{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T03:08:17Z","timestamp":1775790497399,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T00:00:00Z","timestamp":1749945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Artif Intell"],"DOI":"10.1007\/s44163-025-00370-1","type":"journal-article","created":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T09:13:05Z","timestamp":1749978785000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["The potential of machine learning in diagnosing neurological and psychiatric diseases: a review"],"prefix":"10.1007","volume":"5","author":[{"given":"Claudia","family":"Ricetti","sequence":"first","affiliation":[]},{"given":"Luca","family":"Carrara","sequence":"additional","affiliation":[]},{"given":"Davide","family":"La Torre","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,15]]},"reference":[{"issue":"7","key":"370_CR1","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.amjmed.2019.01.017","volume":"132","author":"N Noorbakhsh-Sabet","year":"2019","unstructured":"Noorbakhsh-Sabet N, Zand R, Zhang Y, Abedi V. Artificial intelligence transforms the future of health care. Am J Med. 2019;132(7):795\u2013801. https:\/\/doi.org\/10.1016\/j.amjmed.2019.01.017.","journal-title":"Am J Med"},{"issue":"1","key":"370_CR2","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1093\/bmb\/ldab016","volume":"139","author":"YYM Aung","year":"2021","unstructured":"Aung YYM, Wong DCS, Ting DSW. The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. Br Med Bull. 2021;139(1):4\u201315. https:\/\/doi.org\/10.1093\/bmb\/ldab016.","journal-title":"Br Med Bull"},{"key":"370_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103311","volume":"100","author":"N Mehta","year":"2019","unstructured":"Mehta N, Pandit A, Shukla S. Transforming healthcare with big data analytics and artificial intelligence: a systematic mapping study. J Biomed Inf. 2019;100: 103311. https:\/\/doi.org\/10.1016\/j.jbi.2019.103311.","journal-title":"J Biomed Inf"},{"issue":"1","key":"370_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1002\/hast.973","volume":"49","author":"AJ London","year":"2019","unstructured":"London AJ. Artificial intelligence and black-box medical decisions: accuracy versus explainability. Hastings Cent Rep. 2019;49(1):15\u201321. https:\/\/doi.org\/10.1002\/hast.973.","journal-title":"Hastings Cent Rep"},{"issue":"50","key":"370_CR5","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000036671","volume":"102","author":"C Elendu","year":"2023","unstructured":"Elendu C, Amaechi DC, Elendu TC, Jingwa KA, Okoye OK, John Okah M, Ladele JA, Farah AH, Alimi HA. Ethical implications of AI and robotics in healthcare: a review. Medicine. 2023;102(50): e36671. https:\/\/doi.org\/10.1097\/MD.0000000000036671.","journal-title":"Medicine"},{"key":"370_CR6","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1007\/s44163-024-00114-7","volume":"4","author":"M Frasca","year":"2024","unstructured":"Frasca M, La Torre D, Pravettoni G, et al. Explainable and interpretable artificial intelligence in medicine: a systematic bibliometric review. Discov Artif Intell. 2024;4:15. https:\/\/doi.org\/10.1007\/s44163-024-00114-7.","journal-title":"Discov Artif Intell"},{"issue":"12","key":"370_CR7","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1515\/cclm-2022-0291","volume":"60","author":"Q Pei","year":"2022","unstructured":"Pei Q, Luo Y, Chen Y, Li J, Xie D, Ye T. Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis. Clin Chem Lab Med (CCLM). 