{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T20:18:33Z","timestamp":1775506713258,"version":"3.50.1"},"reference-count":65,"publisher":"IMR Press","issue":"11","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Front. Biosci. (Landmark Ed)"],"abstract":"<jats:p>Artificial intelligence (AI) is an iterative process by which information is captured, transformed into knowledge and processed to produce adaptive changes in the environment. AI is a broad concept, involving virtual (computing) and physical (robotics) elements. In this narrative literature review, we focus on the aspects of AI that present major opportunities for developing health care. Within a few years, AI will be part of our daily clinical practice. Although significant advances are being made, the application of AI in musculoskeletal medicine is still in its early stages compared with its implementation in other areas of medicine. AI is increasingly being employed in fields such as musculoskeletal radiology, skeletal trauma, orthopedic surgery, physical and rehabilitation medicine and sports medicine, as well as for \u201cbig data\u201d and AI in gastrointestinal (GI) endoscopy related injuries. Among the limitations of IA are that it analyzes information based on the data it is supplied, which must therefore be well-labeled and that some algorithms such as DL uses more time, data, and computational power than other techniques. Moreover, AI currently does not solve the problem of causality that exists in medicine with observational data; information that physicians interpret within a broad clinical context. AI should therefore be integrated in a prudent and reasonable manner into the workflows of health professionals.<\/jats:p>","DOI":"10.52586\/5027","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:14:42Z","timestamp":1638321282000},"source":"Crossref","is-referenced-by-count":19,"title":["Artificial intelligence in musculoskeletal conditions"],"prefix":"10.31083","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2693-1439","authenticated-orcid":true,"given":"Juan M.","family":"Rom\u00e1n-Belmonte","sequence":"first","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Cruz Roja San Jos\u00e9 y Santa Adela University Hospital, 28003 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9179-201X","authenticated-orcid":true,"given":"Hortensia De la","family":"Corte-Rodr\u00edguez","sequence":"first","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, La Paz University Hospital, 28046 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6360-0113","authenticated-orcid":true,"given":"Em\u00e9rito Carlos","family":"Rodr\u00edguez-Merch\u00e1n","sequence":"first","affiliation":[{"name":"Department of Orthopedic Surgery, La Paz University Hospital, 28046 Madrid, Spain"},{"name":"Osteoarticular Surgery Research, Hospital La Paz Institute for Health Research \u2013 IdiPAZ (La Paz University Hospital \u2013 Autonomous University of Madrid), 28046 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16242","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"Goldenfeld N, Kadanoff LP. Simple Lessons from Complexity. Science. 1996; 284: 87\u201389.","DOI":"10.1126\/science.284.5411.87"},{"key":"ref2","doi-asserted-by":"crossref","unstructured":"Anderson D. Artificial Intelligence and Applications in PM&R. American Journal of Physical Medicine & Rehabilitation. 2019; 98: e128\u2013e129.","DOI":"10.1097\/PHM.0000000000001171"},{"key":"ref3","doi-asserted-by":"crossref","unstructured":"Laur O, Wang B. Musculoskeletal trauma and artificial intelligence: current trends and projections. Skeletal Radiology. 