{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T12:13:06Z","timestamp":1783599186720,"version":"3.55.0"},"reference-count":66,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosdigitalhealth.org"],"crossmark-restriction":false},"short-container-title":["PLOS Digit Health"],"abstract":"<jats:p>\n                    Medical artificial intelligence (AI) is being rapidly deployed in clinical practice, yet its real-world effectiveness across diverse patient populations remains poorly characterized. We conducted a systematic review combining automated screening (fine-tuned BERT-PubMed classifiers) with manual validation to identify studies of mature medical AI models deployed in healthcare facilities worldwide. We included 171 studies at the \u201cdevice-into-practice\u201d stage with sufficient demographic and performance data, representing 209,772 patients. Patient access to these models showed marked demographic disparities: geographic concentration was extreme (Dagum\u2013Gini coefficient 0.97,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001), with 95.1% of patient cohorts (studies) from high-income (62.2%) or upper-middle-income (32.9%) countries\u2014primarily China (28.7%) and the United States (18.9%)\u2014and no studies from low-income countries. Racial representation was dominated by White (49.1%) and Asian (42.6%) patients, and 63.8% of studies exhibited moderate-to-high sex imbalance. Across all studies, AI models outperformed human practitioners (81.7% vs. 77.8% accuracy,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001), but this superiority was confined to in-distribution applications (same geographic\/demographic context: 82.9% vs. 77.3%,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009&lt;\u2009.001) and disappeared in out-of-distribution deployments (cross-geographic\/demographic contexts: 74.1% vs. 76.3%,\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u2009.45). In underrepresented populations, AI performance was not significantly different from that of human practitioners. Overall, mature medical AI models are deployed predominantly in economically advantaged settings, with performance advantages concentrated in well-represented demographic groups, highlighting a digital divide in access and effectiveness, and the need for demographic-specific validation.\n                  <\/jats:p>","DOI":"10.1371\/journal.pdig.0001283","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T17:31:42Z","timestamp":1774027902000},"page":"e0001283","update-policy":"https:\/\/doi.org\/10.1371\/journal.pdig.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Characterizing patients who benefit from mature medical AI models in real-world clinical applications"],"prefix":"10.1371","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1744-4647","authenticated-orcid":true,"given":"Zhiyi","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6864-6559","authenticated-orcid":true,"given":"Zhicheng","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"pdig.0001283.ref001","article-title":"Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review","volume":"375","author":"CL Andaur Navarro","year":"2021","journal-title":"BMJ"},{"issue":"12","key":"pdig.0001283.ref002","article-title":"Time to reality check the promises of machine learning-powered precision medicine","volume":"2","author":"J Wilkinson","year":"2020","journal-title":"Lancet Digit Health"},{"issue":"3","key":"pdig.0001283.ref003","article-title":"Sources of bias in artificial intelligence that perpetuate healthcare disparities-A global review","volume":"1","author":"LA Celi","year":"2022","journal-title":"PLOS Digit Health"},{"issue":"3","key":"pdig.0001283.ref004","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2023.1671","article-title":"Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review","volume":"6","author":"Z Chen","year":"2023","journal-title":"JAMA Netw Open"},{"issue":"4","key":"pdig.0001283.ref005","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1038\/s41591-021-01312-x","article-title":"How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals","volume":"27","author":"E Wu","year":"2021","journal-title":"Nat Med"},{"issue":"1","key":"pdig.0001283.ref006","article-title":"Characterizing the clinical adoption of medical AI devices through U.S. insurance claims","volume":"1","author":"K Wu","year":"2024","journal-title":"NEJM AI"},{"issue":"1","key":"pdig.0001283.ref007","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/s41746-024-01212-7","article-title":"Disparities in clinical studies of AI enabled applications from a global perspective","volume":"7","author":"R Yang","year":"2024","journal-title":"NPJ Digit Med"},{"key":"pdig.0001283.ref008","doi-asserted-by":"crossref","first-page":"100067","DOI":"10.1016\/j.ibmed.2022.100067","article-title":"The challenges of health inequities and AI","volume":"6","author":"CM Moore","year":"2022","journal-title":"Intell Based Med"},{"key":"pdig.0001283.ref009","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/B978-0-323-95068-8.00009-1","article-title":"Chapter 9 - 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Adam and AdamW optimization algorithm application on BERT model for hate speech detection on Twitter. 2024 International Conference on Data Science and Its Applications (ICoDSA); 2024. p. 346\u201351. doi: 10.1109\/icodsa62899.2024.10651619","DOI":"10.1109\/ICoDSA62899.2024.10651619"},{"key":"pdig.0001283.ref017","article-title":"Cost-sensitive BERT for generalisable sentence classification with imbalanced data.","author":"HT Madabushi","year":"2020"},{"issue":"1","key":"pdig.0001283.ref018","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":"R Aggarwal","year":"2021","journal-title":"NPJ Digit Med"},{"key":"pdig.0001283.ref019","article-title":"Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies","volume":"368","author":"M Nagendran","year":"2020","journal-title":"BMJ"},{"issue":"1","key":"pdig.0001283.ref020","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s12916-023-02941-4","article-title":"Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry","volume":"21","author":"Z Chen","year":"2023","journal-title":"BMC Med"},{"issue":"7","key":"pdig.0001283.ref021","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1007\/s00134-021-06446-7","article-title":"Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit","volume":"47","author":"D van de Sande","year":"2021","journal-title":"Intensive Care Med"},{"key":"pdig.0001283.ref022","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.661","article-title":"Artificial intelligence maturity model: a systematic literature review","volume":"7","author":"RB Sadiq","year":"2021","journal-title":"PeerJ Comput 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Joshi","year":"2024","journal-title":"Electronics"},{"issue":"9","key":"pdig.0001283.ref045","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1056\/NEJMra2214964","article-title":"Considering biased data as informative artifacts in AI-assisted health care","volume":"389","author":"K Ferryman","year":"2023","journal-title":"N Engl J Med"},{"issue":"8","key":"pdig.0001283.ref046","doi-asserted-by":"crossref","DOI":"10.1056\/AIpc2400545","article-title":"The regulation of clinical artificial intelligence","volume":"1","author":"D Blumenthal","year":"2024","journal-title":"NEJM AI"},{"issue":"8","key":"pdig.0001283.ref047","doi-asserted-by":"crossref","DOI":"10.1056\/AIp2400223","article-title":"A call for artificial intelligence implementation science centers to evaluate clinical effectiveness","volume":"1","author":"CA Longhurst","year":"2024","journal-title":"NEJM 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