{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T22:15:46Z","timestamp":1790115346354,"version":"4.0.1"},"reference-count":88,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:00:00Z","timestamp":1788566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>The clinical integration of artificial intelligence has outpaced the development of robust evaluative frameworks, raising critical safety concerns. This perspective establishes a clear taxonomy distinguishing probabilistic language models from deterministic classifiers and applies a multi-dimensional combinatorial model to calculate the requirements for complete diagnostic coverage. Our analysis demonstrates that comprehensive diagnostic coverage requires between 50,000 and 150,000 distinct, task-specific classifiers under subspecialty-level clinical granularity; conservative aggregated estimates (4500\u201318,750 binary classifiers) do not reflect the multiplicative expansion introduced by subtype differentiation, severity staging, temporal variants, demographic stratification, and equipment variation, whereas currently cleared devices cover less than one percent of this clinical space. More fundamentally, although population-trained models can generate conditional patient-specific risk estimates when predictors are informative and calibration is adequate, these statistical parameters optimized on population-scale data cannot provide the categorical certainty required for individual diagnostic decisions, which is a gap that clinical judgment must bridge. Because clinical AI tools are inherently statistical and perform reliably only on common, highly represented presentations while failing on rare, atypical cases rare in their training data, attempting to automate routine tasks leaves human clinicians with only the most challenging diagnostics. Furthermore, selective automation of these low-complexity cases introduces severe occupational hazards, including cognitive surrender, diagnostic complacency, and rapid expertise atrophy. Rather than pursuing the computationally and logistically unfeasible goal of complete diagnostic classification, developers should prioritize predictive, prognostic trajectory modeling. This paradigm shift aligns the probabilistic nature of machine learning with clinical utility, reinforcing clinical judgment as the irreplaceable diagnostic integrator.<\/jats:p>","DOI":"10.3390\/bioengineering13091034","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T08:07:06Z","timestamp":1788768426000},"page":"1034","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Perspectives on the Limits and Clinical Alignment of Medical AI from Population Statistics to Individual Care"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2154-3728","authenticated-orcid":false,"given":"Milan","family":"Toma","sequence":"first","affiliation":[{"name":"Laboratory of Algorithmic Medicine, Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5304-0372","authenticated-orcid":false,"given":"David","family":"Yusupov","sequence":"additional","affiliation":[{"name":"Laboratory of Algorithmic Medicine, Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury, NY 11568, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1038\/s41551-022-00898-y","article-title":"Shifting machine learning for healthcare from development to deployment and from models to data","volume":"6","author":"Zhang","year":"2022","journal-title":"Nat. Biomed. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1038\/s41591-019-0548-6","article-title":"Do no harm: A roadmap for responsible machine learning for health care","volume":"25","author":"Wiens","year":"2019","journal-title":"Nat. Med."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1038\/s41746-021-00549-7","article-title":"Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: A scoping review","volume":"5","author":"Leeuwenberg","year":"2022","journal-title":"npj Digit. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1038\/s41746-025-01725-9","article-title":"Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap","volume":"8","author":"Sokol","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1038\/s41591-023-02448-8","article-title":"Large language models in medicine","volume":"29","author":"Thirunavukarasu","year":"2023","journal-title":"Nat. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1038\/s41586-023-06291-2","article-title":"Large language models encode clinical knowledge","volume":"620","author":"Singhal","year":"2023","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bender, E.M., Gebru, T., McMillan-Major, A., and Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, ACM.","DOI":"10.1145\/3442188.3445922"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1056\/NEJMsr2214184","article-title":"Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine","volume":"388","author":"Lee","year":"2023","journal-title":"N. Engl. J. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"20250014","DOI":"10.1098\/rsta.2025.0014","article-title":"Large language models and emergence: A complex systems perspective","volume":"384","author":"Krakauer","year":"2026","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1038\/s41746-025-01670-7","article-title":"A framework to assess clinical safety and hallucination rates of LLMs for medical text summarisation","volume":"8","author":"Asgari","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41586-025-08869-4","article-title":"Towards accurate differential diagnosis with large language models","volume":"642","author":"McDuff","year":"2025","journal-title":"Nature"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1038\/s41586-025-08866-7","article-title":"Towards conversational diagnostic artificial intelligence","volume":"642","author":"Tu","year":"2025","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1038\/s41591-024-03416-6","article-title":"A generalist medical language model for disease diagnosis assistance","volume":"31","author":"Liu","year":"2025","journal-title":"Nat. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e428","DOI":"10.1016\/S2589-7500(24)00061-X","article-title":"Ethical and regulatory challenges of large language models in medicine","volume":"6","author":"Ong","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"e2440969","DOI":"10.1001\/jamanetworkopen.2024.40969","article-title":"Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial","volume":"7","author":"Goh","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e264003","DOI":"10.1001\/jamanetworkopen.2026.4003","article-title":"Large Language Model Performance and Clinical Reasoning Tasks","volume":"9","author":"Rao","year":"2026","journal-title":"JAMA Netw. Open"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e2526021","DOI":"10.1001\/jamanetworkopen.2025.26021","article-title":"Fidelity of Medical Reasoning in Large Language Models","volume":"8","author":"Bedi","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1038\/s41591-026-04229-5","article-title":"LLM-assisted systematic review of large language models in clinical medicine","volume":"32","author":"Chen","year":"2026","journal-title":"Nat. Med."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hong, S., Matalia, M., and Toma, M. (2026). Chatting Ain\u2019t Diagnosing: Diagnostic Variability and Fundamental Errors in Multimodal LLM Interpretation in Radiology. Algorithms, 19.","DOI":"10.3390\/a19030170"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e71446","DOI":"10.1002\/hsr2.71446","article-title":"Evaluation of Machine Learning Methods Developed for Prediction and Diagnosis of Pneumonia: A Systematic Review","volume":"8","author":"Kheirdoust","year":"2025","journal-title":"Health Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Patwary, R., and Toma, M. (2026). Bidirectional Domain Shift Failure in Radiograph Fracture Classification Across Out-of-Distribution Pediatric and Mixed-Age Multi-Region Data. Appl. Sci., 16.","DOI":"10.3390\/app16146853"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sher, M., Remyes, D., Sharma, R., and Toma, M. (2025). Learning Dynamics Analysis: Assessing Generalization of Machine Learning Models for Optical Coherence Tomography Multiclass Classification. Informatics, 12.","DOI":"10.3390\/informatics12040128"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shah, A., and Toma, M. (2026). Channel reduction in AdaBoost ensemble models for energy-efficient ambulatory seizure detection. Glob. Transl. Med., 026220022.","DOI":"10.36922\/GTM026220022"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e271","DOI":"10.1016\/S2589-7500(19)30123-2","article-title":"A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: A systematic review and meta-analysis","volume":"1","author":"Liu","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"m689","DOI":"10.1136\/bmj.m689","article-title":"Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"Nagendran","year":"2020","journal-title":"BMJ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","article-title":"Diagnostic accuracy of deep learning in medical imaging: A systematic review and meta-analysis","volume":"4","author":"Aggarwal","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Starcke, J., Spadafora, J., Spadafora, J., Spadafora, P., and Toma, M. (2025). The Effect of Data Leakage and Feature Selection on Machine Learning Performance for Early Parkinson\u2019s Disease Detection. Bioengineering, 12.","DOI":"10.3390\/bioengineering12080845"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Toma, M. (2026). From Interpretable Models to Clinical Implementation: Advances in AI-Assisted Medical Diagnostics. Algorithms, 19.","DOI":"10.3390\/a19050330"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1214\/21-SS133","article-title":"Interpretable machine learning: Fundamental principles and 10 grand challenges","volume":"16","author":"Rudin","year":"2022","journal-title":"Stat. Surv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Daovisan, H., Suwanwong, C., Prasittichok, P., Prayai, N., and Choowan, P. (2025). Reclaiming XAI as an Innovation in Healthcare: Bridging Rule-Based Systems. Algorithms, 18.","DOI":"10.3390\/a18090586"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"3283","DOI":"10.1038\/s41591-025-03953-8","article-title":"The STARD-AI reporting guideline for diagnostic accuracy studies using artificial intelligence","volume":"31","author":"Sounderajah","year":"2025","journal-title":"Nat. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e240300","DOI":"10.1148\/ryai.240300","article-title":"Checklist for Artificial Intelligence in Medical Imaging (CLAIM): 2024 Update","volume":"6","author":"Tejani","year":"2024","journal-title":"Radiol. Artif. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"\u0142o\u015b, A., Bartusik-Aebisher, D., Mytych, W., and Aebisher, D. (2026). Applications of Artificial Intelligence in Selected Internal Medicine Specialties: A Critical Narrative Review of the Latest Clinical Evidence. Algorithms, 19.","DOI":"10.3390\/a19010054"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Almarie, B., Gonzalez-Gonzalez, L.F., dos Santos Barbosa, L.A., Lutz, A., Grosse, U., and Fregni, F. (2025). Machine Learning-Enabled Medical Devices Authorized by the US Food and Drug Administration in 2024: Regulatory Characteristics, Predicate Lineage, and Transparency Reporting. Biomedicines, 13.","DOI":"10.3390\/biomedicines13123005"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hettikankanamage, N., Shafiabady, N., Chatteur, F., Wu, R.M.X., Ud Din, F., and Zhou, J. (2025). eXplainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing. Sensors, 25.","DOI":"10.3390\/s25216649"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"e100971","DOI":"10.1136\/bmjhci-2023-100971","article-title":"Achieving large-scale clinician adoption of AI-enabled decision support","volume":"31","author":"Scott","year":"2024","journal-title":"BMJ Health Care Inform."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zech, J.R., Badgeley, M.A., Liu, M., Costa, A.B., Titano, J.J., and Oermann, E.K. (2018). Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study. PLoS Med., 15.","DOI":"10.1371\/journal.pmed.1002683"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1038\/s42256-021-00338-7","article-title":"AI for radiographic COVID-19 detection selects shortcuts over signal","volume":"3","author":"DeGrave","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e210064","DOI":"10.1148\/ryai.210064","article-title":"External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review","volume":"4","author":"Yu","year":"2022","journal-title":"Radiol. Artif. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2176","DOI":"10.1038\/s41591-021-01595-0","article-title":"Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations","volume":"27","author":"Zhang","year":"2021","journal-title":"Nat. Med."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41746-019-0105-1","article-title":"Deep learning predicts hip fracture using confounding patient and healthcare variables","volume":"2","author":"Badgeley","year":"2019","journal-title":"npj Digit. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.1038\/s41591-024-03113-4","article-title":"The limits of fair medical imaging AI in real-world generalization","volume":"30","author":"Yang","year":"2024","journal-title":"Nat. Med."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"528","DOI":"10.3934\/mbe.2025020","article-title":"Uncertainty CNNs: A path to enhanced medical image classification performance","volume":"22","author":"Papageorgiou","year":"2025","journal-title":"Math. Biosci. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Van Calster, B., McLernon, D.J., van Smeden, M., Wynants, L., and Steyerberg, E.W. (2019). Calibration: The Achilles heel of predictive analytics. BMC Med., 17.","DOI":"10.1186\/s12916-019-1466-7"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1038\/s41746-020-00367-3","article-title":"Second opinion needed: Communicating uncertainty in medical machine learning","volume":"4","author":"Kompa","year":"2021","journal-title":"npj Digit. Med."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","article-title":"Human\u2013computer collaboration for skin cancer recognition","volume":"26","author":"Tschandl","year":"2020","journal-title":"Nat. Med."