2021. (in press)","DOI":"10.1007\/s00256-021-03824-6"},{"key":"ref4","doi-asserted-by":"crossref","unstructured":"Cabitza F, Locoro A, Banfi G. Machine Learning in Orthopedics: a Literature Review. Frontiers in Bioengineering and Biotechnology. 2018; 6: 75.","DOI":"10.3389\/fbioe.2018.00075"},{"key":"ref5","doi-asserted-by":"crossref","unstructured":"McCorduck P. Time Line: The Mechanization of Thinking. En: Machines who think (pp. 23\u201330). 2nd edn. A. K. Peters: Natick, MA. 2004.","DOI":"10.1201\/9780429258985"},{"key":"ref6","doi-asserted-by":"crossref","unstructured":"Liew C. The future of radiology augmented with Artificial Intelligence: a strategy for success. European Journal of Radiology. 2018; 102: 152\u2013156.","DOI":"10.1016\/j.ejrad.2018.03.019"},{"key":"ref7","doi-asserted-by":"crossref","unstructured":"Deo RC. Machine Learning in Medicine. Circulation. 2015; 132: 1920\u20131930.","DOI":"10.1161\/CIRCULATIONAHA.115.001593"},{"key":"ref8","doi-asserted-by":"crossref","unstructured":"Suzuki K. Overview of deep learning in medical imaging. Radiological Physics and Technology. 2017; 10: 257\u2013273.","DOI":"10.1007\/s12194-017-0406-5"},{"key":"ref9","doi-asserted-by":"crossref","unstructured":"Soffer S, Ben-Cohen A, Shimon O, Amitai MM, Greenspan H, Klang E. Convolutional Neural Networks for Radiologic Images: a Radiologist\u2019s Guide. Radiology. 2019; 290: 590\u2013606.","DOI":"10.1148\/radiol.2018180547"},{"key":"ref10","doi-asserted-by":"crossref","unstructured":"Do S, Song KD, Chung JW. Basics of Deep Learning: a Radiologist\u2019s Guide to Understanding Published Radiology Articles on Deep Learning. Korean Journal of Radiology. 2020; 21: 33.","DOI":"10.3348\/kjr.2019.0312"},{"key":"ref11","doi-asserted-by":"crossref","unstructured":"Yasaka K, Abe O. Deep learning and artificial intelligence in radiology: Current applications and future directions. PLOS Medicine. 2018; 15: e1002707.","DOI":"10.1371\/journal.pmed.1002707"},{"key":"ref12","unstructured":"Brownlee J. Supervised and Unsupervised Machine Learning Algorithms. 2016. Available at: https:\/\/machinelearningmastery.com\/supervised-and-unsupervised-machine-learning-algorithms\/ (Accessed: 22 July 2021)."},{"key":"ref13","unstructured":"Rajpurkar P, Irvin J, Bagul A, Ding D, Duan T, Mehta H, et al. MURA: Large dataset for abnormality detection in musculoskeletal radiographs. arXiv. 2017. Available at: https:\/\/openreview.net\/pdf?id=r1Q98pjiG (Accessed: 22 July 2021)."},{"key":"ref14","doi-asserted-by":"crossref","unstructured":"Munk PL, Murphy KJ. The Paradigm Shift. Canadian Association of Radiologists Journal. 2017; 68: 97.","DOI":"10.1016\/j.carj.2017.03.001"},{"key":"ref15","doi-asserted-by":"crossref","unstructured":"Gore JC. Artificial intelligence in medical imaging. Magnetic Resonance Imaging. 2020; 68: A1\u2013A4.","DOI":"10.1016\/j.mri.2019.12.006"},{"key":"ref16","doi-asserted-by":"crossref","unstructured":"Shen Y, Chen L, Yue W, Xu H. Artificial intelligence in ultrasound. European Journal of Radiology. 2021; 139: 109717.","DOI":"10.1016\/j.ejrad.2021.109717"},{"key":"ref17","doi-asserted-by":"crossref","unstructured":"Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R. SLIDE: automatic spine level identification system using a deep convolutional neural network. International Journal of Computer Assisted Radiology and Surgery. 2017; 12: 1189\u20131198.","DOI":"10.1007\/s11548-017-1575-8"},{"key":"ref18","doi-asserted-by":"crossref","unstructured":"Burlina P, Billings S, Joshi N, Albayda J. Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods. PLoS ONE. 2017; 12: e0184059.","DOI":"10.1371\/journal.pone.0184059"},{"key":"ref19","doi-asserted-by":"crossref","unstructured":"Chen X, Xie C, Chen Z, Li Q. Automatic Tracking of Muscle Cross-Sectional Area Using Convolutional Neural Networks with Ultrasound. Journal of Ultrasound in Medicine. 2019; 38: 2901\u20132908.","DOI":"10.1002\/jum.14995"},{"key":"ref20","doi-asserted-by":"crossref","unstructured":"Krogue JD, Cheng KV, Hwang KM, Toogood P, Meinberg EG, Geiger EJ, et al. Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning. Radiology: Artificial Intelligence. 2020; 2: e190023.","DOI":"10.1148\/ryai.2020190023"},{"key":"ref21","doi-asserted-by":"crossref","unstructured":"Liu F, Guan B, Zhou Z, Samsonov A, Rosas H, Lian K, et al. Fully Automated Diagnosis of Anterior Cruciate Ligament Tears on Knee MR Images by Using Deep Learning. Radiology: Artificial Intelligence. 2019; 1: 180091.","DOI":"10.1148\/ryai.2019180091"},{"key":"ref22","doi-asserted-by":"crossref","unstructured":"Roblot V, Giret Y, Bou Antoun M, Morillot C, Chassin X, Cotten A, et al. Artificial intelligence to diagnose meniscus tears on MRI. Diagnostic and Interventional Imaging. 2019; 100: 243\u2013249.","DOI":"10.1016\/j.diii.2019.02.007"},{"key":"ref23","doi-asserted-by":"crossref","unstructured":"Liu F, Zhou Z, Samsonov A, Blankenbaker D, Larison W, Kanarek A, et al. Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection. Radiology. 2018; 289: 160\u2013169.","DOI":"10.1148\/radiol.2018172986"},{"key":"ref24","doi-asserted-by":"crossref","unstructured":"O\u2019Neill TJ, Xi Y, Stehel E, Browning T, Ng YS, Baker C, et al. Active Reprioritization of the Reading Worklist Using Artificial Intelligence has a Beneficial Effect on the Turnaround Time for Interpretation of Head CT with Intracranial Hemorrhage. Radiology: Artificial Intelligence. 2020; 3: e200024.","DOI":"10.1148\/ryai.2020200024"},{"key":"ref25","doi-asserted-by":"crossref","unstructured":"Recht MP, Zbontar J, Sodickson DK, Knoll F, Yakubova N, Sriram A, et al. Using Deep Learning to Accelerate Knee MRI at 3 T: Results of an Interchangeability Study. American Journal of Roentgenology. 2020; 215: 1421\u20131429.","DOI":"10.2214\/AJR.20.23313"},{"key":"ref26","doi-asserted-by":"crossref","unstructured":"Dreizin D, Zhou Y, Chen T, Li G, Yuille AL, McLenithan A, et al. Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support. Journal of Trauma and Acute Care Surgery. 2020; 88: 425\u2013433.","DOI":"10.1097\/TA.0000000000002566"},{"key":"ref27","doi-asserted-by":"crossref","unstructured":"Stinner DJ, Edwards D. Surgical Management of Musculoskeletal Trauma. The Surgical Clinics of North America. 2017; 97: 1119\u20131131.","DOI":"10.1016\/j.suc.2017.06.005"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Bruno MA, Walker EA, Abujudeh HH. Understanding and Confronting our Mistakes: the Epidemiology of Error in Radiology and Strategies for Error Reduction. Radiographics. 2015; 35: 1668\u20131676.","DOI":"10.1148\/rg.2015150023"},{"key":"ref29","doi-asserted-by":"crossref","unstructured":"Hallas P, Ellingsen T. Errors in fracture diagnoses in the emergency department\u2013characteristics of patients and diurnal variation. BMC Emergency Medicine. 2006; 6: 4.","DOI":"10.1186\/1471-227X-6-4"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Kalmet PHS, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T, et al. Deep learning in fracture detection: a narrative review. Acta Orthopaedica. 