2022;60(12):1974\u201383. https:\/\/doi.org\/10.1515\/cclm-2022-0291.","journal-title":"Clin Chem Lab Med (CCLM)"},{"issue":"1\u20133","key":"370_CR8","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1159\/000504292","volume":"82","author":"U Raghavendra","year":"2020","unstructured":"Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol. 2020;82(1\u20133):41\u201364. https:\/\/doi.org\/10.1159\/000504292.","journal-title":"Eur Neurol"},{"key":"370_CR9","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, et al. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500\u201310. https:\/\/doi.org\/10.1038\/s41568-018-0016-5.","journal-title":"Nat Rev Cancer"},{"key":"370_CR10","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1146\/annurev-clinpsy-032816-045037","volume":"14","author":"DB Dwyer","year":"2018","unstructured":"Dwyer DB, Falkai P, Koutsouleris N. Machine learning approaches for clinical psychology and psychiatry. Annu Rev Clin Psychol. 2018;14:91\u2013118. https:\/\/doi.org\/10.1146\/annurev-clinpsy-032816-045037.","journal-title":"Annu Rev Clin Psychol"},{"key":"370_CR11","doi-asserted-by":"publisher","first-page":"1239","DOI":"10.1007\/s11517-016-1585-7","volume":"55","author":"X Duan","year":"2017","unstructured":"Duan X, Yang Y, Tan S, et al. Application of artificial neural network model combined with four biomarkers in auxiliary diagnosis of lung cancer. Med Biol Eng Comput. 2017;55:1239\u201348. https:\/\/doi.org\/10.1007\/s11517-016-1585-7.","journal-title":"Med Biol Eng Comput"},{"issue":"6","key":"370_CR12","doi-asserted-by":"publisher","first-page":"1999","DOI":"10.3390\/app10061999","volume":"10","author":"MM Bad\u017ea","year":"2020","unstructured":"Bad\u017ea MM, Barjaktarovi\u0107 M\u010c. Classification of brain tumors from MRI images using a convolutional neural network. Appl Sci. 2020;10(6):1999. https:\/\/doi.org\/10.3390\/app10061999.","journal-title":"Appl Sci"},{"key":"370_CR13","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1186\/s12909-023-04698-z","volume":"23","author":"SA Alowais","year":"2023","unstructured":"Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689. https:\/\/doi.org\/10.1186\/s12909-023-04698-z.","journal-title":"BMC Med Educ"},{"issue":"3","key":"370_CR14","doi-asserted-by":"publisher","DOI":"10.2196\/10010","volume":"7","author":"J Shen","year":"2019","unstructured":"Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang PH, Ming WK. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inform. 2019;7(3): e10010. https:\/\/doi.org\/10.2196\/10010.","journal-title":"JMIR Med Inform"},{"issue":"3","key":"370_CR15","doi-asserted-by":"publisher","first-page":"e138","DOI":"10.1016\/S2589-7500(20)30003-0","volume":"2","author":"HE Kim","year":"2020","unstructured":"Kim HE, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader. Lancet Digit Health. 2020;2(3):e138\u201348. https:\/\/doi.org\/10.1016\/S2589-7500(20)30003-0.","journal-title":"Lancet Digit Health"},{"issue":"6","key":"370_CR16","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.3390\/diagnostics12061465","volume":"12","author":"J Becker","year":"2022","unstructured":"Becker J, Decker JA, R\u00f6mmele C, Kahn M, Messmann H, Wehler M, Schwarz F, Kroencke T, Scheurig-Muenkler C. Artificial intelligence-based detection of pneumonia in chest radiographs. Diagnostics. 2022;12(6):1465. https:\/\/doi.org\/10.3390\/diagnostics12061465.","journal-title":"Diagnostics"},{"key":"370_CR17","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva A, Kuprel B, Novoa R, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115\u20138. https:\/\/doi.org\/10.1038\/nature21056.","journal-title":"Nature"},{"key":"370_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.arr.2023.102013","volume":"90","author":"R Gupta","year":"2023","unstructured":"Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson\u2019s disease. Ageing Res Rev. 2023;90: 