelli","year":"2023","unstructured":"Marelli L, Stevens M, Sharon T, Van Hoyweghen I, Boeckhout M, Colussi I, et al. The European health data space: too big to succeed? Health Policy. 2023;135:104861. https:\/\/doi.org\/10.1016\/j.healthpol.2023.104861.","journal-title":"Health Policy"},{"key":"1372_CR90","doi-asserted-by":"publisher","unstructured":"Horgan D, Hajduch M, Vrana M, Soderberg J, Hughes N, Omar MI, et al. European health data space-an opportunity now to grasp the future of data-driven healthcare. Healthcare. 2022. https:\/\/doi.org\/10.3390\/healthcare10091629.","DOI":"10.3390\/healthcare10091629"},{"key":"1372_CR91","doi-asserted-by":"publisher","first-page":"48","DOI":"10.3233\/SHTI230062","volume":"302","author":"M Mateus","year":"2023","unstructured":"Mateus M, Loureiro M, Fernandes AR, Oliveira M, Cruz-Correia R. Implementation status of the proposal for a regulation of the European health data space in portugal: are we ready for it? Stud Health Technol Inf. 2023;302:48\u201352. https:\/\/doi.org\/10.3233\/SHTI230062.","journal-title":"Stud Health Technol Inf"},{"key":"1372_CR92","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https:\/\/doi.org\/10.1136\/bmj.n71.","journal-title":"BMJ"},{"issue":"1","key":"1372_CR93","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-020-01332-6","volume":"20","author":"J Amann","year":"2020","unstructured":"Amann J, Blasimme A, Vayena E, Frey D, Madai VI, Precise Qc. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310. https:\/\/doi.org\/10.1186\/s12911-020-01332-6.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"2","key":"1372_CR94","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1007\/s00146-020-01008-9","volume":"36","author":"K Astromsk\u0117","year":"2020","unstructured":"Astromsk\u0117 K, Pei\u010dius E, Astromskis P. Ethical and legal challenges of informed consent applying artificial intelligence in medical diagnostic consultations. AI Soc. 2020;36(2):509\u201320. https:\/\/doi.org\/10.1007\/s00146-020-01008-9.","journal-title":"AI Soc"},{"issue":"4","key":"1372_CR95","doi-asserted-by":"publisher","first-page":"257","DOI":"10.2471\/blt.19.237289","volume":"98","author":"K B\u00e6r\u00f8e","year":"2020","unstructured":"B\u00e6r\u00f8e K, Miyata-Sturm A, Henden E. How to achieve trustworthy artificial intelligence for health. Bull World Health Organ. 2020;98(4):257\u201362. https:\/\/doi.org\/10.2471\/blt.19.237289.","journal-title":"Bull World Health Organ"},{"key":"1372_CR96","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.3233\/shti200330","volume":"270","author":"B Blobel","year":"2020","unstructured":"Blobel B, Ruotsalainen P, Brochhausen M, Oemig F, Uribe GA. Autonomous systems and artificial intelligence in healthcare transformation to 5P medicine - ethical challenges. Stud Health Technol Inf. 2020;270:1089\u201393. https:\/\/doi.org\/10.3233\/shti200330.","journal-title":"Stud Health Technol Inf"},{"issue":"10","key":"1372_CR97","doi-asserted-by":"publisher","first-page":"5510","DOI":"10.1007\/s00330-020-06874-x","volume":"30","author":"M Bukowski","year":"2020","unstructured":"Bukowski M, Farkas R, Beyan O, Moll L, Hahn H, Kiessling F, et al. Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective? Eur Radiol. 2020;30(10):5510\u201324. https:\/\/doi.org\/10.1007\/s00330-020-06874-x.","journal-title":"Eur Radiol"},{"issue":"1","key":"1372_CR98","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1017\/s0963180119000847","volume":"29","author":"S Dalton-Brown","year":"2020","unstructured":"Dalton-Brown S. The ethics of medical AI and the physician-patient relationship. Camb Q Healthc Ethics. 2020;29(1):115\u201321. https:\/\/doi.org\/10.1017\/s0963180119000847.","journal-title":"Camb Q Healthc Ethics"},{"key":"1372_CR99","doi-asserted-by":"publisher","first-page":"137","DOI":"10.3233\/shti200710","volume":"275","author":"P Mangold","year":"2020","unstructured":"Mangold P, Filiot A, Moussa M, Sobanski V, Ficheur G, Andrey P, et al. A decentralized framework for biostatistics and privacy concerns. Stud Health Technol Inf. 2020;275:137\u201341. https:\/\/doi.org\/10.3233\/shti200710.","journal-title":"Stud Health Technol Inf"},{"issue":"2","key":"1372_CR100","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1111\/all.13953","volume":"75","author":"PM Matricardi","year":"2020","unstructured":"Matricardi PM, Dramburg S, Alvarez-Perea A, Antolin-Amerigo D, Apfelbacher C, Atanaskovic-Markovic M, et al. The role of mobile health technologies in allergy care: an EAACI position paper. Allergy. 2020;75(2):259\u201372. https:\/\/doi.org\/10.1111\/all.13953.","journal-title":"Allergy"},{"issue":"1","key":"1372_CR101","doi-asserted-by":"publisher","DOI":"10.1093\/jlb\/lsaa002","volume":"7","author":"T Minssen","year":"2020","unstructured":"Minssen T, Gerke S, Aboy M, Price N, Cohen G. Regulatory responses to medical machine learning. J Law Biosci. 2020;7(1):lsaa002. https:\/\/doi.org\/10.1093\/jlb\/lsaa002.","journal-title":"J Law Biosci"},{"key":"1372_CR102","doi-asserted-by":"publisher","unstructured":"Poulsen A, Fosch-Villaronga E, Burmeister OK. Cybersecurity, value sensing robots for LGBTIQ\u2009+\u2009elderly, and the need for revised codes of conduct. Australasian J Inform Syst. 2020;24. https:\/\/doi.org\/10.3127\/ajis.v24i0.2789.","DOI":"10.3127\/ajis.v24i0.2789"},{"issue":"9","key":"1372_CR103","doi-asserted-by":"publisher","first-page":"1996","DOI":"10.1093\/geronb\/gbz070","volume":"75","author":"T Vandemeulebroucke","year":"2020","unstructured":"Vandemeulebroucke T, Dierckx de Casterl\u00e9 B, Welbergen L, Massart M, Gastmans C. The ethics of socially assistive robots in aged care. A focus group study with older adults in Flanders, Belgium. J Gerontol B Psychol Sci Soc Sci. 2020;75(9):1996\u20132007. https:\/\/doi.org\/10.1093\/geronb\/gbz070.","journal-title":"J Gerontol B Psychol Sci Soc Sci"},{"key":"1372_CR104","doi-asserted-by":"publisher","first-page":"l6927","DOI":"10.1136\/bmj.l6927","volume":"368","author":"S Vollmer","year":"2020","unstructured":"Vollmer S, Mateen BA, Bohner G, Kiraly FJ, Ghani R, Jonsson P, et al. Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness. BMJ. 2020;368:l6927. https:\/\/doi.org\/10.1136\/bmj.l6927.","journal-title":"BMJ"},{"issue":"11","key":"1372_CR105","doi-asserted-by":"publisher","first-page":"8797","DOI":"10.1007\/s00330-021-07782-4","volume":"31","author":"M Huisman","year":"2021","unstructured":"Huisman M, Ranschaert E, Parker W, Mastrodicasa D, Koci M, de Pinto Santos D, et al. An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education. Eur Radiol. 2021;31(11):8797\u2013806. https:\/\/doi.org\/10.1007\/s00330-021-07782-4.","journal-title":"Eur Radiol"},{"issue":"1","key":"1372_CR106","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1080\/13814788.2021.1962845","volume":"27","author":"MJ Kasteleyn","year":"2021","unstructured":"Kasteleyn MJ, Versluis A, van Peet P, Kirk UB, van Dalfsen J, Meijer E, et al. SERIES: eHealth in primary care. Part 5: a critical appraisal of five widely used eHealth applications for primary care - opportunities and challenges. Eur J Gen Pract. 2021;27(1):248\u201356. https:\/\/doi.org\/10.1080\/13814788.2021.1962845.","journal-title":"Eur J Gen Pract"},{"issue":"9","key":"1372_CR107","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2020-047083","volume":"11","author":"A Konig","year":"2021","unstructured":"Konig A, Zeghari R, Guerchouche R, Duc Tran M, Bremond F, Linz N, et al. Remote cognitive assessment of older adults in rural areas by telemedicine and automatic speech and video analysis: protocol for a cross-over feasibility study. BMJ Open. 2021;11(9):e047083. https:\/\/doi.org\/10.1136\/bmjopen-2020-047083.","journal-title":"BMJ Open"},{"issue":"3","key":"1372_CR108","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1177\/15562646211002744","volume":"16","author":"G Samuel","year":"2021","unstructured":"Samuel G, Chubb J, Derrick G. Boundaries between research ethics and ethical research use in artificial intelligence health research. J Empir Res Hum Res Ethics. 2021;16(3):325\u201337. https:\/\/doi.org\/10.1177\/15562646211002744.","journal-title":"J Empir Res Hum Res Ethics"},{"issue":"3","key":"1372_CR109","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1080\/17843286.2019.1708152","volume":"76","author":"W Van Biesen","year":"2021","unstructured":"Van Biesen W, Decruyenaere J, Sideri K, Cockbain J, Sterckx S. Remote digital monitoring of medication intake: methodological, medical, ethical and legal reflections. Acta Clin Belg. 2021;76(3):209\u201316. https:\/\/doi.org\/10.1080\/17843286.2019.1708152.","journal-title":"Acta Clin Belg"},{"key":"1372_CR110","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2021.689736","volume":"3","author":"GN Vilaza","year":"2021","unstructured":"Vilaza GN, McCashin D. Is the automation of digital mental health ethical? Applying an ethical framework to chatbots for cognitive behaviour therapy. Frontiers in Digital Health. 2021;3:689736. https:\/\/doi.org\/10.3389\/fdgth.2021.689736.","journal-title":"Frontiers in Digital Health"},{"key":"1372_CR111","doi-asserted-by":"publisher","DOI":"10.1136\/openhrt-2021-001686","author":"D Willems","year":"2021","unstructured":"Willems D, Bak M, Tan H, Lindinger G, Kocar A, Seperhi Shamloo A, et al. Ethical issues in two parallel trials of personalised criteria for implantation of implantable cardioverter defibrillators for primary prevention: the PROFID project-a position paper. Open Heart. 2021. https:\/\/doi.org\/10.1136\/openhrt-2021-001686.","journal-title":"Open Heart"},{"key":"1372_CR112","doi-asserted-by":"publisher","unstructured":"Zicari RV, Ahmed S, Amann J, Braun SA, Brodersen J, Bruneault F, et al. Co-Design of a trustworthy AI system in healthcare: deep learning based skin lesion classifier. Front Hum Dynamics. 2021;3. https:\/\/doi.org\/10.3389\/fhumd.2021.688152.","DOI":"10.3389\/fhumd.2021.688152"},{"key":"1372_CR113","doi-asserted-by":"publisher","DOI":"10.3389\/fhumd.2021.673104","author":"RV Zicari","year":"2021","unstructured":"Zicari RV, Brusseau J, Blomberg SN, Christensen HC, Coffee M, Ganapini MB, et al. On assessing trustworthy AI in healthcare. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Front Hum Dyn. 2021. https:\/\/doi.org\/10.3389\/fhumd.2021.673104.","journal-title":"Front Hum Dyn"},{"issue":"4","key":"1372_CR114","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1109\/tts.2022.3195114","volume":"3","author":"H Allahabadi","year":"2022","unstructured":"Allahabadi H, Amann J, Balot I, Beretta A, Binkley C, Bozenhard J, et al. Assessing trustworthy AI in times of COVID-19: deep learning for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients. IEEE Trans Technol Soc. 2022;3(4):272\u201389. https:\/\/doi.org\/10.1109\/tts.2022.3195114.","journal-title":"IEEE Trans Technol Soc"},{"key":"1372_CR115","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.929453","volume":"13","author":"M Bak","year":"2022","unstructured":"Bak M, Madai VI, Fritzsche MC, Mayrhofer MT, McLennan S. You can\u2019t have AI both ways: balancing health data privacy and access fairly. Front Genet. 2022;13:929453. https:\/\/doi.org\/10.3389\/fgene.2022.929453.","journal-title":"Front Genet"},{"issue":"3","key":"1372_CR116","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1109\/Taffc.2020.3021015","volume":"13","author":"A Batliner","year":"2022","unstructured":"Batliner A, Hantke S, Schuller B. Ethics and good practice in computational paralinguistics. IEEE Trans Affect Comput. 