2020; 91: 215\u2013220.","DOI":"10.1080\/17453674.2019.1711323"},{"key":"ref31","doi-asserted-by":"crossref","unstructured":"Jones RM, Sharma A, Hotchkiss R, Sperling JW, Hamburger J, Ledig C, et al. Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs. Npj Digital Medicine. 2020; 3: 144.","DOI":"10.1038\/s41746-020-00352-w"},{"key":"ref32","doi-asserted-by":"crossref","unstructured":"Langerhuizen DWG, Janssen SJ, Mallee WH, van den Bekerom MPJ, Ring D, Kerkhoffs GMMJ, et al. What are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. Clinical Orthopaedics & Related Research. 2019; 477: 2482\u20132491.","DOI":"10.1097\/CORR.0000000000000848"},{"key":"ref33","doi-asserted-by":"crossref","unstructured":"Tanzi L, Vezzetti E, Moreno R, Aprato A, Audisio A, Mass\u00e8 A. Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach. European Journal of Radiology. 2020; 133: 109373.","DOI":"10.1016\/j.ejrad.2020.109373"},{"key":"ref34","doi-asserted-by":"crossref","unstructured":"Chung SW, Han SS, Lee JW, Oh K, Kim NR, Yoon JP, et al. Automated detection and classification of the proximal humerus fracture by using deep learning algorithm. Acta Orthopaedica. 2018; 89: 468\u2013473.","DOI":"10.1080\/17453674.2018.1453714"},{"key":"ref35","doi-asserted-by":"crossref","unstructured":"Lind A, Akbarian E, Olsson S, N\u00e5sell H, Sk\u00f6ldenberg O, Razavian AS, et al. Artificial intelligence for the classification of fractures around the knee in adults according to the 2018 AO\/OTA classification system. PLoS ONE. 2021; 16: e0248809.","DOI":"10.1371\/journal.pone.0248809"},{"key":"ref36","doi-asserted-by":"crossref","unstructured":"Olczak J, Emilson F, Razavian A, Antonsson T, Stark A, Gordon M. Ankle fracture classification using deep learning: automating detailed AO Foundation\/Orthopedic Trauma Association (AO\/OTA) 2018 malleolar fracture identification reaches a high degree of correct classification. Acta Orthopaedica. 2021; 92: 102\u2013108.","DOI":"10.1080\/17453674.2020.1837420"},{"key":"ref37","doi-asserted-by":"crossref","unstructured":"Murata K, Endo K, Aihara T, Suzuki H, Sawaji Y, Matsuoka Y, et al. Artificial intelligence for the detection of vertebral fractures on plain spinal radiography. Scientific Reports. 2020; 10: 20031.","DOI":"10.1038\/s41598-020-76866-w"},{"key":"ref38","doi-asserted-by":"crossref","unstructured":"Yabu A, Hoshino M, Tabuchi H, Takahashi S, Masumoto H, Akada M, et al. Using artificial intelligence to diagnose fresh osteoporotic vertebral fractures on magnetic resonance images. The Spine Journal. 2021; 21: 1652\u20131658.","DOI":"10.1016\/j.spinee.2021.03.006"},{"key":"ref39","doi-asserted-by":"crossref","unstructured":"Langerhuizen DWG, Bulstra AEJ, Janssen SJ, Ring D, Kerkhoffs GMMJ, Jaarsma RL, et al. Is Deep Learning on Par with Human Observers for Detection of Radiographically Visible and Occult Fractures of the Scaphoid? Clinical Orthopaedics & Related Research. 2020; 478: 2653\u20132659.","DOI":"10.1097\/CORR.0000000000001318"},{"key":"ref40","doi-asserted-by":"crossref","unstructured":"Zhou Q, Tang W, Wang J, Hu Z, Xia Z, Zhang R, et al. Automatic detection and classification of rib fractures based on patients\u2019 CT images and clinical information via convolutional neural network. European Radiology. 2021; 31: 3815\u20133825.","DOI":"10.1007\/s00330-020-07418-z"},{"key":"ref41","doi-asserted-by":"crossref","unstructured":"Lyman S, Hidaka C. Patient-Reported Outcome Measures\u2014what Data do we Really need? The Journal of Arthroplasty. 2016; 31: 1144\u20131147.","DOI":"10.1016\/j.arth.2016.01.073"},{"key":"ref42","doi-asserted-by":"crossref","unstructured":"Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Artificial Intelligence and Surgical Decision-making. JAMA Surgery. 