102013. https:\/\/doi.org\/10.1016\/j.arr.2023.102013.","journal-title":"Ageing Res Rev"},{"issue":"8","key":"370_CR19","doi-asserted-by":"publisher","first-page":"1850010","DOI":"10.1142\/S0129065718500107","volume":"28","author":"Q Yuan","year":"2018","unstructured":"Yuan Q, Zhou W, Xu F, Leng Y, Wei D. Epileptic EEG identification via LBP operators on wavelet coefficients. Int J Neural Syst. 2018;28(8):1850010. https:\/\/doi.org\/10.1142\/S0129065718500107.","journal-title":"Int J Neural Syst"},{"issue":"4","key":"370_CR20","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1038\/s41551-020-00614-8","volume":"5","author":"Y Zhang","year":"2020","unstructured":"Zhang Y, Wu W, Toll RT, Naparstek S, Maron-Katz A, Watts M, Gordon J, Jeong J, Astolfi L, Shpigel E, Longwell P, Sarhadi K, El-Said D, Li Y, Cooper C, Chin-Fatt C, Arns M, Goodkind MS, Trivedi MH, Marmar CR, Etkin A. Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography. Nat Biomed Eng. 2020;5(4):309\u201323. https:\/\/doi.org\/10.1038\/s41551-020-00614-8.","journal-title":"Nat Biomed Eng"},{"issue":"20","key":"370_CR21","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","volume":"132","author":"RC Deo","year":"2015","unstructured":"Deo RC. Machine learning in medicine. Circulation. 2015;132(20):1920\u201330. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.115.001593.","journal-title":"Circulation"},{"key":"370_CR22","first-page":"249","volume":"31","author":"S Kotsiantis","year":"2007","unstructured":"Kotsiantis S. Supervised machine learning: a review of classification techniques. Informatica (Slovenia). 2007;31:249\u201368.","journal-title":"Informatica (Slovenia)"},{"key":"370_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3201576","author":"Y Chen","year":"2022","unstructured":"Chen Y, Mancini M, Zhu X, Akata Z. Semi-supervised and unsupervised deep visual learning: a survey. IEEE Trans Pattern Anal Mach Intell. 2022. https:\/\/doi.org\/10.1109\/TPAMI.2022.3201576.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"370_CR24","doi-asserted-by":"publisher","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M. Playing Atari with Deep Reinforcement Learning. 2013; https:\/\/doi.org\/10.48550\/arXiv.1312.5602","DOI":"10.48550\/arXiv.1312.5602"},{"issue":"6","key":"370_CR25","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1016\/j.rcl.2021.06.004","volume":"59","author":"BJ Erickson","year":"2021","unstructured":"Erickson BJ. Basic artificial intelligence techniques: machine learning and deep learning. Radiol Clin North Am. 2021;59(6):933\u201340. https:\/\/doi.org\/10.1016\/j.rcl.2021.06.004.","journal-title":"Radiol Clin North Am"},{"issue":"521","key":"370_CR26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"2015","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;2015(521):436\u201344. https:\/\/doi.org\/10.1038\/nature14539.","journal-title":"Nature"},{"issue":"3","key":"370_CR27","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.bpsc.2017.11.007","volume":"3","author":"D Bzdok","year":"2017","unstructured":"Bzdok D, Meyer-Lindenberg A. Machine learning for precision psychiatry: opportunities and challenges. Biol Psychiatry Cognit Neurosci Neuroimag. 2017;3(3):223\u201330. https:\/\/doi.org\/10.1016\/j.bpsc.2017.11.007.","journal-title":"Biol Psychiatry Cognit Neurosci Neuroimag"},{"issue":"09","key":"370_CR28","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1017\/s0033291719000151","volume":"49","author":"ABR Shatte","year":"2019","unstructured":"Shatte ABR, Hutchinson DM, Teague SJ. Machine learning in mental health: a scoping review of methods and applications. Psychol Med. 2019;49(09):1426\u201348. https:\/\/doi.org\/10.1017\/s0033291719000151.","journal-title":"Psychol Med"},{"issue":"1","key":"370_CR29","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1002\/lio2.354","volume":"5","author":"DM Low","year":"2020","unstructured":"Low DM, Bentley KH, Ghosh SS. Automated assessment of psychiatric disorders using speech: a systematic review. Laryngoscope Investig Otolaryngol. 