{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:22:33Z","timestamp":1788884553052,"version":"build-2803163510"},"reference-count":201,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T00:00:00Z","timestamp":1730160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Neurology International"],"abstract":"<jats:p>In recent years, Artificial Intelligence (AI) methods, specifically Machine Learning (ML) models, have been providing outstanding results in different areas of knowledge, with the health area being one of its most impactful fields of application. However, to be applied reliably, these models must provide users with clear, simple, and transparent explanations about the medical decision-making process. This systematic review aims to investigate the use and application of explainability in ML models used in brain disease studies. A systematic search was conducted in three major bibliographic databases, Web of Science, Scopus, and PubMed, from January 2014 to December 2023. A total of 133 relevant studies were identified and analyzed out of a total of 682 found in the initial search, in which the explainability of ML models in the medical context was studied, identifying 11 ML models and 12 explainability techniques applied in the study of 20 brain diseases.<\/jats:p>","DOI":"10.3390\/neurolint16060098","type":"journal-article","created":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T10:45:35Z","timestamp":1730198735000},"page":"1285-1307","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5344-7952","authenticated-orcid":false,"given":"Mirko Jerber","family":"Rodr\u00edguez Mallma","sequence":"first","affiliation":[{"name":"Facultad de Ingenier\u00eda Industrial y de Sistemas, Universidad Nacional de Ingenier\u00eda, Lima 15333, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9289-3632","authenticated-orcid":false,"given":"Luis","family":"Zuloaga-Rotta","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Industrial y de Sistemas, Universidad Nacional de Ingenier\u00eda, Lima 15333, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2081-551X","authenticated-orcid":false,"given":"Rub\u00e9n","family":"Borja-Rosales","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Industrial y de Sistemas, Universidad Nacional de Ingenier\u00eda, Lima 15333, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8819-1985","authenticated-orcid":false,"given":"Josef Renato","family":"Rodr\u00edguez Mallma","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda Industrial y de Sistemas, Universidad Nacional de Ingenier\u00eda, Lima 15333, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcos","family":"Vilca-Aguilar","sequence":"additional","affiliation":[{"name":"Instituto de Radiocirug\u00eda del Per\u00fa, Cl\u00ednica San Pablo, Lima 150140, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2417-6898","authenticated-orcid":false,"given":"Mar\u00eda","family":"Salas-Ojeda","sequence":"additional","affiliation":[{"name":"Facultad de Artes y Humanidades, Universidad San Ignacio de Loyola, Lima 15024, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Mauricio","sequence":"additional","affiliation":[{"name":"Facultad de Ingenier\u00eda de Sistemas e Inform\u00e1tica, Universidad Nacional Mayor de San Marcos, Lima 15081, Peru"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Segato, A., Marzullo, A., Calimeri, F., and De Momi, E. (2020). Artificial Intelligence for Brain Diseases: A Systematic Review. APL Bioeng., 4.","DOI":"10.1063\/5.0011697"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.3748\/wjg.v25.i14.1666","article-title":"Application of Artificial Intelligence in Gastroenterology","volume":"25","author":"Yang","year":"2019","journal-title":"World J. Gastroenterol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1016\/j.crad.2019.02.006","article-title":"Artificial Intelligence in Breast Imaging","volume":"74","author":"Le","year":"2019","journal-title":"Clin. Radiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11904-020-00490-6","article-title":"Artificial Intelligence and Machine Learning for HIV Prevention: Emerging Approaches to Ending the Epidemic","volume":"17","author":"Marcus","year":"2020","journal-title":"Curr. HIV\/AIDS Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1167\/tvst.9.2.55","article-title":"Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice","volume":"9","author":"Ng","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_6","first-page":"397","article-title":"Potenziali conseguenze inattese dell\u2019uso di sistemi di intelligenza artificiale oracolari in medicina","volume":"108","author":"Cabitza","year":"2017","journal-title":"Recent. Progress Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"eaay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable Artificial Intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s44163-024-00114-7","article-title":"Explainable and Interpretable Artificial Intelligence in Medicine: A Systematic Bibliometric Review","volume":"4","author":"Frasca","year":"2024","journal-title":"Discov. Artif. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e1391","DOI":"10.1002\/widm.1391","article-title":"A Historical Perspective of Explainable Artificial Intelligence","volume":"11","author":"Confalonieri","year":"2021","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s00146-020-01008-9","article-title":"Ethical and Legal Challenges of Informed Consent Applying Artificial Intelligence in Medical Diagnostic Consultations","volume":"36","author":"Astromskis","year":"2021","journal-title":"AI Soc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101901","DOI":"10.1016\/j.artmed.2020.101901","article-title":"The Four Dimensions of Contestable AI Diagnostics\u2014A Patient-Centric Approach to Explainable AI","volume":"107","author":"Ploug","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1053\/j.semnuclmed.2020.08.001","article-title":"Ethical and Legal Challenges of Artificial Intelligence in Nuclear Medicine","volume":"51","author":"Currie","year":"2021","journal-title":"Semin. Nucl. Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Loh, H.W., Ooi, C.P., Seoni, S., Barua, P.D., Molinari, F., and Acharya, U.R. (2022). Application of Explainable Artificial Intelligence for Healthcare: A Systematic Review of the Last Decade (2011\u20132022). Comput. Methods Programs Biomed., 226.","DOI":"10.1016\/j.cmpb.2022.107161"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"108391","DOI":"10.1016\/j.asoc.2021.108391","article-title":"Interpretability in the Medical Field: A Systematic Mapping and Review Study","volume":"117","author":"Hakkoum","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1111\/jgh.15384","article-title":"Opening the Black Box of AI-Medicine","volume":"36","author":"Poon","year":"2021","journal-title":"J. Gastroenterol. Hepatol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1016\/j.tips.2019.06.001","article-title":"The Missing Pieces of Artificial Intelligence in Medicine","volume":"40","author":"Gilvary","year":"2019","journal-title":"Trends Pharmacol. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1353\/pbm.2019.0012","article-title":"Three Problems with Big Data and Artificial Intelligence in Medicine","volume":"62","author":"Upshur","year":"2019","journal-title":"Perspect. Biol. Med."