2020; 155: 148.","DOI":"10.1001\/jamasurg.2019.4917"},{"key":"ref43","doi-asserted-by":"crossref","unstructured":"Karnuta JM, Churchill JL, Haeberle HS, Nwachukwu BU, Taylor SA, Ricchetti ET, et al. The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty. Journal of Shoulder and Elbow Surgery. 2020; 29: 2385\u20132394.","DOI":"10.1016\/j.jse.2020.04.009"},{"key":"ref44","doi-asserted-by":"crossref","unstructured":"Navarro SM, Wang EY, Haeberle HS, Mont MA, Krebs VE, Patterson BM, et al. Machine Learning and Primary Total Knee Arthroplasty: Patient Forecasting for a Patient-Specific Payment Model. The Journal of Arthroplasty. 2018; 33: 3617\u20133623.","DOI":"10.1016\/j.arth.2018.08.028"},{"key":"ref45","doi-asserted-by":"crossref","unstructured":"Humbyrd CJ. The Ethics of Bundled Payments in Total Joint Replacement: \u201cCherry Picking\u201d and \u201cLemon Dropping\u201d. Journal of Clinical Ethics. 2018; 28: 62\u201368.","DOI":"10.1086\/JCE2018291062"},{"key":"ref46","doi-asserted-by":"crossref","unstructured":"van Dokkum LEH, Ward T, Laffont I. Brain computer interfaces for neurorehabilitation \u2013 its current status as a rehabilitation strategy post-stroke. Annals of Physical and Rehabilitation Medicine. 2015; 58: 3\u20138.","DOI":"10.1016\/j.rehab.2014.09.016"},{"key":"ref47","doi-asserted-by":"crossref","unstructured":"Ithapu VK, Singh V, Okonkwo OC, Chappell RJ, Dowling NM, Johnson SC. Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment. Alzheimer\u2019s & Dementia. 2015; 11: 1489\u20131499.","DOI":"10.1016\/j.jalz.2015.01.010"},{"key":"ref48","doi-asserted-by":"crossref","unstructured":"Vourganas I, Stankovic V, Stankovic L. Individualised Responsible Artificial Intelligence for Home-Based Rehabilitation. Sensors. 2020; 21: 2.","DOI":"10.3390\/s21010002"},{"key":"ref49","doi-asserted-by":"crossref","unstructured":"Vourganas I, Stankovic V, Stankovic L, Kerr A. Factors that contribute to the use of stroke self-rehabilitation technologies: A review. JMIR Biomedical Engineering. 2019; 4: e13732.","DOI":"10.2196\/13732"},{"key":"ref50","doi-asserted-by":"crossref","unstructured":"Simonov M, Delconte G. Humanoid assessing rehabilitative exercises. Methods of Information in Medicine. 2015; 54: 114\u2013121.","DOI":"10.3414\/ME13-02-0054"},{"key":"ref51","doi-asserted-by":"crossref","unstructured":"L\u00f6tsch J, Ultsch A. Machine learning in pain research. Pain. 2018; 159: 623\u2013630.","DOI":"10.1097\/j.pain.0000000000001118"},{"key":"ref52","doi-asserted-by":"crossref","unstructured":"Tack C. Artificial intelligence and machine learning \u2014 applications in musculoskeletal physiotherapy. Musculoskeletal Science & Practice. 2019; 39: 164\u2013169.","DOI":"10.1016\/j.msksp.2018.11.012"},{"key":"ref53","doi-asserted-by":"crossref","unstructured":"Oude Nijeweme-d\u2019Hollosy W, van Velsen L, Poel M, Groothuis-Oudshoorn CGM, Soer R, Hermens H. Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. International Journal of Medical Informatics. 2018; 110: 31\u201341.","DOI":"10.1016\/j.ijmedinf.2017.11.010"},{"key":"ref54","doi-asserted-by":"crossref","unstructured":"Weston AD, Korfiatis P, Kline TL, Philbrick KA, Kostandy P, Sakinis T, et al. Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning. Radiology. 2019; 290: 669\u2013679.","DOI":"10.1148\/radiol.2018181432"},{"key":"ref55","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Valenciano A, Ayala F, Puerta JM, DE Ste Croix MBA, Vera-Garcia FJ, Hern\u00e1ndez-S\u00e1nchez S, et al. A Preventive Model for Muscle Injuries: a Novel Approach based on Learning Algorithms. Medicine and Science in Sports and Exercise. 