2022;13(3):1236\u201353. https:\/\/doi.org\/10.1109\/Taffc.2020.3021015.","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"1372_CR117","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1089\/dna.2021.0501","volume":"41","author":"G Brambilla Pisoni","year":"2022","unstructured":"Brambilla Pisoni G, Taddeo M. Apropos data sharing: abandon the distrust and embrace the opportunity. DNA Cell Biol. 2022;41(1):11\u20135. https:\/\/doi.org\/10.1089\/dna.2021.0501.","journal-title":"DNA Cell Biol"},{"issue":"11","key":"1372_CR118","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.1080\/13501763.2022.2126515","volume":"29","author":"T B\u00fcthe","year":"2022","unstructured":"B\u00fcthe T, Djeffal C, L\u00fctge C, Maasen S, Ingersleben-Seip N. Governing AI \u2013 attempting to herd cats? Introduction to the special issue on the governance of artificial intelligence. J Eur Public Policy. 2022;29(11):1721\u201352. https:\/\/doi.org\/10.1080\/13501763.2022.2126515.","journal-title":"J Eur Public Policy"},{"issue":"1","key":"1372_CR119","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1186\/s12913-022-08441-0","volume":"22","author":"A Chatterjee","year":"2022","unstructured":"Chatterjee A, Prinz A, Gerdes M, Martinez S, Pahari N, Meena YK. ProHealth ecoach: user-centered design and development of an ecoach app to promote healthy lifestyle with personalized activity recommendations. BMC Health Serv Res. 2022;22(1):1120. https:\/\/doi.org\/10.1186\/s12913-022-08441-0.","journal-title":"BMC Health Serv Res"},{"issue":"6","key":"1372_CR120","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2022.100489","volume":"3","author":"D De Silva","year":"2022","unstructured":"De Silva D, Alahakoon D. An artificial intelligence life cycle: from conception to production. Patterns. 2022;3(6):100489. https:\/\/doi.org\/10.1016\/j.patter.2022.100489.","journal-title":"Patterns"},{"key":"1372_CR121","doi-asserted-by":"publisher","DOI":"10.1177\/20552076221089099","volume":"8","author":"H Ejaz","year":"2022","unstructured":"Ejaz H, McGrath H, Wong BL, Guise A, Vercauteren T, Shapey J. Artificial intelligence and medical education: a global mixed-methods study of medical students\u2019 perspectives. Digit Health. 2022;8:20552076221089099. https:\/\/doi.org\/10.1177\/20552076221089099.","journal-title":"Digit Health"},{"issue":"1","key":"1372_CR122","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1038\/s41746-022-00573-1","volume":"5","author":"A Essen","year":"2022","unstructured":"Essen A, Stern AD, Haase CB, Car J, Greaves F, Paparova D, et al. Health app policy: international comparison of nine countries\u2019 approaches. NPJ Digit Med. 2022;5(1):31. https:\/\/doi.org\/10.1038\/s41746-022-00573-1.","journal-title":"NPJ Digit Med"},{"key":"1372_CR123","doi-asserted-by":"publisher","DOI":"10.3390\/bs12040103","author":"S Fox","year":"2022","unstructured":"Fox S. Behavioral ethics ecologies of human-artificial intelligence systems. Behav Sci Basel. 2022. https:\/\/doi.org\/10.3390\/bs12040103.","journal-title":"Behav Sci Basel"},{"key":"1372_CR124","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2022.730430","volume":"4","author":"T Garani-Papadatos","year":"2022","unstructured":"Garani-Papadatos T, Natsiavas P, Meyerheim M, Hoffmann S, Karamanidou C, Payne SA. Ethical principles in digital palliative care for children: the MyPal project and experiences made in designing a trustworthy approach. Front Digit Health. 2022;4:730430. https:\/\/doi.org\/10.3389\/fdgth.2022.730430.","journal-title":"Front Digit Health"},{"key":"1372_CR125","doi-asserted-by":"publisher","DOI":"10.1007\/s12152-022-09498-8","author":"M Ienca","year":"2022","unstructured":"Ienca M, Fins JJ, Jox RJ, Jotterand F, Voeneky S, Andorno R, et al. Towards a governance framework for brain data. Neuroethics. 2022. https:\/\/doi.org\/10.1007\/s12152-022-09498-8.","journal-title":"Neuroethics"},{"issue":"2","key":"1372_CR126","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1080\/17483107.2020.1773549","volume":"17","author":"RM Johansson-Pajala","year":"2022","unstructured":"Johansson-Pajala RM, Gustafsson C. Significant challenges when introducing care robots in Swedish elder care. Disabil Rehabil Assist Technol. 2022;17(2):166\u201376. https:\/\/doi.org\/10.1080\/17483107.2020.1773549.","journal-title":"Disabil Rehabil Assist Technol"},{"issue":"3","key":"1372_CR127","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1111\/hsc.13327","volume":"30","author":"N Kodate","year":"2022","unstructured":"Kodate N, Donnelly S, Suwa S, Tsujimura M, Kitinoja H, Hallila J, et al. Home-care robots - attitudes and perceptions among older people, carers and care professionals in Ireland: a questionnaire study. Health & Social Care in the Community. 2022;30(3):1086\u201396. https:\/\/doi.org\/10.1111\/hsc.13327.","journal-title":"Health & Social Care in the Community"},{"issue":"1","key":"1372_CR128","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/s41746-022-00737-z","volume":"5","author":"KM Kostick-Quenet","year":"2022","unstructured":"Kostick-Quenet KM, Gerke S. AI in the hands of imperfect users. NPJ Digit Med. 2022;5(1):197. https:\/\/doi.org\/10.1038\/s41746-022-00737-z.","journal-title":"NPJ Digit Med"},{"issue":"4","key":"1372_CR129","doi-asserted-by":"publisher","first-page":"e054310","DOI":"10.1136\/bmjopen-2021-054310","volume":"12","author":"I Mlakar","year":"2022","unstructured":"Mlakar I, Kampic T, Flis V, Kobilica N, Molan M, Smrke U, et al. Study protocol: a survey exploring patients\u2019 and healthcare professionals\u2019 expectations, attitudes and ethical acceptability regarding the integration of socially assistive humanoid robots in nursing. BMJ Open. 2022;12(4):e054310. https:\/\/doi.org\/10.1136\/bmjopen-2021-054310.","journal-title":"BMJ Open"},{"key":"1372_CR130","doi-asserted-by":"publisher","DOI":"10.1177\/20552076221089079","volume":"8","author":"E Niemiec","year":"2022","unstructured":"Niemiec E. Will the EU medical device regulation help to improve the safety and performance of medical AI devices? Digit Health. 