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fraccaro, P., Nicolo, M., Bonetto, M., Giacomini, M., Weller, P., Traverso, C.E., Prosperi, M., and OSullivan, D. (2015). Combining Macula Clinical Signs and Patient Characteristics for Age-Related Macular Degeneration Diagnosis: A Machine Learning Approach. BMC Ophthalmol., 15.","DOI":"10.1186\/1471-2415-15-10"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.eswa.2016.01.029","article-title":"Cardiac ScoreCard: A Diagnostic Multivariate Index Assay System for Predicting a Spectrum of Cardiovascular Disease","volume":"54","author":"McRae","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1038\/s41467-019-14225-8","article-title":"Machine Learning Can Identify Newly Diagnosed Patients with CLL at High Risk of Infection","volume":"11","author":"Agius","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"15839","DOI":"10.1109\/JIOT.2020.3037915","article-title":"An Explainable System for Diagnosis and Prognosis of COVID-19","volume":"8","author":"Lu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e735","DOI":"10.1016\/S2214-109X(23)00602-2","article-title":"Global Brain Health\u2014The Time to Act Is Now","volume":"12","author":"Winkler","year":"2024","journal-title":"Lancet Glob. Health"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.neuro.2017.04.002","article-title":"Determinants of Neurological Disease: Synthesis of Systematic Reviews","volume":"61","author":"Krewski","year":"2017","journal-title":"NeuroToxicology"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6","DOI":"10.2174\/1874196702008010006","article-title":"Human Brain Disorders: A Review","volume":"8","author":"Naz","year":"2020","journal-title":"Open Biol. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"m3683","DOI":"10.1136\/bmj.m3683","article-title":"What Is Brain Health and Why Is It Important?","volume":"371","author":"Wang","year":"2020","journal-title":"BMJ"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e12330","DOI":"10.1002\/trc2.12330","article-title":"Challenges and Progress in Research, Diagnostics, and Therapeutics in Alzheimer\u2019s Disease and Related Dementias","volume":"8","author":"Dokholyan","year":"2022","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ningrum, D.N.A., and Kung, W.-M. (2023). Challenges and Perspectives of Neurological Disorders. Brain Sci., 13.","DOI":"10.3390\/brainsci13040676"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1159\/000504292","article-title":"Artificial Intelligence Techniques for Automated Diagnosis of Neurological Disorders","volume":"82","author":"Raghavendra","year":"2019","journal-title":"Eur. Neurol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1097\/WCO.0000000000000838","article-title":"Machine Learning for Classification and Prediction of Brain Diseases: Recent Advances and Upcoming Challenges","volume":"33","author":"Burgos","year":"2020","journal-title":"Curr. Opin. Neurol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.neucom.2022.07.005","article-title":"Applicable Artificial Intelligence for Brain Disease: A Survey","volume":"504","author":"Huang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_31","unstructured":"Pillai, A.S., and Menon, B. (2022). 10-Artificial Intelligence in the Management of Neurological Disorders: Its Prevalence and Prominence. Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence, Academic Press."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Colliot, O. (2023). Neuroimaging in Machine Learning for Brain Disorders. Machine Learning for Brain Disorders, Springer.","DOI":"10.1007\/978-1-0716-3195-9"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bhupathyraaj, M., Vijayarani, K.R., Dhanasekaran, M., and Essa, M.M. (2024). The Thriving Influence of Artificial Intelligence in Neuroscience. Application of Artificial Intelligence in Neurological Disorders, Springer Nature.","DOI":"10.1007\/978-981-97-2577-9"},{"key":"ref_34","unstructured":"(2024, June 04). EXPLANATION|English Meaning-Cambridge Dictionary. Available online: https:\/\/dictionary.cambridge.org\/dictionary\/english\/explanation."},{"key":"ref_35","unstructured":"van Lent, M., Fisher, W., and Mancuso, M. (2004, January 15\u201317). An Explainable Artificial Intelligence System for Small-Unit Tactical Behavior. Proceedings of the 16th Conference on Innovative Applications of Artifical Intelligence, Boca Raton, FL, USA."},{"key":"ref_36","unstructured":"(2024, June 04). Regulation-2016\/679-EN-Gdpr-EUR-Lex. Available online: https:\/\/eur-lex.europa.eu\/eli\/reg\/2016\/679\/oj."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.inffus.2019.12.012","article-title":"Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI","volume":"58","author":"Bennetot","year":"2020","journal-title":"Inf. Fusion."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"52138","DOI":"10.1109\/ACCESS.2018.2870052","article-title":"Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)","volume":"6","author":"Adadi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Banegas-Luna, A.J., Pe\u00f1a-Garc\u00eda, J., Iftene, A., Guadagni, F., Ferroni, P., Scarpato, N., Zanzotto, F.M., Bueno-Crespo, A., and P\u00e9rez-S\u00e1nchez, H. (2021). Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22094394"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e27293","DOI":"10.2196\/27293","article-title":"Classification Models for COVID-19 Test Prioritization in Brazil: Machine Learning Approach","volume":"23","author":"Sobrinho","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Estiri, H., Strasser, Z.H., and Murphy, S.N. (2021). Individualized Prediction of COVID-19 Adverse Outcomes with MLHO. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-84781-x"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1001\/jamapsychiatry.2021.0228","article-title":"Discriminating Heterogeneous Trajectories of Resilience and Depression After Major Life Stressors Using Polygenic Scores","volume":"78","author":"Schultebraucks","year":"2021","journal-title":"JAMA Psychiatry"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/JBHI.2020.3004072","article-title":"HCET: Hierarchical Clinical Embedding With Topic Modeling on Electronic Health Records for Predicting Future Depression","volume":"25","author":"Meng","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1038\/s41746-020-00377-1","article-title":"Development and Validation of an Interpretable Neural Network for Prediction of Postoperative In-Hospital Mortality","volume":"4","author":"Lee","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Torrente, A., Maccora, S., Prinzi, F., Alonge, P., Pilati, L., Lupica, A., Di Stefano, V., Camarda, C., Vitabile, S., and Brighina, F. (2024). The Clinical Relevance of Artificial Intelligence in Migraine. Brain Sci., 14.","DOI":"10.3390\/brainsci14010085"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1515\/revneuro-2023-0050","article-title":"Automated Diagnosis of Autism with Artificial Intelligence: State of the Art","volume":"35","author":"Valizadeh","year":"2023","journal-title":"Rev. Neurosci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1016\/j.surg.2020.06.049","article-title":"Leveraging Interpretable Machine Learning Algorithms to Predict Postoperative Patient Outcomes on Mobile Devices","volume":"169","author":"Maurer","year":"2021","journal-title":"Surgery"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"154096","DOI":"10.1109\/ACCESS.2019.2949286","article-title":"Black-Box vs. White-Box: Understanding Their Advantages and Weaknesses From a Practical Point of View","volume":"7","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"Molnar, C. (2024, March 20). Interpretable Machine Learning; Lulu.com: 2020; ISBN 978-0-244-76852-2. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/cite.html."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"n160","DOI":"10.1136\/bmj.n160","article-title":"PRISMA 2020 Explanation and Elaboration: Updated Guidance and Exemplars for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1016\/j.ijnurstu.2009.02.010","article-title":"What Are Scoping Studies? A Review of the Nursing Literature","volume":"46","author":"Davis","year":"2009","journal-title":"Int. J. Nurs. Stud."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Colliot, O. (2023). Classic Machine Learning Methods. Machine Learning for Brain Disorders, Humana.","DOI":"10.1007\/978-1-0716-3195-9"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"An, Q., Rahman, S., Zhou, J., and Kang, J.J. (2023). A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges. Sensors, 23.","DOI":"10.3390\/s23094178"},{"key":"ref_56","unstructured":"Wells, G., Shea, B., O\u2019Connell, D., Peterson, J., Losos, M., Tugwell, P., Ga, S.W., Zello, G., and Petersen, J. (2024, May 04). The Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Nonrandomised Studies in Meta-Analyses. Available online: https:\/\/api.semanticscholar.org\/CorpusID:79550924."},{"key":"ref_57","unstructured":"(2024, June 15). PRISMA 2020 Flow Diagram. Available online: https:\/\/www.prisma-statement.org\/prisma-2020-flow-diagram."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ta\u015fc\u0131, B. (2023). Attention Deep Feature Extraction from Brain MRIs in Explainable Mode: DGXAINet. Diagnostics, 13.","DOI":"10.3390\/diagnostics13050859"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s40042-021-00202-2","article-title":"Cerebral Hemorrhage Detection and Localization with Medical Imaging for Cerebrovascular Disease Diagnosis and Treatment Using Explainable Deep Learning","volume":"79","author":"Kim","year":"2021","journal-title":"J. Korean Phys. Soc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"102171","DOI":"10.1016\/j.compmedimag.2022.102171","article-title":"Towards Better Interpretable and Generalizable AD Detection Using Collective Artificial Intelligence","volume":"104","author":"Nguyen","year":"2023","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jeong, T., Park, U., and Kang, S.W. (2022). Novel Quantitative Electroencephalogram Feature Image Adapted for Deep Learning: Verification through Classification of Alzheimer\u2019s Disease Dementia. Front. Neurosci., 16.","DOI":"10.3389\/fnins.2022.1033379"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2513107","DOI":"10.1109\/TIM.2021.3107056","article-title":"Alzheimer\u2019s Patient Analysis Using Image and Gene Expression Data and Explainable-AI to Present Associated Genes","volume":"70","author":"Kamal","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Varzandian, A., Razo, M.A.S., Sanders, M.R., Atmakuru, A., and Di Fatta, G. (2021). Classification-Biased Apparent Brain Age for the Prediction of Alzheimer\u2019s Disease. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.673120"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Maqsood, S., Dama\u0161evi\u010dius, R., and Maskeli\u016bnas, R. (2022). Multi-Modal Brain Tumor Detection Using Deep Neural Network and Multiclass SVM. Medicina, 58.","DOI":"10.3390\/medicina58081090"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"15656","DOI":"10.1007\/s10489-022-04276-8","article-title":"A Type-2 Neuro-Fuzzy System with a Novel Learning Method for Parkinson\u2019s Disease Diagnosis","volume":"53","author":"Hashemi","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"103405","DOI":"10.1016\/j.nicl.2023.103405","article-title":"Explainable Classification of Parkinson\u2019s Disease Using Deep Learning Trained on a Large Multi-Center Database of T1-Weighted MRI Datasets","volume":"38","author":"Camacho","year":"2023","journal-title":"Neuroimage Clin."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1995","DOI":"10.32604\/cmc.2022.029073","article-title":"Automated Machine Learning for Epileptic Seizure Detection Based on EEG Signals","volume":"73","author":"Liu","year":"2022","journal-title":"CMC"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Kokkotis, C., Giarmatzis, G., Giannakou, E., Moustakidis, S., Tsatalas, T., Tsiptsios, D., Vadikolias, K., and Aggelousis, N. (2022). An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102392"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Lee, H. (2023). PINNet: A Deep Neural Network with Pathway Prior Knowledge for Alzheimer\u2019s Disease. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1126156"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"2910","DOI":"10.1093\/brain\/awab453","article-title":"A Recurrent Machine Learning Model Predicts Intracranial Hypertension in Neurointensive Care Patients","volume":"145","author":"Schweingruber","year":"2022","journal-title":"Brain"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"68366","DOI":"10.1109\/ACCESS.2023.3291406","article-title":"Explainable Artificial Intelligence (EXAI) Models for Early Prediction of Parkinson\u2019s Disease Based on Spiral and Wave Drawings","volume":"11","author":"Saravanan","year":"2023","journal-title":"IEEE Access"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"119709","DOI":"10.1016\/j.eswa.2023.119709","article-title":"An Evolutionary Explainable Deep Learning Approach for Alzheimer\u2019s MRI Classification","volume":"220","author":"Shojaei","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_73","first-page":"189","article-title":"An Explainable Convolutional Neural Network for the Early Diagnosis of Alzheimer\u2019s Disease from 18F-FDG PET","volume":"36","author":"Pasini","year":"2023","journal-title":"J. Digit. Imaging"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Karim, A., Su, Z., West, P.K., Keon, M., The NYGC ALS Consortium, Shamsani, J., Brennan, S., Wong, T., Milicevic, O., and Teunisse, G. (2021). Molecular Classification and Interpretation of Amyotrophic Lateral Sclerosis Using Deep Convolution Neural Networks and Shapley Values. Genes, 12.","DOI":"10.20944\/preprints202110.0059.v1"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.1109\/TNSRE.2023.3288835","article-title":"Early Detection of Alzheimer\u2019s Disease From Cortical and Hippocampal Local Field Potentials Using an Ensembled Machine Learning Model","volume":"31","author":"Fabietti","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"119929","DOI":"10.1016\/j.neuroimage.2023.119929","article-title":"Deep Neural Network Heatmaps Capture Alzheimer\u2019s Disease Patterns Reported in a Large Meta-Analysis of Neuroimaging Studies","volume":"269","author":"Wang","year":"2023","journal-title":"Neuroimage"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"104846","DOI":"10.1016\/j.msard.2023.104846","article-title":"Discrimination of Multiple Sclerosis Using OCT Images from Two Different Centers","volume":"77","author":"Khodabandeh","year":"2023","journal-title":"Mult. Scler. Relat. Disord."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Liu, Y., Li, Z., Ge, Q., Lin, N., and Xiong, M. (2019). Deep Feature Selection and Causal Analysis of Alzheimer\u2019s Disease. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.01198"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1016\/j.aej.2023.09.050","article-title":"Time-Series Visual Explainability for Alzheimer\u2019s Disease Progression Detection for Smart Healthcare","volume":"82","author":"Rahim","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, D., Yao, Z., Xin, B., Wang, B., Lan, C., Qin, Y., Xu, S., He, D., and Liu, Y. (2018). Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.01046"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"109098","DOI":"10.1016\/j.jneumeth.2021.109098","article-title":"Grad-CAM Helps Interpret the Deep Learning Models Trained to Classify Multiple Sclerosis Types Using Clinical Brain Magnetic Resonance Imaging","volume":"353","author":"Zhang","year":"2021","journal-title":"J. Neurosci. Methods"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3404","DOI":"10.1038\/s41467-022-31037-5","article-title":"Multimodal Deep Learning for Alzheimer\u2019s Disease Dementia Assessment","volume":"13","author":"Qiu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2000675","DOI":"10.1002\/advs.202000675","article-title":"Generalizable, Reproducible, and Neuroscientifically Interpretable Imaging Biomarkers for Alzheimer\u2019s Disease","volume":"7","author":"Jin","year":"2020","journal-title":"Adv. Sci."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"108603","DOI":"10.1109\/ACCESS.2023.3321220","article-title":"sMRI-PatchNet: A Novel Efficient Explainable Patch-Based Deep Learning Network for Alzheimer\u2019s Disease Diagnosis With Structural MRI","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"101950","DOI":"10.1016\/j.inffus.2023.101950","article-title":"Explainable and Programmable Hypergraph Convolutional Network for Imaging Genetics Data Fusion","volume":"100","author":"Bi","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1002\/ima.22762","article-title":"Interpretable Ensemble Deep Learning Model for Early Detection of Alzheimer\u2019s Disease Using Local Interpretable Model-Agnostic Explanations","volume":"32","author":"Aghaei","year":"2022","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Lee, G., Kang, B., Nho, K., Sohn, K.-A., and Kim, D. (2019). MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework. Front. Genet., 10.","DOI":"10.3389\/fgene.2019.00617"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"102636","DOI":"10.1016\/j.artmed.2023.102636","article-title":"Deep Grading for MRI-Based Differential Diagnosis of Alzheimer\u2019s Disease and Frontotemporal Dementia","volume":"144","author":"Nguyen","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.neunet.2020.03.017","article-title":"Brain MRI Analysis Using a Deep Learning Based Evolutionary Approach","volume":"126","author":"Shahamat","year":"2020","journal-title":"Neural Netw."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.neunet.2023.04.025","article-title":"A Deep Connectome Learning Network Using Graph Convolution for Connectome-Disease Association Study","volume":"164","author":"Yang","year":"2023","journal-title":"Neural Netw."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"8358","DOI":"10.3934\/mbe.2023366","article-title":"Classification of Alzheimer\u2019s Disease Using Robust TabNet Neural Networks on Genetic Data","volume":"20","author":"Jin","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref_92","unstructured":"Liang, Y., Xu, G., and ur Rehman, S. (2024, July 01). Multi-Scale Attention-Based Deep Neural Network for Brain Disease Diagnosis.|Computers, Materials & Continua|EBSCOhost. Available online: https:\/\/openurl.ebsco.com\/contentitem\/doi:10.32604%2Fcmc.2022.026999?sid=ebsco:plink:crawler&id=ebsco:doi:10.32604%2Fcmc.2022.026999."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Lee, M.-H., Yun, C.-S., Kim, K., and Lee, Y. (2022). Effect of Denoising and Deblurring 18F-Fluorodeoxyglucose Positron Emission Tomography Images on a Deep Learning Model\u2019s Classification Performance for Alzheimer\u2019s Disease. Metabolites, 12.","DOI":"10.3390\/metabo12030231"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Hoang, G.M., Kim, U.-H., and Kim, J.G. (2023). Vision Transformers for the Prediction of Mild Cognitive Impairment to Alzheimer\u2019s Disease Progression Using Mid-Sagittal sMRI. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1102869"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"4426","DOI":"10.1002\/hbm.26399","article-title":"An Automatic Interpretable Deep Learning Pipeline for Accurate Parkinson\u2019s Disease Diagnosis Using Quantitative Susceptibility Mapping and T1-Weighted Images","volume":"44","author":"Wang","year":"2023","journal-title":"Hum. Brain Mapp."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Munjal, N.K., Clark, R.S.B., Simon, D.W., Kochanek, P.M., and Horvat, C.M. (2023). Interoperable and Explainable Machine Learning Models to Predict Morbidity and Mortality in Acute Neurological Injury in the Pediatric Intensive Care Unit: Secondary Analysis of the TOPICC Study. Front. Pediatr., 11.","DOI":"10.3389\/fped.2023.1177470"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Tinauer, C., Heber, S., Pirpamer, L., Damulina, A., Schmidt, R., Stollberger, R., Ropele, S., and Langkammer, C. (2022). Interpretable Brain Disease Classification and Relevance-Guided Deep Learning. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-24541-7"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"91969","DOI":"10.1109\/ACCESS.2023.3307702","article-title":"TriAD: A Deep Ensemble Network for Alzheimer Classification and Localization","volume":"11","author":"Mercaldo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1159\/000531819","article-title":"Predicting Alzheimer\u2019s Disease with Interpretable Machine Learning","volume":"52","author":"Jia","year":"2023","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"e32508","DOI":"10.2196\/32508","article-title":"Machine Learning Models for Predicting Influential Factors of Early Outcomes in Acute Ischemic Stroke: Registry-Based Study","volume":"10","author":"Su","year":"2022","journal-title":"JMIR Med. Inf."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Park, C., Jung, W., and Suk, H.-I. (2023). Deep Joint Learning of Pathological Region Localization and Alzheimer\u2019s Disease Diagnosis. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-38240-4"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1093\/brain\/awaa137","article-title":"Development and Validation of an Interpretable Deep Learning Framework for Alzheimer\u2019s Disease Classification","volume":"143","author":"Qiu","year":"2020","journal-title":"Brain"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Lu, P., Hu, L., Zhang, N., Liang, H., Tian, T., and Lu, L. (2022). A Two-Stage Model for Predicting Mild Cognitive Impairment to Alzheimer\u2019s Disease Conversion. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.826622"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1002\/hbm.25685","article-title":"A Parallel Attention-Augmented Bilinear Network for Early Magnetic Resonance Imaging-Based Diagnosis of Alzheimer\u2019s Disease","volume":"43","author":"Guan","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Jemimah, S., and AlShehhi, A. (2023). Alzheimer\u2019s Disease Neuroimaging Initiative C-Diadem: A Constrained Dual-Input Deep Learning Model to Identify Novel Biomarkers in Alzheimer\u2019s Disease. BMC Med. Genom., 16.","DOI":"10.1186\/s12920-023-01675-9"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Yan, F., Chen, Y., Xia, Y., Wang, Z., and Xiao, R. (2023). An Explainable Brain Tumor Detection Framework for MRI Analysis. Appl. Sci., 13.","DOI":"10.3390\/app13063438"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Deatsch, A., Perovnik, M., Nam\u00edas, M., Tro\u0161t, M., and Jeraj, R. (2022). Development of a Deep Learning Network for Alzheimer\u2019s Disease Classification with Evaluation of Imaging Modality and Longitudinal Data. Phys. Med. Biol., 67.","DOI":"10.1088\/1361-6560\/ac8f10"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Safai, A., Vakharia, N., Prasad, S., Saini, J., Shah, A., Lenka, A., Pal, P.K., and Ingalhalikar, M. (2021). Multimodal Brain Connectomics-Based Prediction of Parkinson\u2019s Disease Using Graph Attention Networks. Front. Neurosci., 15.","DOI":"10.3389\/fnins.2021.741489"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.neuroscience.2023.09.003","article-title":"Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms","volume":"531","author":"Liu","year":"2023","journal-title":"Neuroscience"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.inffus.2022.11.028","article-title":"Prediction of Alzheimer\u2019s Progression Based on Multimodal Deep-Learning-Based Fusion and Visual Explainability of Time-Series Data","volume":"92","author":"Rahim","year":"2023","journal-title":"Inf. Fusion."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"116113","DOI":"10.1016\/j.neuroimage.2019.116113","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative Toward an Interpretable Alzheimer\u2019s Disease Diagnostic Model with Regional Abnormality Representation via Deep Learning","volume":"202","author":"Lee","year":"2019","journal-title":"Neuroimage"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Magesh, P.R., Myloth, R.D., and Tom, R.J. (2020). An Explainable Machine Learning Model for Early Detection of Parkinson\u2019s Disease Using LIME on DaTSCAN Imagery. Comput. Biol. Med., 126.","DOI":"10.1016\/j.compbiomed.2020.104041"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.future.2020.04.038","article-title":"Game Theoretic Interpretability for Learning Based Preoperative Gliomas Grading","volume":"112","author":"Ma","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Acquarelli, J., van Laarhoven, T., Postma, G.J., Jansen, J.J., Rijpma, A., van Asten, S., Heerschap, A., Buydens, L.M.C., and Marchiori, E. (2022). Convolutional Neural Networks to Predict Brain Tumor Grades and Alzheimer\u2019s Disease with MR Spectroscopic Imaging Data. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0268881"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"124122","DOI":"10.1109\/ACCESS.2021.3069211","article-title":"Exploring sMRI Biomarkers for Diagnosis of Autism Spectrum Disorders Based on Multi Class Activation Mapping Models","volume":"9","author":"Yang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"036007","DOI":"10.1088\/1741-2552\/ac697d","article-title":"An EEG-Based Systematic Explainable Detection Framework for Probing and Localizing Abnormal Patterns in Alzheimer\u2019s Disease","volume":"19","author":"Song","year":"2022","journal-title":"J. Neural Eng."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"120073","DOI":"10.1016\/j.neuroimage.2023.120073","article-title":"Estimating Explainable Alzheimer\u2019s Disease Likelihood Map via Clinically-Guided Prototype Learning","volume":"273","author":"Mulyadi","year":"2023","journal-title":"Neuroimage"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1109\/JSEN.2020.3018262","article-title":"Gait Spatiotemporal Signal Analysis for Parkinson\u2019s Disease Detection and Severity Rating","volume":"21","author":"Alharthi","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Lu, S., Wang, S.-H., Gorriz, J.M., and Zhang, Y.-D. (2022). DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification. Front. Syst. Neurosci., 16.","DOI":"10.3389\/fnsys.2022.838822"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Huang, L., Ye, X., Yang, M., Pan, L., and Zheng, S.H. (2023). MNC-Net: Multi-Task Graph Structure Learning Based on Node Clustering for Early Parkinson\u2019s Disease Diagnosis. Comput. Biol. Med., 152.","DOI":"10.1016\/j.compbiomed.2022.106308"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Hao, J., Kim, Y., Kim, T.-K., and Kang, M. (2018). PASNet: Pathway-Associated Sparse Deep Neural Network for Prognosis Prediction from High-Throughput Data. BMC Bioinform., 19.","DOI":"10.1186\/s12859-018-2500-z"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Shahtalebi, S., Atashzar, S.F., Patel, R.V., Jog, M.S., and Mohammadi, A. (2021). A Deep Explainable Artificial Intelligent Framework for Neurological Disorders Discrimination. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-88919-9"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Kim, S.Y. (2023). Personalized Explanations for Early Diagnosis of Alzheimer\u2019s Disease Using Explainable Graph Neural Networks with Population Graphs. Bioengineering, 10.","DOI":"10.3390\/bioengineering10060701"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Qin, Z., Liu, Z., Guo, Q., and Zhu, P. (2022). 3D Convolutional Neural Networks with Hybrid Attention Mechanism for Early Diagnosis of Alzheimer\u2019s Disease. Biomed. Signal Process. Control, 77.","DOI":"10.1016\/j.bspc.2022.103828"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"B\u00f6hle, M., Eitel, F., Weygandt, M., and Ritter, K. (2019). Layer-Wise Relevance Propagation for Explaining Deep Neural Network Decisions in MRI-Based Alzheimer\u2019s Disease Classification. Front. Aging Neurosci., 11.","DOI":"10.3389\/fnagi.2019.00194"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Williamson, B.J., Khandwala, V., Wang, D., Maloney, T., Sucharew, H., Horn, P., Haverbusch, M., Alwell, K., Gangatirkar, S., and Mahammedi, A. (2022). Automated Grading of Enlarged Perivascular Spaces in Clinical Imaging Data of an Acute Stroke Cohort Using an Interpretable, 3D Deep Learning Framework. Sci. Rep., 12.","DOI":"10.1038\/s41598-021-04287-4"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"5963","DOI":"10.3390\/cimb44120406","article-title":"Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer\u2019s Disease and Progressive Supranuclear Palsy","volume":"44","year":"2022","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Huang, D., Hao, X., Wei, J., Lu, H., and Liu, Y. (2023). UniVisNet: A Unified Visualization and Classification Network for Accurate Grading of Gliomas from MRI. Comput. Biol. Med., 165.","DOI":"10.1016\/j.compbiomed.2023.107332"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"e2200141","DOI":"10.1200\/CCI.22.00141","article-title":"Prediction of Brain Metastases Development in Patients With Lung Cancer by Explainable Artificial Intelligence From Electronic Health Records","volume":"7","author":"Li","year":"2023","journal-title":"JCO Clin. Cancer Inf."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Wang, K., Jiang, Q., Gao, M., Wei, X., Xu, C., Yin, C., Liu, H., Gu, R., Wang, H., and Li, W. (2023). A Clinical Prediction Model Based on Interpretable Machine Learning Algorithms for Prolonged Hospital Stay in Acute Ischemic Stroke Patients: A Real-World Study. Front. Endocrinol., 14.","DOI":"10.3389\/fendo.2023.1165178"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1177\/0271678X221129230","article-title":"Interpretable Deep Learning for the Prognosis of Long-Term Functional Outcome Post-Stroke Using Acute Diffusion Weighted Imaging","volume":"43","author":"Moulton","year":"2023","journal-title":"J. Cereb. Blood Flow. Metab."