2020;5(1):96\u2013116. https:\/\/doi.org\/10.1002\/lio2.354.","journal-title":"Laryngoscope Investig Otolaryngol"},{"issue":"11","key":"370_CR30","doi-asserted-by":"publisher","first-page":"1037","DOI":"10.1111\/cns.13048","volume":"24","author":"S Gao","year":"2018","unstructured":"Gao S, Calhoun VD, Sui J. Machine learning in major depression: from classification to treatment outcome prediction. CNS Neurosci Ther. 2018;24(11):1037\u201352. https:\/\/doi.org\/10.1111\/cns.13048.","journal-title":"CNS Neurosci Ther"},{"issue":"1\u20133","key":"370_CR31","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1159\/000504292","volume":"82","author":"U Raghavendra","year":"2019","unstructured":"Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol. 2019;82(1\u20133):41\u201364. https:\/\/doi.org\/10.1159\/000504292.","journal-title":"Eur Neurol"},{"issue":"8","key":"370_CR32","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","volume":"16","author":"MA Myszczynska","year":"2020","unstructured":"Myszczynska MA, Ojamies PN, Lacoste AMB, Neil D, Saffari A, Mead R, Hautbergue GM, Holbrook JD, Ferraiuolo L. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol. 2020;16(8):440\u201356. https:\/\/doi.org\/10.1038\/s41582-020-0377-8.","journal-title":"Nat Rev Neurol"},{"issue":"2","key":"370_CR33","doi-asserted-by":"publisher","first-page":"21","DOI":"10.3390\/jpm10020021","volume":"10","author":"G Battineni","year":"2020","unstructured":"Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of machine learning predictive models in the chronic disease diagnosis. J Pers Med. 2020;10(2):21. https:\/\/doi.org\/10.3390\/jpm10020021.","journal-title":"J Pers Med"},{"issue":"11","key":"370_CR34","doi-asserted-by":"publisher","first-page":"5780","DOI":"10.3390\/ijerph18115780","volume":"18","author":"A Shoeibi","year":"2021","unstructured":"Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Panahiazar M, Khozeimeh F, Zare A, Hosseini-Nejad H, Khosravi A, Atiya AF, Aminshahidi D, Hussain S, Rouhani M, Nahavandi S, Acharya UR. Epileptic seizures detection using deep learning techniques: a review. Int J Environ Res Public Health. 2021;18(11):5780. https:\/\/doi.org\/10.3390\/ijerph18115780.","journal-title":"Int J Environ Res Public Health"},{"issue":"1s","key":"370_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/32410562","volume":"15","author":"MS Hossain","year":"2019","unstructured":"Hossain MS, Amin SU, Alsulaiman M, Muhammad G. Applying deep learning for epilepsy seizure detection and brain mapping visualization. ACM Trans Multimed Comput Commun Appl. 2019;15(1s):1\u201317. https:\/\/doi.org\/10.1145\/32410562.","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"key":"370_CR36","doi-asserted-by":"publisher","unstructured":"Pisner DA, Schnyer DM. Support vector machine. In Elsevier eBooks. 2019; 101\u2013121. https:\/\/doi.org\/10.1016\/b978-0-12-815739-8.00006-7","DOI":"10.1016\/b978-0-12-815739-8.00006-7"},{"issue":"12","key":"370_CR37","doi-asserted-by":"publisher","first-page":"1565","DOI":"10.1038\/nbt1206-1565","volume":"24","author":"WS Noble","year":"2006","unstructured":"Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565\u20137. https:\/\/doi.org\/10.1038\/nbt1206-1565.","journal-title":"Nat Biotechnol"},{"key":"370_CR38","doi-asserted-by":"publisher","unstructured":"Shyam R. Convolutional Neural Network and its Architectures. 