2022;8:20552076221089079. https:\/\/doi.org\/10.1177\/20552076221089079.","journal-title":"Digit Health"},{"issue":"18","key":"1372_CR131","doi-asserted-by":"publisher","first-page":"6418","DOI":"10.26355\/eurrev_202209_29741","volume":"26","author":"P Refolo","year":"2022","unstructured":"Refolo P, Sacchini D, Raimondi C, Spagnolo AG. Ethics of digital therapeutics (DTx). Eur Rev Med Pharmacol Sci. 2022;26(18):6418\u201323. https:\/\/doi.org\/10.26355\/eurrev_202209_29741.","journal-title":"Eur Rev Med Pharmacol Sci"},{"issue":"1","key":"1372_CR132","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1038\/s41746-022-00689-4","volume":"5","author":"LR Soenksen","year":"2022","unstructured":"Soenksen LR, Ma Y, Zeng C, Boussioux L, Villalobos Carballo K, Na L, et al. Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit Med. 2022;5(1):149. https:\/\/doi.org\/10.1038\/s41746-022-00689-4.","journal-title":"NPJ Digit Med"},{"issue":"1","key":"1372_CR133","doi-asserted-by":"publisher","DOI":"10.1186\/s12910-022-00787-8","volume":"23","author":"D Van Cauwenberge","year":"2022","unstructured":"Van Cauwenberge D, Van Biesen W, Decruyenaere J, Leune T, Sterckx S. Many roads lead to Rome and the Artificial Intelligence only shows me one road\": an interview study on physician attitudes regarding the implementation of computerised clinical decision support systems. BMC Med Ethics. 2022;23(1):50. https:\/\/doi.org\/10.1186\/s12910-022-00787-8.","journal-title":"BMC Med Ethics"},{"issue":"9","key":"1372_CR134","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.1111\/jdv.18192","volume":"36","author":"T Willem","year":"2022","unstructured":"Willem T, Krammer S, Bohm AS, French LE, Hartmann D, Lasser T, et al. Risks and benefits of dermatological machine learning health care applications-an overview and ethical analysis. J Eur Acad Dermatol Venereol. 2022;36(9):1660\u20138. https:\/\/doi.org\/10.1111\/jdv.18192.","journal-title":"J Eur Acad Dermatol Venereol"},{"issue":"1","key":"1372_CR135","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0279088","volume":"18","author":"J Amann","year":"2023","unstructured":"Amann J, Vayena E, Ormond KE, Frey D, Madai VI, Blasimme A. Expectations and attitudes towards medical artificial intelligence: a qualitative study in the field of stroke. PLoS One. 2023;18(1):e0279088. https:\/\/doi.org\/10.1371\/journal.pone.0279088.","journal-title":"PLoS One"},{"key":"1372_CR136","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102658","volume":"144","author":"R Baumgartner","year":"2023","unstructured":"Baumgartner R, Arora P, Bath C, Burljaev D, Ciereszko K, Custers B, et al. Fair and equitable AI in biomedical research and healthcare: social science perspectives. Artif Intell Med. 2023;144:102658. https:\/\/doi.org\/10.1016\/j.artmed.2023.102658.","journal-title":"Artif Intell Med"},{"key":"1372_CR137","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2022.104427","volume":"88","author":"MA Berb\u00eds","year":"2023","unstructured":"Berb\u00eds MA, McClintock DS, Bychkov A, der Van Laak J, Pantanowitz L, Lennerz JK, et al. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. EBioMedicine. 2023;88:104427. https:\/\/doi.org\/10.1016\/j.ebiom.2022.104427.","journal-title":"EBioMedicine"},{"issue":"1","key":"1372_CR138","doi-asserted-by":"publisher","DOI":"10.1038\/s44184-023-00033-y","volume":"2","author":"RR Bond","year":"2023","unstructured":"Bond RR, Mulvenna MD, Potts C, O\u2019Neill S, Ennis E, Torous J. Digital transformation of mental health services. Npj Ment Health Res. 2023;2(1):13. https:\/\/doi.org\/10.1038\/s44184-023-00033-y.","journal-title":"Npj Ment Health Res"},{"key":"1372_CR139","doi-asserted-by":"publisher","unstructured":"Botes M. Regulating scientific and technological uncertainty: the precautionary principle in the context of human genomics and AI. South Afr J Sci. 2023;119(5\u20136). https:\/\/doi.org\/10.17159\/sajs.2023\/15037.","DOI":"10.17159\/sajs.2023\/15037"},{"issue":"1","key":"1372_CR140","doi-asserted-by":"publisher","DOI":"10.1186\/s12910-023-00917-w","volume":"24","author":"R Dlugatch","year":"2023","unstructured":"Dlugatch R, Georgieva A, Kerasidou A. Trustworthy artificial intelligence and ethical design: public perceptions of trustworthiness of an AI-based decision-support tool in the context of intrapartum care. BMC Med Ethics. 2023;24(1):42. https:\/\/doi.org\/10.1186\/s12910-023-00917-w.","journal-title":"BMC Med Ethics"},{"issue":"1","key":"1372_CR141","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1038\/s41746-023-00823-w","volume":"6","author":"MN Duffourc","year":"2023","unstructured":"Duffourc MN, Gerke S. The proposed EU directives for AI liability leave worrying gaps likely to impact medical AI. NPJ Digit Med. 2023;6(1):77. https:\/\/doi.org\/10.1038\/s41746-023-00823-w.","journal-title":"NPJ Digit Med"},{"key":"1372_CR142","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2022.104949","volume":"170","author":"R Hussein","year":"2023","unstructured":"Hussein R, Scherdel L, Nicolet F, Martin-Sanchez F. Towards the European Health Data Space (EHDS) ecosystem: a survey research on future health data scenarios. Int J Med Inform. 2023;170:104949. https:\/\/doi.org\/10.1016\/j.ijmedinf.2022.104949.","journal-title":"Int J Med Inform"},{"issue":"4","key":"1372_CR143","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S0963180123000245","volume":"32","author":"S Ligthart","year":"2023","unstructured":"Ligthart S, Ienca M, Meynen G, Molnar-Gabor F, Andorno R, Bublitz C, et al. Minding rights: mapping ethical and legal foundations of \u201cNeurorights.\u201d Camb Q Healthc Ethics. 2023;32(4):1\u201321. https:\/\/doi.org\/10.1017\/S0963180123000245.","journal-title":"Camb Q Healthc Ethics"},{"key":"1372_CR144","doi-asserted-by":"publisher","first-page":"e50216","DOI":"10.2196\/50216","volume":"12","author":"P Nilsen","year":"2023","unstructured":"Nilsen P, Svedberg P, Neher M, Nair M, Larsson I, Petersson L, et al. A framework to guide implementation of AI in health care: protocol for a cocreation research project. JMIR Res Protoc. 