{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T08:00:08Z","timestamp":1790841608810,"version":"4.1.0"},"reference-count":47,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Interact J Med Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Health care systems around the world face numerous challenges. Recent advances in artificial intelligence (AI) have offered promising solutions, particularly in diagnostic imaging.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This systematic review focused on evaluating the economic feasibility of AI in real-world diagnostic imaging scenarios, specifically for dermatological, neurological, and pulmonary diseases. The central question was whether the use of AI in these diagnostic assessments improves economic outcomes and promotes equity in health care systems.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n This systematic review has 2 main components, economic evaluation and equity assessment. We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) tool to ensure adherence to best practices in systematic reviews. The protocol was registered with PROSPERO (International Prospective Register of Systematic Reviews), and we followed the PRISMA-E (Preferred Reporting Items for Systematic Reviews and Meta-Analyses - Equity Extension) guidelines for equity. Scientific articles reporting on economic evaluations or equity considerations related to the use of AI-based tools in diagnostic imaging in dermatology, neurology, or pulmonology were included in the study. The search was conducted in the PubMed, Embase, Scopus, and Web of Science databases. Methodological quality was assessed using the following checklists, CHEC (Consensus on Health Economic Criteria) for economic evaluations, EPHPP (Effective Public Health Practice Project) for equity evaluation studies, and Welte for transferability.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n The systematic review identified 9 publications within the scope of the research question, with sample sizes ranging from 122 to over 1.3 million participants. The majority of studies addressed economic evaluation (88.9%), with most studies addressing pulmonary diseases (n=6; 66.6%), followed by neurological diseases (n=2; 22.3%), and only 1 (11.1%) study addressing dermatological diseases. These studies had an average quality access of 87.5% on the CHEC checklist. Only 2 studies were found to be transferable to Brazil and other countries with a similar health context. The economic evaluation revealed that 87.5% of studies highlighted the benefits of using AI in dermatology, neurology, and pulmonology, highlighting significant cost-effectiveness outcomes, with the most advantageous being a negative cost-effectiveness ratio of \u2013US $27,580 per QALY (quality-adjusted life year) for melanoma diagnosis, indicating substantial cost savings in this scenario. The only study assessing equity, based on 129,819 radiographic images, identified AI-assisted underdiagnosis, particularly in certain subgroups defined by gender, ethnicity, and socioeconomic status.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This review underscores the importance of transparency in the description of AI tools and the representativeness of population subgroups to mitigate health disparities. As AI is rapidly being integrated into health care, detailed assessments are essential to ensure that benefits reach all patients, regardless of sociodemographic factors.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/56240","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T09:35:06Z","timestamp":1747215306000},"page":"e56240-e56240","source":"Crossref","is-referenced-by-count":5,"title":["Economic Evaluations and Equity in the Use of Artificial Intelligence in Imaging Examinations for Medical Diagnosis in People With Dermatological, Neurological, and Pulmonary Diseases: Systematic Review"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7050-8278","authenticated-orcid":false,"given":"Giulia Os\u00f3rio","family":"Santana","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2598-5830","authenticated-orcid":false,"given":"Rodrigo de Macedo","family":"Couto","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1635-2225","authenticated-orcid":false,"given":"Rafael Maffei","family":"Loureiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1654-5980","authenticated-orcid":false,"given":"Brunna Carolinne Rocha Silva","family":"Furriel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-5610","authenticated-orcid":false,"given":"Luis Gustavo Nascimento","family":"de Paula","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9453-3582","authenticated-orcid":false,"given":"Edna Terezinha","family":"Rother","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7487-397X","authenticated-orcid":false,"given":"Joselisa P\u00e9res Queiroz","family":"de Paiva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4737-3293","authenticated-orcid":false,"given":"Lucas Reis","family":"Correia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"issue":"11","key":"R1","doi-asserted-by":"publisher","first-page":"e1196","DOI":"10.1016\/S2214-109X(18)30386-3","article-title":"High-quality health systems in the sustainable development goals era: time for a revolution","volume":"6","author":"Kruk","journal-title":"Lancet Glob Health"},{"issue":"4","key":"R2","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1136\/svn-2017-000101","article-title":"Artificial intelligence in healthcare: past, present and future","volume":"2","author":"Jiang","journal-title":"Stroke Vasc Neurol"},{"issue":"3","key":"R3","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.eng.2019.08.015","article-title":"Artificial intelligence in healthcare: review and prediction case studies","volume":"6","author":"Rong","journal-title":"Engineering (Beijing)"},{"issue":"18","key":"R4","doi-asserted-by":"publisher","first-page":"1174","DOI":"10.1016\/j.artint.2007.10.009","article-title":"From here to human-level AI","volume":"171","author":"McCarthy","journal-title":"Artif Intell"},{"issue":"7","key":"R5","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1016\/j.jval.2019.02.012","article-title":"Machine learning for health services researchers","volume":"22","author":"Doupe","journal-title":"Value Health"},{"issue":"1","key":"R6","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1146\/annurev.cs.04.060190.002221","article-title":"Machine Learning","volume":"4","author":"Mitchell","journal-title":"Annu Rev Comput Sci"},{"issue":"1","key":"R7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s12551-018-0449-9","article-title":"Machine learning: applications of artificial intelligence to imaging and diagnosis","volume":"11","author":"Nichols","journal-title":"Biophys Rev"},{"key":"R8","unstructured":"Organization WHO. 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