Accepted for/Published in: JMIR Research Protocols
Date Submitted: Apr 28, 2023
Date Accepted: Oct 24, 2023
Economic evaluations and equity in the use of artificial intelligence in imaging exams for medical diagnosis in people with skin, neurological and pulmonary diseases: a systematic review protocol
ABSTRACT
Background:
Traditional healthcare systems face long-standing challenges, including patient diversity, geographical disparities, and financial constraints. The emergence of Artificial Intelligence (AI) in healthcare offers solutions to these challenges. AI, a multidisciplinary field, enhances clinical decision-making. However, imbalanced AI models may enhance health disparities.
Objective:
This systematic review aims to investigate the economic performance and equity impact of AI in diagnostic imaging for skin, neurological, and pulmonary diseases. The research question is: "To what extent does the use of AI in imaging exams for diagnosing skin, neurological, and pulmonary diseases result in improved economic outcomes, and does it promote equity in healthcare systems?".
Methods:
The study is a systematic review of economic and equity evaluations following PRISMA and CHEERS guidelines. Eligibility criteria include articles reporting on economic evaluations or equity considerations related to AI-based diagnostic imaging for specified diseases. Data will be collected from PubMed, Embase, Scopus, Web of Science, and reference lists. Data quality and transferability will be assessed according to CHEC, EPHPP and Welte checklists.
Results:
The results section will present a PRISMA flowchart of article selection, general characteristics of included studies, and the results of quality assessments. The main economic evaluation outcomes and equity results will be presented in a table.
Conclusions:
AI in diagnostic imaging offers potential benefits but also raises concerns about equity and economic impact. Bias in algorithms and disparities in access may hinder equitable outcomes. Evaluating the economic viability of AI applications is essential for resource allocation and affordability. Policymakers and healthcare stakeholders can benefit from this review's insights to make informed decisions. Limitations, including study variability and publication bias, will be considered in the analysis. This systematic review will provide valuable insights into the economic and equity implications of AI in diagnostic imaging. It aims to inform evidence-based decision-making and contribute to more efficient and equitable healthcare systems.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.