2021. https:\/\/doi.org\/10.37591\/JoCTA","DOI":"10.37591\/JoCTA"},{"issue":"04","key":"370_CR39","doi-asserted-by":"publisher","first-page":"190","DOI":"10.4236\/jdaip.2019.74012","volume":"07","author":"EY Boateng","year":"2019","unstructured":"Boateng EY, Abaye DA. A review of the logistic regression model with emphasis on medical research. J Data Anal Inf Process. 2019;07(04):190\u2013207. https:\/\/doi.org\/10.4236\/jdaip.2019.74012.","journal-title":"J Data Anal Inf Process"},{"key":"370_CR40","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1126\/science.3287615","volume":"240","author":"J Swets","year":"1988","unstructured":"Swets J. Measuring the accuracy of diagnostic systems. Science. 1988;240:1285\u201393. https:\/\/doi.org\/10.1126\/science.3287615.","journal-title":"Science"},{"issue":"3","key":"370_CR41","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1109\/tkde.2005.50","volume":"17","author":"NJ Huang","year":"2005","unstructured":"Huang NJ, Ling C. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans Knowl Data Eng. 2005;17(3):299\u2013310. https:\/\/doi.org\/10.1109\/tkde.2005.50.","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"370_CR42","unstructured":"Ling CX, Huang J, Zhang H. AUC: a statistically consistent and more discriminating measure than accuracy. International Joint Conference on Artificial Intelligence. 2003; 519\u2013524. https:\/\/cling.csd.uwo.ca\/papers\/ijcai03.pdf"},{"issue":"12","key":"370_CR43","doi-asserted-by":"publisher","first-page":"1257","DOI":"10.1038\/ki.2009.92","volume":"75","author":"KJ Van Stralen","year":"2009","unstructured":"Van Stralen KJ, Stel VS, Reitsma JB, Dekker FW, Zoccali C, Jager KJ. Diagnostic methods I: sensitivity, specificity, and other measures of accuracy. Kidney Int. 2009;75(12):1257\u201363. https:\/\/doi.org\/10.1038\/ki.2009.92.","journal-title":"Kidney Int"},{"issue":"1","key":"370_CR44","doi-asserted-by":"publisher","first-page":"45","DOI":"10.4103\/0301-4738.37595","volume":"56","author":"R Parikh","year":"2008","unstructured":"Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol. 2008;56(1):45\u201350. https:\/\/doi.org\/10.4103\/0301-4738.37595.","journal-title":"Indian J Ophthalmol"},{"key":"370_CR45","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.cmpb.2018.04.012","volume":"161","author":"UR Acharya","year":"2018","unstructured":"Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H, Subha DP. Automated EEG-based screening of depression using deep convolutional neural network. Comput Methods Progr Biomed. 2018;161:103\u201313. https:\/\/doi.org\/10.1016\/j.cmpb.2018.04.012.","journal-title":"Comput Methods Progr Biomed"},{"issue":"1","key":"370_CR46","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1001\/jamapsychiatry.2014.1754","volume":"72","author":"RC Kessler","year":"2014","unstructured":"Kessler RC, Warner CH, Ivany C, Petukhova MV, Rose S, Bromet EJ, Brown M, Cai T, Colpe LJ, Cox KL, Fullerton CS, Gilman SE, Gruber MJ, Heeringa SG, Lewandowski-Romps L, Li J, Millikan-Bell AM, Naifeh JA, Nock MK, Rosellini AJ, Sampson NA, Schoenbaum M, Stein MB, Wessely S, Zavslasky AM, Ursano RJ. Predicting suicides after psychiatric hospitalization in US army soldiers. JAMA Psychiat. 2014;72(1):49. https:\/\/doi.org\/10.1001\/jamapsychiatry.2014.1754.","journal-title":"JAMA Psychiat"},{"issue":"4","key":"370_CR47","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1002\/hbm.22278","volume":"35","author":"L Zeng","year":"2013","unstructured":"Zeng L, Shen H, Liu L, Hu D. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp. 2013;35(4):1630\u201341. https:\/\/doi.org\/10.1002\/hbm.22278.","journal-title":"Hum Brain Mapp"},{"issue":"8","key":"370_CR48","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1111\/jcpp.12559","volume":"57","author":"D Bone","year":"2016","unstructured":"Bone D, Bishop SL, Black MP, Goodwin MS, Lord C, Narayanan SS. Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion. J Child Psychol Psychiatry. 2016;57(8):927\u201337. https:\/\/doi.org\/10.1111\/jcpp.12559.","journal-title":"J Child Psychol Psychiatry"},{"key":"370_CR49","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-015-0399-8","author":"K Karstoft","year":"2015","unstructured":"Karstoft K, Galatzer-Levy IR, Statnikov A, Li Z, Shalev AY. Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry. 2015. https:\/\/doi.org\/10.1186\/s12888-015-0399-8.","journal-title":"BMC Psychiatry"},{"key":"370_CR50","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1519-7","author":"R Gautam","year":"2020","unstructured":"Gautam R, Sharma M. Prevalence and diagnosis of neurological disorders using different deep learning techniques: a meta-analysis. J Med Syst. 2020. https:\/\/doi.org\/10.1007\/s10916-019-1519-7.","journal-title":"J Med Syst"},{"issue":"3","key":"370_CR51","doi-asserted-by":"publisher","DOI":"10.1038\/tp.2017.38","volume":"7","author":"IR Galatzer-Levy","year":"2017","unstructured":"Galatzer-Levy IR, Ma S, Statnikov A, Yehuda R, Shalev AY. Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl Psychiatry. 2017;7(3): e1070. https:\/\/doi.org\/10.1038\/tp.2017.38.","journal-title":"Transl Psychiatry"},{"key":"370_CR52","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1017\/S1461145707008127","volume":"11","author":"AY Shalev","year":"2008","unstructured":"Shalev AY, Videlock EJ, Peleg T, Segman R, Pitman RK, Yehuda R. Stress hormones and post-traumatic stress disorder in civilian trauma victims: a longitudinal study. Part I: HPA axis responses. Int J Neuropsychopharmacol. 2008;11:365\u201372. https:\/\/doi.org\/10.1017\/S1461145707008127.","journal-title":"Int J Neuropsychopharmacol"},{"key":"370_CR53","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1017\/S1461145707008139","volume":"11","author":"EJ Videlock","year":"2008","unstructured":"Videlock EJ, Peleg T, Segman R, Yehuda R, Pitman RK, Shalev AY. Stress hormones and post-traumatic stress disorder in civilian trauma victims: a longitudinal study. Part II: the adrenergic response. Int J Neuropsychopharmacol. 2008;11:373\u201380. https:\/\/doi.org\/10.1017\/S1461145707008139.","journal-title":"Int J Neuropsychopharmacol"},{"key":"370_CR54","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.media.2018.05.004","volume":"48","author":"N Amoroso","year":"2018","unstructured":"Amoroso N, La Rocca M, Monaco A, Bellotti R, Tangaro S. Complex networks reveal early MRI markers of Parkinson\u2019s disease. Med Image Anal. 2018;48:12\u201324. https:\/\/doi.org\/10.1016\/j.media.2018.05.004.","journal-title":"Med Image Anal"},{"key":"370_CR55","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2020.1005","volume":"28","author":"E Eyigoz","year":"2020","unstructured":"Eyigoz E, Mathur S, Santamaria M, Cecchi G, Naylor M. Linguistic markers predict onset of Alzheimer\u2019s disease. EClinicalMedicine. 2020;28: 100583. https:\/\/doi.org\/10.1016\/j.eclinm.2020.1005.","journal-title":"EClinicalMedicine"},{"key":"370_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102472","volume":"110","author":"NA Wani","year":"2024","unstructured":"Wani NA, Kumar R, Bedi J, Mamta, Rida I. Explainable AI-driven IoMT fusion: unravelling techniques, opportunities, and challenges with explainable AI in healthcare. Inf Fusion. 