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Beede, E., Baylor, E., Hersch, F., Iurchenko, A., Wilcox, L., Ruamviboonsuk, P., and Vardoulakis, L.M. (2020). A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, ACM.","DOI":"10.1145\/3313831.3376718"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"2275","DOI":"10.1001\/jama.2023.22295","article-title":"Measuring the Impact of AI in the Diagnosis of Hospitalized Patients: A Randomized Clinical Vignette Survey Study","volume":"330","author":"Jabbour","year":"2023","journal-title":"JAMA"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1001\/jama.2023.22557","article-title":"Automation Bias and Assistive AI: Risk of Harm From AI-Driven Clinical Decision Support","volume":"330","author":"Khera","year":"2023","journal-title":"JAMA"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Lei, S., Zhou, G., Berzin, T.M., Xiao, X., Liu, P., and Wang, P. (2026). Advanced computer-aided detection system exhibits no more false positives than experienced endoscopists in an image-based comparative study of colon polyps. Front. Med., 13.","DOI":"10.3389\/fmed.2026.1793448"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Shaw, S.D., and Nave, G. (2026). Thinking\u2014Fast, Slow, and Artificial: How AI Is Reshaping Human Reasoning and the Rise of Cognitive Surrender, Social Science Research Network (SSRN). Working Paper 6097646.","DOI":"10.2139\/ssrn.6097646"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"101102","DOI":"10.1016\/j.chbr.2026.101102","article-title":"AI-overdependence and human cognitive decline: Hazards, evidence, and mitigation strategies","volume":"22","author":"Noorbehbahani","year":"2026","journal-title":"Comput. Hum. Behav. Rep."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1038\/s41746-026-02547-z","article-title":"Impact of AI misinformation on diagnostic accuracy and confidence calibration in novice medical students","volume":"9","author":"Teng","year":"2026","journal-title":"npj Digit. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1038\/s41746-026-02873-2","article-title":"Reconciling how clinical reasoning is learned in the age of artificial intelligence","volume":"9","author":"Ong","year":"2026","journal-title":"npj Digit. Med."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1038\/s41746-026-02521-9","article-title":"AI literacy mediates AI assisted diagnosis participation and critical thinking among medical students under supervision","volume":"9","author":"Xin","year":"2026","journal-title":"npj Digit. Med."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/s41542-026-00262-5","article-title":"Aligning Artificial Intelligence with Worker Health, Well-Being, and Safety: An Occupational Health Psychology Agenda","volume":"10","author":"Probst","year":"2026","journal-title":"Occup. Health Sci."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"El Tarhouny, S., and Farghaly, A. (2026). Deskilling dilemma: Brain over automation. Front. Med., 13.","DOI":"10.3389\/fmed.2026.1765692"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1007\/s10462-025-11352-1","article-title":"AI-induced Deskilling in Medicine: A Mixed-Method Review and Research Agenda for Healthcare and Beyond","volume":"58","author":"Natali","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"524","DOI":"10.1126\/science.adz4433","article-title":"Performance of a large language model on the reasoning tasks of a physician","volume":"392","author":"Brodeur","year":"2026","journal-title":"Science"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Emanuel, E.J., Baker-Butler, A., Khosla, N., and Khosla, V. (2026). Will Autonomous AI Exceed AI-Aided Physicians as the Best Medical Care?. JAMA.","DOI":"10.1001\/jama.2026.15380"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1282","DOI":"10.1038\/s41586-026-10675-5","article-title":"Towards autonomous medical artificial intelligence agents","volume":"655","author":"Ferber","year":"2026","journal-title":"Nature"},{"key":"ref_64","unstructured":"Centers for Medicare & Medicaid Services (2026, June 01). ICD-10-CM Diagnosis Code Files and Official Coding Resources, Available online: https:\/\/www.cms.gov\/medicare\/coding-billing\/icd-10-codes."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Papageorgiou, V.E., Petmezas, G., Papageorgiou, D.P., Stefanopoulos, L., and Maglaveras, N. (2026). Multi-cancer detection using a computationally efficient CNN with transfer learning. Commun. Stat.-Simul. Comput., 1\u201320.","DOI":"10.1080\/03610918.2026.2680237"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1038\/s41746-025-01800-1","article-title":"How AI is used in FDA-authorized medical devices: A taxonomy across 1016 authorizations","volume":"8","author":"Singh","year":"2025","journal-title":"npj Digit. Med."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41746-024-01270-x","article-title":"A scoping review of reporting gaps in FDA-approved AI medical devices","volume":"7","author":"Muralidharan","year":"2024","journal-title":"npj Digit. Med."