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Economic evaluations and equity in the use of artificial intelligence in imaging exams for medical diagnosis in people with dermatological, neurological, and pulmonary diseases: a systematic review
ABSTRACT
Background:
Healthcare systems around the world face numerous challenges. Recent advances in artificial intelligence (AI) have offered promising solutions, particularly in diagnostic imaging.
Objective:
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 healthcare systems.
Methods:
This systematic review has two main components: economic evaluation and equity assessment. We used the PRISMA tool to ensure adherence to best practices in systematic reviews. The protocol was registered with PROSPERO, and we followed the CHEERS guidelines for economic reviews and PRISMA-E for equity. The search was conducted in the PubMed, Embase, Scopus, and Web of Science databases. Methodological quality was assessed using the following checklists: CHEC for economic evaluations, EPHPP for equity evaluation studies, and Welte for transferability.
Results:
The systematic review identified nine publications within the scope of the research question. The majority of studies addressed economic evaluation (88.9%), with most studies addressing pulmonary diseases (6 - 66.6%), followed by neurological diseases (2 - 22.3%) and only one study addressing dermatological diseases (11.1%). These studies had an average quality access of 87.5% on the CHEC checklist. Only two studies were found to be transferable to Brazil and other countries with similar health context. The economic evaluation revealed that 87.5% of studies highlighted the benefits of using AI in dermatology, neurology, and pulmonology. The only study assessing equity identified AI-assisted underdiagnosis, particularly in certain subgroups.
Conclusions:
This review underscores the importance of transparency in the description of AI tools and representativeness of population subgroups to mitigate health disparities. As AI is rapidly being integrated into healthcare, detailed assessments are essential to ensure that benefits reach all patients, regardless of sociodemographic factors.
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Copyright
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