2024;110: 102472. https:\/\/doi.org\/10.1016\/j.inffus.2024.102472.","journal-title":"Inf Fusion"},{"key":"370_CR57","doi-asserted-by":"publisher","DOI":"10.1016\/j.health.2023.100170","volume":"3","author":"K Chadaga","year":"2023","unstructured":"Chadaga K, Prabhu S, Sampathila N, Chadaga R. A machine learning and explainable artificial intelligence approach for predicting the efficacy of hematopoietic stem cell transplant in pediatric patients. Healthc Anal. 2023;3: 100170. https:\/\/doi.org\/10.1016\/j.health.2023.100170.","journal-title":"Healthc Anal"},{"issue":"8","key":"370_CR58","doi-asserted-by":"publisher","first-page":"435","DOI":"10.3390\/info14080435","volume":"14","author":"K Chadaga","year":"2023","unstructured":"Chadaga K, Sampathila N, Prabhu S, Chadaga R. Multiple explainable approaches to predict the risk of stroke using artificial intelligence. Information. 2023;14(8):435. https:\/\/doi.org\/10.3390\/info14080435.","journal-title":"Information"},{"key":"370_CR59","doi-asserted-by":"publisher","unstructured":"Wani NA, Bedi J, Kumar R, Khan MA and Rida I. Synergizing Fusion Modelling for Accurate Cardiac Prediction Through Explainable Artificial Intelligence,\" in IEEE Transactions on Consumer Electronics. 2024. https:\/\/doi.org\/10.1109\/TCE.2024.3419814","DOI":"10.1109\/TCE.2024.3419814"},{"key":"370_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107879","volume":"243","author":"NA Wani","year":"2023","unstructured":"Wani NA, Kumar R, Bedi J. DeepXplainer: An interpretable deep learning-based approach for lung cancer detection using explainable artificial intelligence. Comput Methods Progr Biomed. 2023;243: 107879. https:\/\/doi.org\/10.1016\/j.cmpb.2023.107879.","journal-title":"Comput Methods Progr Biomed"},{"key":"370_CR61","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108939","volume":"136","author":"NA Wani","year":"2024","unstructured":"Wani NA, Kumar R, Bedi J. Harnessing fusion modeling for enhanced breast cancer classification through interpretable artificial intelligence and in-depth explanations. Eng Appl Artif Intell. 2024;136: 108939. https:\/\/doi.org\/10.1016\/j.engappai.2024.108939.","journal-title":"Eng Appl Artif Intell"},{"key":"370_CR62","doi-asserted-by":"publisher","DOI":"10.1080\/23311916.2024.2314872","author":"N Goenka","year":"2024","unstructured":"Goenka N, Sharma AK, Tiwari S, Singh N, Yadav V, Prabhu S, Chadaga K. A regularized volumetric ConvNet based Alzheimer detection using T1-weighted MRI images. Cogent Eng. 2024. https:\/\/doi.org\/10.1080\/23311916.2024.2314872.","journal-title":"Cogent Eng"},{"issue":"1","key":"370_CR63","doi-asserted-by":"publisher","first-page":"20230179","DOI":"10.1515\/jisys-2023-0179","volume":"33","author":"N Goswami","year":"2024","unstructured":"Goswami N, Goswami A, Sampathila N, Bairy M, Chadaga K, Belurkar S. Detection of sickle cell disease using deep neural networks and explainable artificial intelligence. J Intell Syst. 2024;33(1):20230179. https:\/\/doi.org\/10.1515\/jisys-2023-0179.","journal-title":"J Intell Syst"}],"container-title":["Discover Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00370-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44163-025-00370-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44163-025-00370-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T09:13:07Z","timestamp":1749978787000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44163-025-00370-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,15]]},"references-count":63,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["370"],"URL":"https:\/\/doi.org\/10.1007\/s44163-025-00370-1","relation":{},"ISSN":["2731-0809"],"issn-type":[{"value":"2731-0809","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,15]]},"assertion":[{"value":"24 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","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":"105"}}