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e258052","DOI":"10.1001\/jamanetworkopen.2025.8052","article-title":"Generalizability of FDA-Approved AI-Enabled Medical Devices for Clinical Use","volume":"8","author":"Windecker","year":"2025","journal-title":"JAMA Netw. Open"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"W1","DOI":"10.7326\/M18-1377","article-title":"PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration","volume":"170","author":"Moons","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1038\/s41591-020-1034-x","article-title":"Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1038\/s41591-020-1034-x","article-title":"Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension","volume":"26","author":"Liu","year":"2020","journal-title":"Nat. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"924","DOI":"10.1038\/s41591-022-01772-9","article-title":"Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI","volume":"28","author":"Vasey","year":"2022","journal-title":"Nat. Med."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1320","DOI":"10.1038\/s41591-020-1041-y","article-title":"Minimum information about clinical artificial intelligence modeling: The MI-CLAIM checklist","volume":"26","author":"Norgeot","year":"2020","journal-title":"Nat. Med."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e081554","DOI":"10.1136\/bmj-2024-081554","article-title":"FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare","volume":"388","author":"Lekadir","year":"2025","journal-title":"BMJ"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Kim, D.W., Jang, H.Y., Ko, Y., Son, J.H., Kim, P.H., Kim, S.O., Lim, J.S., and Park, S.H. (2020). Inconsistency in the use of the term \u201cvalidation\u201d in studies reporting the performance of deep learning algorithms in providing diagnosis from medical imaging. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0238908"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/s41746-018-0029-1","article-title":"Scalable and accurate deep learning with electronic health records","volume":"1","author":"Rajkomar","year":"2018","journal-title":"npj Digit. Med."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1038\/s41586-019-1390-1","article-title":"A clinically applicable approach to continuous prediction of future acute kidney injury","volume":"572","author":"Glorot","year":"2019","journal-title":"Nature"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1038\/s41591-023-02332-5","article-title":"A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories","volume":"29","author":"Placido","year":"2023","journal-title":"Nat. Med."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.jclinepi.2021.01.009","article-title":"Clinical prediction models: Diagnosis versus prognosis","volume":"132","author":"Reitsma","year":"2021","journal-title":"J. Clin. Epidemiol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"e074819","DOI":"10.1136\/bmj-2023-074819","article-title":"Evaluation of clinical prediction models (part 1): From development to external validation","volume":"384","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"e074820","DOI":"10.1136\/bmj-2023-074820","article-title":"Evaluation of clinical prediction models (part 2): How to undertake an external validation study","volume":"384","author":"Riley","year":"2024","journal-title":"BMJ"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1177\/0272989X06295361","article-title":"Decision Curve Analysis: A Novel Method for Evaluating Prediction Models","volume":"26","author":"Vickers","year":"2006","journal-title":"Med. Decis. Mak."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1126\/science.aax2342","article-title":"Dissecting racial bias in an algorithm used to manage the health of populations","volume":"366","author":"Obermeyer","year":"2019","journal-title":"Science"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"12592","DOI":"10.1073\/pnas.1919012117","article-title":"Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis","volume":"117","author":"Larrazabal","year":"2020","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","article-title":"Algorithmic fairness in artificial intelligence for medicine and healthcare","volume":"7","author":"Chen","year":"2023","journal-title":"Nat. Biomed. Eng."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"e64","DOI":"10.1016\/S2589-7500(24)00224-3","article-title":"Tackling algorithmic bias and promoting transparency in health datasets: The STANDING Together consensus recommendations","volume":"7","author":"Alderman","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1038\/s41746-022-00663-0","article-title":"A framework for digital health equity","volume":"5","author":"Richardson","year":"2022","journal-title":"npj Digit. Med."}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1034\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T08:51:38Z","timestamp":1788771098000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/9\/1034"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,5]]},"references-count":88,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["bioengineering13091034"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13091034","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,5]]}}}