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Gkantzios, A., Kokkotis, C., Tsiptsios, D., Moustakidis, S., Gkartzonika, E., Avramidis, T., Tripsianis, G., Iliopoulos, I., Aggelousis, N., and Vadikolias, K. (2023). From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning. J. Pers. Med., 13.","DOI":"10.3390\/jpm13091375"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"115050","DOI":"10.1016\/j.psychres.2023.115050","article-title":"Early Identification of Autism Spectrum Disorder by Multi-Instrument Fusion: A Clinically Applicable Machine Learning Approach","volume":"320","author":"Wei","year":"2023","journal-title":"Psychiatry Res."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"3937","DOI":"10.1109\/TNSRE.2023.3314100","article-title":"An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson\u2019s Disease","volume":"31","author":"Chen","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"102545","DOI":"10.1016\/j.artmed.2023.102545","article-title":"Evaluation of Interpretability for Deep Learning Algorithms in EEG Emotion Recognition: A Case Study in Autism","volume":"143","author":"Clarkson","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"ref_136","first-page":"1471","article-title":"Enhancing Trust in Alzheimer\u2019s Disease Classification Using Explainable Artificial Intelligence: Incorporating Local Post Hoc Explanations for a Glass-Box Model","volume":"45","author":"Varghese","year":"2023","journal-title":"Bahrain Med. Bull."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/JBHI.2022.3223970","article-title":"LightSeizureNet: A Lightweight Deep Learning Model for Real-Time Epileptic Seizure Detection","volume":"27","author":"Qiu","year":"2023","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1192\/bjp.2022.13","article-title":"Deep Learning Identifies Robust Gender Differences in Functional Brain Organization and Their Dissociable Links to Clinical Symptoms in Autism","volume":"220","author":"Supekar","year":"2022","journal-title":"Br. J. Psychiatry"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Tasci, B., and Tasci, I. (2022). Deep Feature Extraction Based Brain Image Classification Model Using Preprocessed Images: PDRNet. Biomed. Signal Process. Control, 78.","DOI":"10.1016\/j.bspc.2022.103948"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Song, J., Zheng, J., Li, P., Lu, X., Zhu, G., and Shen, P. (2021). An Effective Multimodal Image Fusion Method Using MRI and PET for Alzheimer\u2019s Disease Diagnosis. Front. Digit. Health, 3.","DOI":"10.3389\/fdgth.2021.637386"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Filtjens, B., Ginis, P., Nieuwboer, A., Afzal, M.R., Spildooren, J., Vanrumste, B., and Slaets, P. (2021). Modelling and Identification of Characteristic Kinematic Features Preceding Freezing of Gait with Convolutional Neural Networks and Layer-Wise Relevance Propagation. BMC Med. Inf. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01699-0"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"102413","DOI":"10.1016\/j.media.2022.102413","article-title":"An Attention-Based Hybrid Deep Learning Framework Integrating Brain Connectivity and Activity of Resting-State Functional MRI Data","volume":"78","author":"Zhao","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"134","DOI":"10.3171\/2020.12.JNS203778","article-title":"Rescue Therapy for Vasospasm Following Aneurysmal Subarachnoid Hemorrhage: A Propensity Score-Matched Analysis with Machine Learning","volume":"136","author":"Martini","year":"2022","journal-title":"J. Neurosurg."},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"2299","DOI":"10.1161\/STROKEAHA.121.036557","article-title":"Prediction of Recurrent Ischemic Stroke Using Registry Data and Machine Learning Methods: The Erlangen Stroke Registry","volume":"53","author":"Vodencarevic","year":"2022","journal-title":"Stroke"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1002\/epi4.12655","article-title":"Interpretable Deep Learning-Based Hippocampal Sclerosis Classification","volume":"7","author":"Kim","year":"2022","journal-title":"Epilepsia Open"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1176","DOI":"10.1007\/s00259-021-05569-9","article-title":"Explainable AI to Improve Acceptance of Convolutional Neural Networks for Automatic Classification of Dopamine Transporter SPECT in the Diagnosis of Clinically Uncertain Parkinsonian Syndromes","volume":"49","author":"Nazari","year":"2022","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"1962","DOI":"10.1109\/TNSRE.2019.2940485","article-title":"Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection","volume":"27","author":"Tian","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1038\/s41551-018-0324-9","article-title":"An Explainable Deep-Learning Algorithm for the Detection of Acute Intracranial Haemorrhage from Small Datasets","volume":"3","author":"Lee","year":"2019","journal-title":"Nat. Biomed. Eng."},{"key":"ref_149","doi-asserted-by":"crossref","unstructured":"Wu, S., Venkataraman, A., and Ghosal, S. (2023, January 24\u201327). GIRUS-Net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer\u2019s Disease Severity. Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10341000"},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Jahan, S., Abu Taher, K., Kaiser, M.S., Mahmud, M., Rahman, M.S., Hosen, A.S.M.S., and Ra, I.-H. (2023). Explainable AI-Based Alzheimer\u2019s Prediction and Management Using Multimodal Data. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0294253"},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Kamal, M.S., Chowdhury, L., Nimmy, S.F., Rafi, T.H.H., and Chae, D.-K. (2023, January 24\u201327). An Interpretable Framework for Identifying Cerebral Microbleeds and Alzheimer\u2019s Disease Severity Using Multimodal Data. Proceedings of the 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Sydney, Australia.","DOI":"10.1109\/EMBC40787.2023.10340088"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Bordin, V., Coluzzi, D., Rivolta, M.W., and Baselli, G. (2022, January 11\u201315). Explainable AI Points to White Matter Hyperintensities for Alzheimer\u2019s Disease Identification: A Preliminary Study. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, Scotland, UK.","DOI":"10.1109\/EMBC48229.2022.9871306"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Li, F., Chen, A., Li, Z., Gu, L., Pan, Q., Wang, P., Fan, Y., and Feng, J. (2023). Machine Learning-Based Prediction of Cerebral Hemorrhage in Patients with Hemodialysis: A Multicenter, Retrospective Study. Front. Neurol., 14.","DOI":"10.3389\/fneur.2023.1139096"},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Tasci, E., Jagasia, S., Zhuge, Y., Sproull, M., Cooley Zgela, T., Mackey, M., Camphausen, K., and Krauze, A.V. (2023). RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers, 15.","DOI":"10.3390\/cancers15102672"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"123173","DOI":"10.1109\/ACCESS.2023.3328331","article-title":"Explainable Artificial Intelligence of Multi-Level Stacking Ensemble for Detection of Alzheimer\u2019s Disease Based on Particle Swarm Optimization and the Sub-Scores of Cognitive Biomarkers","volume":"11","author":"Almohimeed","year":"2023","journal-title":"IEEE Access"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"114459","DOI":"10.1109\/ACCESS.2023.3324987","article-title":"Bispectral Analysis of Parkinsonian Rest Tremor: New Characterization and Classification Insights Pre-\/Post-DBS and Medication Treatment","volume":"11","author":"Ziogas","year":"2023","journal-title":"IEEE Access"},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Amoroso, N., Quarto, S., La Rocca, M., Tangaro, S., Monaco, A., and Bellotti, R. (2023). An eXplainability Artificial Intelligence Approach to Brain Connectivity in Alzheimer\u2019s Disease. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1238065"},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Lai, Y., Lin, P., Lin, F., Chen, M., Lin, C., Lin, X., Wu, L., Zheng, M., and Chen, J. (2022). Identification of Immune Microenvironment Subtypes and Signature Genes for Alzheimer\u2019s Disease Diagnosis and Risk Prediction Based on Explainable Machine Learning. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.1046410"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Xu, Y., Sun, X., Liu, Y., Huang, Y., Liang, M., Sun, R., Yin, G., Song, C., Ding, Q., and Du, B. (2023). Prediction of Subjective Cognitive Decline after Corpus Callosum Infarction by an Interpretable Machine Learning-Derived Early Warning Strategy. Front. Neurol., 14.","DOI":"10.3389\/fneur.2023.1123607"},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Olatunji, S.O., Alsheikh, N., Alnajrani, L., Alanazy, A., Almusairii, M., Alshammasi, S., Alansari, A., Zaghdoud, R., Alahmadi, A., and Basheer Ahmed, M.I. (2023). Comprehensible Machine-Learning-Based Models for the Pre-Emptive Diagnosis of Multiple Sclerosis Using Clinical Data: A Retrospective Study in the Eastern Province of Saudi Arabia. Int. J. Environ Res. Public. Health, 20.","DOI":"10.3390\/ijerph20054261"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s11011-023-01322-3","article-title":"Genomic Insights and Advanced Machine Learning: Characterizing Autism Spectrum Disorder Biomarkers and Genetic Interactions","volume":"39","author":"Nahas","year":"2024","journal-title":"Metab. Brain Dis."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"101984","DOI":"10.1016\/j.artmed.2020.101984","article-title":"Cartesian Genetic Programming for Diagnosis of Parkinson Disease through Handwriting Analysis: Performance vs. Interpretability Issues","volume":"111","author":"Parziale","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Junaid, M., Ali, S., Eid, F., El-Sappagh, S., and Abuhmed, T. (2023). Explainable Machine Learning Models Based on Multimodal Time-Series Data for the Early Detection of Parkinson\u2019s Disease. Comput. Methods Programs Biomed., 234.","DOI":"10.1016\/j.cmpb.2023.107495"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"1472","DOI":"10.1109\/JBHI.2021.3073056","article-title":"A Heterogeneous Ensemble Learning Method For Neuroblastoma Survival Prediction","volume":"26","author":"Feng","year":"2022","journal-title":"IEEE J. Biomed. Health Inf."},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Jabal, M.S., Joly, O., Kallmes, D., Harston, G., Rabinstein, A., Huynh, T., and Brinjikji, W. (2022). Interpretable Machine Learning Modeling for Ischemic Stroke Outcome Prediction. Front. Neurol., 13.","DOI":"10.3389\/fneur.2022.884693"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"102051","DOI":"10.1016\/j.eclinm.2023.102051","article-title":"Development and Validation of an Artificial Intelligence Model for the Early Classification of the Aetiology of Meningitis and Encephalitis: A Retrospective Observational Study","volume":"61","author":"Choi","year":"2023","journal-title":"EClinicalMedicine"},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"e311","DOI":"10.1016\/j.cllc.2023.08.002","article-title":"Machine-Learning-Aided Prediction of Brain Metastases Development in Non-Small-Cell Lung Cancers","volume":"24","author":"Spiller","year":"2023","journal-title":"Clin. Lung Cancer"},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Yi, F., Yang, H., Chen, D., Qin, Y., Han, H., Cui, J., Bai, W., Ma, Y., Zhang, R., and Yu, H. (2023). XGBoost-SHAP-Based Interpretable Diagnostic Framework for Alzheimer\u2019s Disease. BMC Med. Inf. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02238-9"},{"key":"ref_169","first-page":"3531","article-title":"Alzheimer\u2019s Disease Diagnosis Based on a Semantic Rule-Based Modeling and Reasoning Approach","volume":"69","author":"Shoaip","year":"2021","journal-title":"Comput. Mater. Contin."},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Jean-Quartier, C., Jeanquartier, F., Ridvan, A., Kargl, M., Mirza, T., Stangl, T., Marka\u0109, R., Jurada, M., and Holzinger, A. (2021). Mutation-Based Clustering and Classification Analysis Reveals Distinctive Age Groups and Age-Related Biomarkers for Glioma. BMC Med. Inf. Decis. Mak., 21.","DOI":"10.1186\/s12911-021-01420-1"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Wehenkel, M., Sutera, A., Bastin, C., Geurts, P., and Phillips, C. (2018). Random Forests Based Group Importance Scores and Their Statistical Interpretation: Application for Alzheimer\u2019s Disease. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00411"},{"key":"ref_172","doi-asserted-by":"crossref","unstructured":"Bohn, L., Drouin, S.M., McFall, G.P., Rolfson, D.B., Andrew, M.K., and Dixon, R.A. (2023). Machine Learning Analyses Identify Multi-Modal Frailty Factors That Selectively Discriminate Four Cohorts in the Alzheimer\u2019s Disease Spectrum: A COMPASS-ND Study. BMC Geriatr., 23.","DOI":"10.1186\/s12877-023-04546-1"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s10020-023-00603-y","article-title":"Gene Targeting in Amyotrophic Lateral Sclerosis Using Causality-Based Feature Selection and Machine Learning","volume":"29","author":"Founta","year":"2023","journal-title":"Mol. Med."},{"key":"ref_174","doi-asserted-by":"crossref","unstructured":"El-Sappagh, S., Alonso, J.M., Islam, S.M.R., Sultan, A.M., and Kwak, K.S. (2021). A Multilayer Multimodal Detection and Prediction Model Based on Explainable Artificial Intelligence for Alzheimer\u2019s Disease. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-82098-3"},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s13195-021-00879-4","article-title":"Alzheimer\u2019s Disease Neuroimaging Initiative Data Analysis with Shapley Values for Automatic Subject Selection in Alzheimer\u2019s Disease Data Sets Using Interpretable Machine Learning","volume":"13","author":"Bloch","year":"2021","journal-title":"Alzheimers Res. Ther."},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Rupprechter, S., Morinan, G., Peng, Y., Foltynie, T., Sibley, K., Weil, R.S., Leyland, L.-A., Baig, F., Morgante, F., and Gilron, R. (2021). A Clinically Interpretable Computer-Vision Based Method for Quantifying Gait in Parkinson\u2019s Disease. Sensors, 21.","DOI":"10.3390\/s21165437"},{"key":"ref_177","doi-asserted-by":"crossref","unstructured":"Vik, A., Koci\u0144ski, M., Rye, I., Lundervold, A.J., and Lundervold, A.S. (2023). Functional Activity Level Reported by an Informant Is an Early Predictor of Alzheimer\u2019s Disease. BMC Geriatr., 23.","DOI":"10.1186\/s12877-023-03849-7"},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.jocn.2021.07.028","article-title":"Analysis of Risk Factors Correlated with Angiographic Vasospasm in Patients with Aneurysmal Subarachnoid Hemorrhage Using Explainable Predictive Modeling","volume":"91","author":"Kim","year":"2021","journal-title":"J. Clin. Neurosci."