2023;12:e50216. https:\/\/doi.org\/10.2196\/50216.","journal-title":"JMIR Res Protoc"},{"issue":"1","key":"1372_CR145","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-023-09493-6","volume":"23","author":"IA Orlova","year":"2023","unstructured":"Orlova IA, Akopyan ZA, Plisyuk AG, Tarasova EV, Borisov EN, Dolgushin GO, et al. Opinion research among Russian physicians on the application of technologies using artificial intelligence in the field of medicine and health care. BMC Health Serv Res. 2023;23(1):749. https:\/\/doi.org\/10.1186\/s12913-023-09493-6.","journal-title":"BMC Health Serv Res"},{"key":"1372_CR146","doi-asserted-by":"publisher","unstructured":"Petersson L, Vincent K, Svedberg P, Nygren JM, Larsson I. Ethical Perspectives on Implementing AI to Predict Mortality Risk in Emergency Department Patients: A Qualitative Study. Stud Health Technol Inform. 2023;302:676-677. https:\/\/doi.org\/10.3233\/shti230234.","DOI":"10.3233\/shti230234"},{"issue":"1","key":"1372_CR147","doi-asserted-by":"publisher","DOI":"10.1007\/s10676-023-09676-z","volume":"25","author":"G Pozzi","year":"2023","unstructured":"Pozzi G. Automated opioid risk scores: a case for machine learning-induced epistemic injustice in healthcare. Ethics Inf Technol. 2023;25(1):3. https:\/\/doi.org\/10.1007\/s10676-023-09676-z.","journal-title":"Ethics Inf Technol"},{"key":"1372_CR148","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11101454","author":"J Rajam\u00e4ki","year":"2023","unstructured":"Rajam\u00e4ki J, Gioulekas F, Rocha PAL, Garcia XDT, Ofem P, Tyni J. ALTAI tool for assessing AI-based technologies: lessons learned and recommendations from SHAPES pilots. Healthcare. 2023. https:\/\/doi.org\/10.3390\/healthcare11101454.","journal-title":"Healthcare"},{"key":"1372_CR149","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare11091286","author":"B Sawik","year":"2023","unstructured":"Sawik B, Tobis S, Baum E, Suwalska A, Kropi\u0144ska S, Stachnik K, et al. Robots for elderly care: review, multi-criteria optimization model and qualitative case study. Healthcare. 2023. https:\/\/doi.org\/10.3390\/healthcare11091286.","journal-title":"Healthcare"},{"key":"1372_CR150","doi-asserted-by":"publisher","DOI":"10.1016\/j.envint.2023.108082","volume":"178","author":"S Schmeisser","year":"2023","unstructured":"Schmeisser S, Miccoli A, von Bergen M, Berggren E, Braeuning A, Busch W, et al. New approach methodologies in human regulatory toxicology - not if, but how and when! Environ Int. 2023;178:108082. https:\/\/doi.org\/10.1016\/j.envint.2023.108082.","journal-title":"Environ Int"},{"key":"1372_CR151","doi-asserted-by":"publisher","DOI":"10.2196\/45815","volume":"25","author":"J Shi","year":"2023","unstructured":"Shi J, Bendig D, Vollmar HC, Rasche P. Mapping the bibliometrics landscape of AI in medicine: methodological study. J Med Internet Res. 2023;25:e45815. https:\/\/doi.org\/10.2196\/45815.","journal-title":"J Med Internet Res"},{"issue":"6","key":"1372_CR152","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.1111\/bjet.13379","volume":"54","author":"JC Sun","year":"2023","unstructured":"Sun JC. Gaps, guesswork, and ghosts lurking in technology integration: laws and policies applicable to student privacy. Br J Edu Technol. 2023;54(6):1604\u201318. https:\/\/doi.org\/10.1111\/bjet.13379.","journal-title":"Br J Edu Technol"},{"issue":"2","key":"1372_CR153","doi-asserted-by":"publisher","DOI":"10.1093\/jlb\/lsad031","volume":"10","author":"H van Kolfschooten","year":"2023","unstructured":"van Kolfschooten H. The AI cycle of health inequity and digital ageism: mitigating biases through the EU regulatory framework on medical devices. J Law Biosci. 2023;10(2):lsad031. https:\/\/doi.org\/10.1093\/jlb\/lsad031.","journal-title":"J Law Biosci"},{"issue":"1","key":"1372_CR154","doi-asserted-by":"publisher","DOI":"10.1186\/s12910-023-01000-0","volume":"25","author":"L Arbelaez Ossa","year":"2024","unstructured":"Arbelaez Ossa L, Lorenzini G, Milford SR, Shaw D, Elger BS, Rost M. Integrating ethics in AI development: a qualitative study. BMC Med Ethics. 2024;25(1):10. https:\/\/doi.org\/10.1186\/s12910-023-01000-0.","journal-title":"BMC Med Ethics"},{"issue":"1","key":"1372_CR155","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1109\/Mis.2023.3343488","volume":"39","author":"R Baeza-Yates","year":"2024","unstructured":"Baeza-Yates R, Fayyad UM. Responsible AI: an urgent mandate. Ieee Intelligent Systems. 2024;39(1):12\u20137. https:\/\/doi.org\/10.1109\/Mis.2023.3343488.","journal-title":"Ieee Intelligent Systems"},{"key":"1372_CR156","doi-asserted-by":"publisher","unstructured":"Bouderhem R. Shaping the future of AI in healthcare through ethics and governance. Humanit Social Sci Commun. 2024;11(1). https:\/\/doi.org\/10.1057\/s41599-024-02894-w.","DOI":"10.1057\/s41599-024-02894-w"},{"issue":"3","key":"1372_CR157","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1007\/s00414-023-03152-5","volume":"138","author":"R Cecchi","year":"2024","unstructured":"Cecchi R, Haja TM, Calabr\u00f2 F, Fasterholdt I, Rasmussen BSB. Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma? Int J Legal Med. 2024;138(3):1173\u20138. https:\/\/doi.org\/10.1007\/s00414-023-03152-5.","journal-title":"Int J Legal Med"},{"key":"1372_CR158","doi-asserted-by":"publisher","unstructured":"Davat A, Martin-Juchat F, M\u00e9nissier T. Co-design with affect stories and applied ethics for health technologies. Front Communication. 2024;9. https:\/\/doi.org\/10.3389\/fcomm.2024.1327711.","DOI":"10.3389\/fcomm.2024.1327711"},{"key":"1372_CR159","doi-asserted-by":"publisher","DOI":"10.3389\/fdgth.2024.1267290","volume":"6","author":"J Fehr","year":"2024","unstructured":"Fehr J, Citro B, Malpani R, Lippert C, Madai VI. A trustworthy AI reality-check: the lack of transparency of artificial intelligence products in healthcare. Front Digit Health. 