โก Quick Summary
This systematic review evaluated the economic feasibility and equity of using artificial intelligence (AI) in diagnostic imaging for dermatological, neurological, and pulmonary diseases. The findings indicate that 87.5% of studies reported significant cost-effectiveness, particularly highlighting a negative cost-effectiveness ratio of -US $27,580 per QALY for melanoma diagnosis.
๐ Key Details
- ๐ Sample Size: Ranged from 122 to over 1.3 million participants
- ๐งฉ Focus Areas: Dermatology, Neurology, Pulmonology
- โ๏ธ Methodology: PRISMA guidelines, CHEC and EPHPP checklists for quality assessment
- ๐ Quality Score: Average of 87.5% on CHEC checklist
๐ Key Takeaways
- ๐ก AI in Imaging: 87.5% of studies showed benefits in economic evaluations.
- ๐ Cost-Effectiveness: Notable negative cost-effectiveness ratio of -US $27,580 per QALY for melanoma.
- ๐ Limited Transferability: Only 2 studies applicable to Brazil and similar health contexts.
- โ๏ธ Equity Concerns: AI-assisted underdiagnosis identified in specific subgroups by gender, ethnicity, and socioeconomic status.
- ๐ Research Scope: Focused on real-world applications of AI in diagnostic imaging.
- ๐ Systematic Review: Included 9 publications relevant to the research question.
- ๐ฅ Health Disparities: Emphasizes the need for transparency and representativeness in AI tools.
๐ Background
Health care systems globally are grappling with numerous challenges, particularly in the realm of diagnostic imaging. Recent advancements in artificial intelligence (AI) have emerged as promising solutions, offering the potential to enhance diagnostic accuracy and efficiency. This systematic review aims to explore the economic implications and equity considerations of AI in imaging for dermatological, neurological, and pulmonary diseases.
๐๏ธ Study
The systematic review was conducted following the PRISMA guidelines, with a focus on economic evaluations and equity assessments related to AI-based diagnostic imaging tools. The research included a comprehensive search across multiple databases, including PubMed, Embase, Scopus, and Web of Science, ensuring a robust selection of relevant studies.
๐ Results
The review identified a total of 9 publications, with the majority focusing on economic evaluations (88.9%). Notably, studies on pulmonary diseases were predominant, comprising 66.6% of the research. The findings revealed that a significant portion of studies (87.5%) highlighted the cost-effectiveness of AI applications, particularly in melanoma diagnosis, which demonstrated a remarkable negative cost-effectiveness ratio of -US $27,580 per QALY.
๐ Impact and Implications
The implications of this review are profound, as it underscores the potential of AI to not only improve economic outcomes in diagnostic imaging but also to address equity issues within healthcare systems. The identification of AI-assisted underdiagnosis in specific demographic groups highlights the necessity for careful implementation of AI technologies to ensure that all patients benefit, regardless of their sociodemographic background.
๐ฎ Conclusion
This systematic review emphasizes the critical role of transparency and representativeness in the development and application of AI tools in healthcare. As AI continues to be integrated into diagnostic processes, it is essential to conduct thorough assessments to ensure equitable access and benefits for all patients. The future of AI in healthcare holds great promise, but it requires ongoing vigilance to mitigate potential disparities.
๐ฌ Your comments
What are your thoughts on the integration of AI in diagnostic imaging? How do you think we can address equity concerns in this rapidly evolving field? ๐ฌ Join the conversation in the comments below or connect with us on social media:
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.
Abstract
BACKGROUND: Health care 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 health care systems.
METHODS: 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.
RESULTS: 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 -US $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.
CONCLUSIONS: 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.
Author: [‘Santana GO’, ‘Couto RM’, ‘Loureiro RM’, ‘Furriel BCRS’, ‘de Paula LGN’, ‘Rother ET’, ‘de Paiva JPQ’, ‘Correia LR’]
Journal: Interact J Med Res
Citation: Santana GO, et al. 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. 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. 2025; 14:e56240. doi: 10.2196/56240