},{"key":"ref_179","doi-asserted-by":"crossref","unstructured":"Islam, M.S., Hussain, I., Rahman, M.M., Park, S.J., and Hossain, M.A. (2022). Explainable Artificial Intelligence Model for Stroke Prediction Using EEG Signal. Sensors, 22.","DOI":"10.3390\/s22249859"},{"key":"ref_180","doi-asserted-by":"crossref","unstructured":"Conti, A., Treaba, C.A., Mehndiratta, A., Barletta, V.T., Mainero, C., and Toschi, N. (2023). An Interpretable Machine Learning Model to Predict Cortical Atrophy in Multiple Sclerosis. Brain Sci., 13.","DOI":"10.3390\/brainsci13020198"},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1089\/brain.2020.1003","article-title":"Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity","volume":"12","author":"Barile","year":"2022","journal-title":"Brain Connect."},{"key":"ref_182","doi-asserted-by":"crossref","unstructured":"Prado, P., Moguilner, S., Mej\u00eda, J.A., Sainz-Ballesteros, A., Otero, M., Birba, A., Santamaria-Garcia, H., Legaz, A., Fittipaldi, S., and Cruzat, J. (2023). Source Space Connectomics of Neurodegeneration: One-Metric Approach Does Not Fit All. Neurobiol. Dis., 179.","DOI":"10.1016\/j.nbd.2023.106047"},{"key":"ref_183","doi-asserted-by":"crossref","unstructured":"Leandrou, S., Lamnisos, D., Bougias, H., Stogiannos, N., Georgiadou, E., Achilleos, K.G., and Pattichis, C.S. (2023). A Cross-Sectional Study of Explainable Machine Learning in Alzheimer\u2019s Disease: Diagnostic Classification Using MR Radiomic Features. Front. Aging Neurosci., 15.","DOI":"10.3389\/fnagi.2023.1149871"},{"key":"ref_184","doi-asserted-by":"crossref","unstructured":"Gao, X.R., Chiariglione, M., Qin, K., Nuytemans, K., Scharre, D.W., Li, Y.-J., and Martin, E.R. (2023). Explainable Machine Learning Aggregates Polygenic Risk Scores and Electronic Health Records for Alzheimer\u2019s Disease Prediction. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-27551-1"},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1002\/psp4.12796","article-title":"Predicting Disease Activity in Patients with Multiple Sclerosis: An Explainable Machine-Learning Approach in the Mavenclad Trials","volume":"11","author":"Basu","year":"2022","journal-title":"CPT Pharmacomet. Syst. Pharmacol."},{"key":"ref_186","first-page":"e12276","article-title":"Automated Text-Level Semantic Markers of Alzheimer\u2019s Disease","volume":"14","author":"Sanz","year":"2022","journal-title":"Alzheimers Dement."},{"key":"ref_187","doi-asserted-by":"crossref","unstructured":"Bhandari, N., Walambe, R., Kotecha, K., and Kaliya, M. (2023). Integrative Gene Expression Analysis for the Diagnosis of Parkinson\u2019s Disease Using Machine Learning and Explainable AI. Comput. Biol. Med., 163.","DOI":"10.1016\/j.compbiomed.2023.107140"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"101986","DOI":"10.1016\/j.media.2021.101986","article-title":"Combining Anatomical and Functional Networks for Neuropathology Identification: A Case Study on Autism Spectrum Disorder","volume":"69","author":"Itani","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_189","doi-asserted-by":"crossref","unstructured":"Kappelhof, N., Ramos, L.A., Kappelhof, M., van Os, H.J.A., Chalos, V., van Kranendonk, K.R., Kruyt, N.D., Roos, Y.B.W.E.M., van Zwam, W.H., and van der Schaaf, I.C. (2021). Evolutionary Algorithms and Decision Trees for Predicting Poor Outcome after Endovascular Treatment for Acute Ischemic Stroke. Comput. Biol. Med., 133.","DOI":"10.1016\/j.compbiomed.2021.104414"},{"key":"ref_190","doi-asserted-by":"crossref","unstructured":"Shah, S.A., Brown, P., Gimeno, H., Lin, J.-P., and McClelland, V.M. (2020). Application of Machine Learning Using Decision Trees for Prognosis of Deep Brain Stimulation of Globus Pallidus Internus for Children With Dystonia. Front. Neurol., 11.","DOI":"10.3389\/fneur.2020.00825"},{"key":"ref_191","doi-asserted-by":"crossref","unstructured":"Chaddad, A., Peng, J., Xu, J., and Bouridane, A. (2023). Survey of Explainable AI Techniques in Healthcare. Sensors, 23.","DOI":"10.3390\/s23020634"},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"100230","DOI":"10.1016\/j.dajour.2023.100230","article-title":"A Systematic Review of Explainable Artificial Intelligence Models and Applications: Recent Developments and Future Trends","volume":"7","author":"Saranya","year":"2023","journal-title":"Decis. Anal. J."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"111507","DOI":"10.1016\/j.ejrad.2024.111507","article-title":"The Crucial Role of Explainability in Healthcare AI","volume":"176","author":"Beger","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1001\/jama.2023.25057","article-title":"Three Epochs of Artificial Intelligence in Health Care","volume":"331","author":"Howell","year":"2024","journal-title":"JAMA"},{"key":"ref_195","doi-asserted-by":"crossref","unstructured":"Rao, S., Mehta, S., Kulkarni, S., Dalvi, H., Katre, N., and Narvekar, M. (2022, January 8\u201310). A Study of LIME and SHAP Model Explainers for Autonomous Disease Predictions. Proceedings of the 2022 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India.","DOI":"10.1109\/IBSSC56953.2022.10037324"},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"523","DOI":"10.4300\/JGME-D-14-00638.1","article-title":"The Diagnosis, Prognosis, and Treatment of Medical Uncertainty","volume":"7","author":"Wray","year":"2015","journal-title":"J. Grad. Med. Educ."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"652","DOI":"10.3390\/ai4030034","article-title":"Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare","volume":"4","author":"Hulsen","year":"2023","journal-title":"AI"},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"101286","DOI":"10.1016\/j.imu.2023.101286","article-title":"Application of Explainable Artificial Intelligence in Medical Health: A Systematic Review of Interpretability Methods","volume":"40","author":"Band","year":"2023","journal-title":"Inform. Med. Unlocked"},{"key":"ref_199","doi-asserted-by":"crossref","first-page":"37622","DOI":"10.1109\/ACCESS.2021.3062484","article-title":"Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances","volume":"9","author":"Khan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_200","doi-asserted-by":"crossref","first-page":"109370","DOI":"10.1016\/j.compeleceng.2024.109370","article-title":"A Review of Explainable Artificial Intelligence in Healthcare","volume":"118","author":"Sadeghi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"e618","DOI":"10.1016\/S2589-7500(23)00126-7","article-title":"FDA-Cleared Artificial Intelligence and Machine Learning-Based Medical Devices and Their 510(k) Predicate Networks","volume":"5","author":"Muehlematter","year":"2023","journal-title":"Lancet Digit. Health"}],"container-title":["Neurology International"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2035-8377\/16\/6\/98\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:23:17Z","timestamp":1760113397000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2035-8377\/16\/6\/98"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,29]]},"references-count":201,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["neurolint16060098"],"URL":"https:\/\/doi.org\/10.3390\/neurolint16060098","relation":{},"ISSN":["2035-8377"],"issn-type":[{"value":"2035-8377","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,29]]}}}