2024;6:1267290. https:\/\/doi.org\/10.3389\/fdgth.2024.1267290.","journal-title":"Front Digit Health"},{"key":"1372_CR160","doi-asserted-by":"publisher","DOI":"10.1016\/j.archger.2023.105137","volume":"116","author":"H Ide","year":"2024","unstructured":"Ide H, Suwa S, Akuta Y, Kodate N, Tsujimura M, Ishimaru M, et al. Developing a model to explain users\u2019 ethical perceptions regarding the use of care robots in home care: a cross-sectional study in Ireland, Finland, and Japan. Arch Gerontol Geriatr. 2024;116:105137. https:\/\/doi.org\/10.1016\/j.archger.2023.105137.","journal-title":"Arch Gerontol Geriatr"},{"key":"1372_CR161","doi-asserted-by":"publisher","first-page":"105377","DOI":"10.1016\/j.ijmedinf.2024.105377","volume":"184","author":"L Marco-Ruiz","year":"2024","unstructured":"Marco-Ruiz L, Hern\u00e1ndez M\u00c1T, Ngo PD, Makhlysheva A, Svenning TO, Dyb K, et al. A multinational study on artificial intelligence adoption: clinical implementers\u2019 perspectives. Int J Med Inf. 2024;184:105377. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105377.","journal-title":"Int J Med Inf"},{"issue":"1","key":"1372_CR162","doi-asserted-by":"publisher","DOI":"10.1186\/s12910-024-01042-y","volume":"25","author":"MT Maris","year":"2024","unstructured":"Maris MT, Ko\u00e7ar A, Willems DL, Pols J, Tan HL, Lindinger GL, et al. Ethical use of artificial intelligence to prevent sudden cardiac death: an interview study of patient perspectives. BMC Med Ethics. 2024;25(1):42. https:\/\/doi.org\/10.1186\/s12910-024-01042-y.","journal-title":"BMC Med Ethics"},{"key":"1372_CR163","doi-asserted-by":"publisher","DOI":"10.1080\/13642537.2024.2318628","author":"H Molden","year":"2024","unstructured":"Molden H. AI, automation and psychotherapy - a proposed model for losses and gains in the automated therapeutic encounter. Eur J Psychother Counselling. 2024. https:\/\/doi.org\/10.1080\/13642537.2024.2318628.","journal-title":"Eur J Psychother Counselling"},{"key":"1372_CR164","doi-asserted-by":"publisher","unstructured":"Molina \u00d3A, Bernal MJ, Wolf DL, Herreros B. What is Spanish regulation on the application of artificial intelligence to medicine like? Humanit Social Sci Commun. 2024;11(1). https:\/\/doi.org\/10.1057\/s41599-023-02565-2.","DOI":"10.1057\/s41599-023-02565-2"},{"issue":"1","key":"1372_CR165","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1080\/17579961.2024.2313795","volume":"16","author":"G Pavlidis","year":"2024","unstructured":"Pavlidis G. Unlocking the black box: analysing the EU artificial intelligence act\u2019s framework for explainability in AI. Law Innov Technol. 2024;16(1):293\u2013308. https:\/\/doi.org\/10.1080\/17579961.2024.2313795.","journal-title":"Law Innov Technol"},{"key":"1372_CR166","doi-asserted-by":"publisher","DOI":"10.1007\/s12553-024-00878-z","author":"L Pecchia","year":"2024","unstructured":"Pecchia L, Maccaro A, Matarrese MAG, Folkvord F, Fico G. Artificial intelligence, data protection and medical device regulations: squaring the circle with a historical perspective in Europe. Health Technol. 2024. https:\/\/doi.org\/10.1007\/s12553-024-00878-z.","journal-title":"Health Technol"},{"key":"1372_CR167","doi-asserted-by":"publisher","DOI":"10.1136\/jme-2023-109711","author":"ET Ugar","year":"2024","unstructured":"Ugar ET, Malele N. Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders. J Med Ethics. 2024. https:\/\/doi.org\/10.1136\/jme-2023-109711.","journal-title":"J Med Ethics"},{"issue":"2","key":"1372_CR168","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2024-084014","volume":"14","author":"J Viberg Johansson","year":"2024","unstructured":"Viberg Johansson J, Dembrower K, Strand F, Grauman \u00c5. Women\u2019s perceptions and attitudes towards the use of AI in mammography in Sweden: a qualitative interview study. BMJ Open. 2024;14(2):e084014. https:\/\/doi.org\/10.1136\/bmjopen-2024-084014.","journal-title":"BMJ Open"},{"issue":"1","key":"1372_CR169","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.77758","volume":"17","author":"SP Chaparala","year":"2025","unstructured":"Chaparala SP, Pathak KD, Dugyala RR, Thomas J, Varakala SP. Leveraging artificial intelligence to predict and manage complications in patients with multimorbidity: a literature review. Cureus. 2025;17(1):e77758. https:\/\/doi.org\/10.7759\/cureus.77758.","journal-title":"Cureus"},{"key":"1372_CR170","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajo.2025.02.022","author":"Z Khan","year":"2025","unstructured":"Khan Z, Gaidhane AM, Singh M, Ganesan S, Kaur M, Sharma GC, et al. Diagnostic accuracy of IDX-DR for detecting diabetic retinopathy: a systematic review and meta-analysis. Am J Ophthalmol. 2025. https:\/\/doi.org\/10.1016\/j.ajo.2025.02.022.","journal-title":"Am J Ophthalmol"},{"issue":"2","key":"1372_CR171","doi-asserted-by":"publisher","DOI":"10.1002\/alz.14600","volume":"21","author":"L Karlsson","year":"2025","unstructured":"Karlsson L, Vogel J, Arvidsson I, Astrom K, Strandberg O, Seidlitz J, et al. <article-title update=\"added\">Machine learning prediction of tau\u2010PET in Alzheimer\u2019s disease using plasma, MRI, and clinical data. Alzheimers Dement. 2025;21(2):e14600. https:\/\/doi.org\/10.1002\/alz.14600.","journal-title":"Alzheimers Dement"},{"key":"1372_CR172","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehaf004","author":"MS Lee","year":"2025","unstructured":"Lee MS, Shin TG, Lee Y, Kim DH, Choi SH, Cho H, et al. Artificial intelligence applied to electrocardiogram to rule out acute myocardial infarction: the ROMIAE multicentre study. Eur Heart J. 2025. https:\/\/doi.org\/10.1093\/eurheartj\/ehaf004.","journal-title":"Eur Heart J"},{"key":"1372_CR173","doi-asserted-by":"publisher","first-page":"1512910","DOI":"10.3389\/frai.2025.1512910","volume":"8","author":"TFO de Camargo","year":"2025","unstructured":"de Camargo TFO, Ribeiro GAS, da Silva MCB, da Silva LO, Torres P, da Silva D, et al. Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs. Front Artif Intell. 2025;8:1512910. https:\/\/doi.org\/10.3389\/frai.2025.1512910.","journal-title":"Front Artif Intell"},{"key":"1372_CR174","doi-asserted-by":"publisher","DOI":"10.1136\/bmjhci-2024-101248","author":"C Cosma","year":"2025","unstructured":"Cosma C, Radi A, Cattano R, Zanobini P, Bonaccorsi G, Lorini C, et al. Potential role of ChatGPT in simplifying and improving informed consent forms for vaccination: a pilot study conducted in Italy. BMJ Health Care Inform. 2025. https:\/\/doi.org\/10.1136\/bmjhci-2024-101248.","journal-title":"BMJ Health Care Inform"},{"key":"1372_CR175","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaf067","author":"Y Schwammenthal","year":"2024","unstructured":"Schwammenthal Y, Rabinowitz T, Basel-Salmon L, Tomashov-Matar R, Shomron N. Noninvasive fetal genotyping using deep neural networks. Brief Bioinform. 2024. https:\/\/doi.org\/10.1093\/bib\/bbaf067.","journal-title":"Brief Bioinform"},{"issue":"6","key":"1372_CR176","doi-asserted-by":"publisher","first-page":"e41470","DOI":"10.1097\/MD.0000000000041470","volume":"104","author":"J Jeong","year":"2025","unstructured":"Jeong J, Kim S, Pan L, Hwang D, Kim D, Choi J, et al. Reducing the workload of medical diagnosis through artificial intelligence: A narrative review. Med (Baltim). 2025;104(6):e41470. https:\/\/doi.org\/10.1097\/MD.0000000000041470.","journal-title":"Med (Baltim)"},{"key":"1372_CR177","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare13030324","author":"K Perez","year":"2025","unstructured":"Perez K, Wisniewski D, Ari A, Lee K, Lieneck C, Ramamonjiarivelo Z. Investigation into application of AI and telemedicine in rural communities: a systematic literature review. Healthcare. 2025. https:\/\/doi.org\/10.3390\/healthcare13030324.","journal-title":"Healthcare"},{"issue":"1","key":"1372_CR178","doi-asserted-by":"publisher","first-page":"e77561","DOI":"10.7759\/cureus.77561","volume":"17","author":"O Basubrin","year":"2025","unstructured":"Basubrin O. Current status and future of artificial intelligence in medicine. Cureus. 2025;17(1):e77561. https:\/\/doi.org\/10.7759\/cureus.77561.","journal-title":"Cureus"},{"key":"1372_CR179","doi-asserted-by":"publisher","DOI":"10.3389\/fsoc.2025.1520810","volume":"10","author":"I Emah","year":"2025","unstructured":"Emah I, Bennett SJ. Algorithmic emergence? Epistemic in\/justice in AI-directed transformations of healthcare. Front Sociol. 2025;10:1520810. https:\/\/doi.org\/10.3389\/fsoc.2025.1520810.","journal-title":"Front Sociol"},{"key":"1372_CR180","doi-asserted-by":"publisher","DOI":"10.2196\/56306","volume":"27","author":"G Starke","year":"2025","unstructured":"Starke G, Gille F, Termine A, Aquino YSJ, Chavarriaga R, Ferrario A, et al. Finding consensus on trust in AI in health care: recommendations from a panel of international experts. J Med Internet Res. 2025;27:e56306. https:\/\/doi.org\/10.2196\/56306.","journal-title":"J Med Internet Res"},{"issue":"2","key":"1372_CR181","doi-asserted-by":"publisher","DOI":"10.1002\/hsr2.70200","volume":"8","author":"AR Al-Qudimat","year":"2025","unstructured":"Al-Qudimat AR, Fares ZE, Elaarag M, Osman M, Al-Zoubi RM, Aboumarzouk OM. Advancing medical research through artificial intelligence: progressive and transformative strategies: a literature review. Health Sci Rep. 2025;8(2):e70200. https:\/\/doi.org\/10.1002\/hsr2.70200.","journal-title":"Health Sci Rep"},{"key":"1372_CR182","doi-asserted-by":"publisher","DOI":"10.1136\/jme-2024-110464","author":"SR Kraaijeveld","year":"2025","unstructured":"Kraaijeveld SR, van Heijster H, Bol N, Bevelander KE. The ethics of using virtual assistants to help people in vulnerable positions access care. J Med Ethics. 2025. https:\/\/doi.org\/10.1136\/jme-2024-110464.","journal-title":"J Med Ethics"},{"key":"1372_CR183","doi-asserted-by":"publisher","first-page":"28","DOI":"10.3390\/ai4010003","volume":"4","author":"F Li","year":"2023","unstructured":"Li F, Ruijs N, Lu Y. Ethics & AI: a systematic review on ethical concerns and related strategies for designing with AI in healthcare. AI. 2023;4:28\u201353. https:\/\/doi.org\/10.3390\/ai4010003.","journal-title":"AI"},{"key":"1372_CR184","doi-asserted-by":"publisher","DOI":"10.12688\/bioethopenres.17693.3","volume":"2","author":"N Lidstr\u00f6mer","year":"2025","unstructured":"Lidstr\u00f6mer N, Kanters JK, Herlenius E. Systematic review of ethics and legislation of a Global Patient co-Owned Cloud (GPOC). Am J Bioeth Open Res. 2025;2:3. https:\/\/doi.org\/10.12688\/bioethopenres.17693.3.","journal-title":"Am J Bioeth Open Res"},{"issue":"1","key":"1372_CR185","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-46503-5","volume":"15","author":"N Lidstromer","year":"2024","unstructured":"Lidstromer N, Davids J, ElSharkawy M, Ashrafian H, Herlenius E. Systematic review and meta-analysis for a Global Patient co-Owned Cloud (GPOC). Nat Commun. 2024;15(1):2186. https:\/\/doi.org\/10.1038\/s41467-024-46503-5.","journal-title":"Nat Commun"},{"key":"1372_CR186","doi-asserted-by":"publisher","DOI":"10.1186\/s44247-024-00133-5","volume":"2","author":"N Lidstr\u00f6mer","year":"2024","unstructured":"Lidstr\u00f6mer N, Davids J, ElSharkawy M, Ashrafian H, Herlenius E. Necessity for a global patient co-owned cloud (GPOC). BMC Digit Health. 2024;2:76. https:\/\/doi.org\/10.1186\/s44247-024-00133-5.","journal-title":"BMC Digit Health"},{"key":"1372_CR187","doi-asserted-by":"publisher","DOI":"10.1186\/s44247-024-00112-w","volume":"2","author":"N Lidstr\u00f6mer","year":"2024","unstructured":"Lidstr\u00f6mer N, Davids J, ElSharkawy M, Ashrafian H, Herlenius E. A summit on a Global Patient co-Owned Cloud (GPOC). BMC Digit Health. 