2018; 50: 915\u2013927.","DOI":"10.1249\/MSS.0000000000001535"},{"key":"ref56","doi-asserted-by":"crossref","unstructured":"Kakavas G, Malliaropoulos N, Pruna R, Maffulli N. Artificial intelligence: a tool for sports trauma prediction. Injury. 2020; 51: S63\u2013S65.","DOI":"10.1016\/j.injury.2019.08.033"},{"key":"ref57","doi-asserted-by":"crossref","unstructured":"Ehrmann FE, Duncan CS, Sindhusake D, Franzsen WN, Greene DA. GPS and Injury Prevention in Professional Soccer. Journal of Strength and Conditioning Research. 2015; 30: 360\u2013367.","DOI":"10.1519\/JSC.0000000000001093"},{"key":"ref58","doi-asserted-by":"crossref","unstructured":"Bates DW, Saria S, Ohno-Machado L, Shah A, Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs. 2014; 33: 1123\u20131131.","DOI":"10.1377\/hlthaff.2014.0041"},{"key":"ref59","doi-asserted-by":"crossref","unstructured":"Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annual Review of Pharmacology and Toxicology. 2020; 60: 573\u2013589.","DOI":"10.1146\/annurev-pharmtox-010919-023324"},{"key":"ref60","doi-asserted-by":"crossref","unstructured":"Richter AN, Khoshgoftaar TM. A review of statistical and machine learning methods for modeling cancer risk using structured clinical data. Artificial Intelligence in Medicine. 2018; 90: 1\u201314.","DOI":"10.1016\/j.artmed.2018.06.002"},{"key":"ref61","doi-asserted-by":"crossref","unstructured":"Yung DE, Banfi T, Ciuti G, Arezzo A, Dario P, Koulaouzidis A. Musculoskeletal injuries in gastrointestinal endoscopists: a systematic review. Expert Review of Gastroenterology & Hepatology. 2017; 11: 939\u2013947.","DOI":"10.1080\/17474124.2017.1356225"},{"key":"ref62","doi-asserted-by":"crossref","unstructured":"Ting DSW, Pasquale LR, Peng L, Campbell JP, Lee AY, Raman R, et al. Artificial intelligence and deep learning in ophthalmology. British Journal of Ophthalmology. 2019; 103: 167\u2013175.","DOI":"10.1136\/bjophthalmol-2018-313173"},{"key":"ref63","doi-asserted-by":"crossref","unstructured":"El-Hassoun O, Maruscakova L, Valaskova Z, Bucova M, Polak S, Hulin I. Artificial intelligence in service of medicine. Bratislavske Lekarske Listy. 2019; 120: 218\u2013222.","DOI":"10.4149\/BLL_2019_028"},{"key":"ref64","unstructured":"Popkin G. Is It Foolish to Model Nature\u2019s Complexity With Equations? WIRED. 2015. Available at: https:\/\/www.wired.com\/2015\/10\/is-it-foolish-to-model-natures-complexity-with-equations\/ (Accessed: 22 July 2021)."},{"key":"ref65","doi-asserted-by":"crossref","unstructured":"Fiske A, Henningsen P, Buyx A. Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy. Journal of Medical Internet Research. 2019; 21: e13216.","DOI":"10.2196\/13216"}],"container-title":["Frontiers in Bioscience-Landmark"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.imrpress.com\/journal\/FBL\/26\/11\/10.52586\/5027","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T11:41:54Z","timestamp":1732707714000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.imrpress.com\/journal\/FBL\/26\/11\/10.52586\/5027"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":65,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021]]}},"alternative-id":["s2768-6701(21)00078-2"],"URL":"https:\/\/doi.org\/10.52586\/5027","relation":{},"ISSN":["2768-6701","2768-6698"],"issn-type":[{"value":"2768-6701","type":"print"},{"value":"2768-6698","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,30]]},"article-number":"1340"}}