2024;2:51. https:\/\/doi.org\/10.1186\/s44247-024-00112-w.","journal-title":"BMC Digit Health"},{"key":"1372_CR188","doi-asserted-by":"publisher","DOI":"10.1186\/s44247-024-00128-2","volume":"2","author":"J Davids","year":"2024","unstructured":"Davids J, ElSharkawy M, Ashrafian H, Herlenius E, Lidstr\u00f6mer N. Technical sandbox for a Global Patient co-Owned Cloud (GPOC). BMC Digit Health. 2024;2:75. https:\/\/doi.org\/10.1186\/s44247-024-00128-2.","journal-title":"BMC Digit Health"},{"key":"1372_CR189","doi-asserted-by":"publisher","DOI":"10.1016\/j.socscimed.2020.113172","volume":"260","author":"J Morley","year":"2020","unstructured":"Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: a mapping review. Soc Sci Med. 2020;260:113172. https:\/\/doi.org\/10.1016\/j.socscimed.2020.113172.","journal-title":"Soc Sci Med"},{"issue":"10220","key":"1372_CR190","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1016\/S0140-6736(19)32975-7","volume":"395","author":"J Morley","year":"2020","unstructured":"Morley J, Floridi L. An ethically mindful approach to AI for health care. Lancet. 2020;395(10220):254\u20135. https:\/\/doi.org\/10.1016\/S0140-6736(19)32975-7.","journal-title":"Lancet"},{"key":"1372_CR191","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2021.102190","volume":"121","author":"A Martinho","year":"2021","unstructured":"Martinho A, Kroesen M, Chorus C. A healthy debate: exploring the views of medical doctors on the ethics of artificial intelligence. Artif Intell Med. 2021;121:102190. https:\/\/doi.org\/10.1016\/j.artmed.2021.102190.","journal-title":"Artif Intell Med"},{"key":"1372_CR192","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1518049","volume":"8","author":"JA Tangsrivimol","year":"2025","unstructured":"Tangsrivimol JA, Darzidehkalani E, Virk HUH, Wang Z, Egger J, Wang M, et al. Benefits, limits, and risks of ChatGPT in medicine. Front Artif Intell. 2025;8:1518049. https:\/\/doi.org\/10.3389\/frai.2025.1518049.","journal-title":"Front Artif Intell"},{"issue":"1","key":"1372_CR193","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.76825","volume":"17","author":"DO Traylor","year":"2025","unstructured":"Traylor DO, Kern KV, Anderson EE, Henderson R. Beyond the screen: the impact of generative artificial intelligence (AI) on patient learning and the patient-physician relationship. Cureus. 2025;17(1):e76825. https:\/\/doi.org\/10.7759\/cureus.76825.","journal-title":"Cureus"},{"key":"1372_CR194","unstructured":"Singh S, Errampalli E, Errampalli N, Miran MS. Enhancing Patient Education on Cardiovascular Rehabilitation with Large Language Models. Mo Med. 2025;122(1):67-71. PMID: 39958590; PMCID: PMC11827661. link: https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/pmid\/39958590\/"},{"issue":"1","key":"1372_CR195","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.jhsg.2024.10.002","volume":"7","author":"G Abdelmalek","year":"2025","unstructured":"Abdelmalek G, Uppal H, Garcia D, Farshchian J, Emami A, McGinniss A. Leveraging ChatGPT to produce patient education materials for common hand conditions. J Hand Surg Glob Online. 2025;7(1):37\u201340. https:\/\/doi.org\/10.1016\/j.jhsg.2024.10.002.","journal-title":"J Hand Surg Glob Online"},{"issue":"1","key":"1372_CR196","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-92575-8","volume":"15","author":"CRB Durai","year":"2025","unstructured":"Durai CRB, Dhanasekaran S, Rani MJ, Sekharan SC. Integrating advanced neural network architectures with privacy enhanced encryption for secure and intelligent healthcare analytics. Sci Rep. 2025;15(1):30367. https:\/\/doi.org\/10.1038\/s41598-025-92575-8.","journal-title":"Sci Rep"},{"key":"1372_CR197","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108036","volume":"170","author":"S Messinis","year":"2024","unstructured":"Messinis S, Temenos N, Protonotarios NE, Rallis I, Kalogeras D, Doulamis N. Enhancing internet of medical things security with artificial intelligence: a comprehensive review. Comput Biol Med. 2024;170:108036. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108036.","journal-title":"Comput Biol Med"},{"key":"1372_CR198","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3233\/SHTI250047","volume":"323","author":"K Kalodanis","year":"2025","unstructured":"Kalodanis K, Feretzakis G, Rizomiliotis P, Verykios VS, Papapavlou C, Skrekas A, et al. Assessing the readiness of European healthcare institutions for EU AI act compliance. Stud Health Technol Inf. 2025;323:50\u20134. https:\/\/doi.org\/10.3233\/SHTI250047.","journal-title":"Stud Health Technol Inf"},{"key":"1372_CR199","doi-asserted-by":"publisher","DOI":"10.1016\/j.healthpol.2025.105428","volume":"161","author":"P de la Cervera Cruz","year":"2025","unstructured":"de la Cervera Cruz P, Lalova-Spinks T, Shabani M. Implementation of the European health data space: a qualitative study on expectations of health data experts from 23 countries. Health Policy. 2025;161:105428. https:\/\/doi.org\/10.1016\/j.healthpol.2025.105428.","journal-title":"Health Policy"}],"container-title":["BMC Medical Ethics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12910-025-01372-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12910-025-01372-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12910-025-01372-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T12:21:13Z","timestamp":1774873273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12910-025-01372-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,10]]},"references-count":199,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1372"],"URL":"https:\/\/doi.org\/10.1186\/s12910-025-01372-5","relation":{},"ISSN":["1472-6939"],"issn-type":[{"value":"1472-6939","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,10]]},"assertion":[{"value":"10 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was approved by the Ethics Committee of the Faculty of Medicine of the University of Porto, Portugal (Reference Number: 118\/CEFMUP\/2023) on February 29th, 2024.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"35"}}