AI Article Synopsis
- AI and ML have the power to change healthcare, but issues like health data poverty can make that impact uneven, perpetuating existing health disparities.* -
- The review found that health disparities are influenced by biases in AI/ML, with a focus on input data and the effectiveness of the systems.* -
- To mitigate these disparities, the authors call for better data generation, a clearer understanding of AI/ML limitations, and ongoing regulation to monitor outcomes.*
Article Abstract
Artificial intelligence (AI) and machine learning (ML) have an immense potential to transform healthcare as already demonstrated in various medical specialties. This scoping review focuses on the factors that influence health data poverty, by conducting a literature review, analysis, and appraisal of results. Health data poverty is often an unseen factor which leads to perpetuating or exacerbating health disparities. Improvements or failures in addressing health data poverty will directly impact the effectiveness of AI/ML systems. The potential causes are complex and may enter anywhere along the development process. The initial results highlighted studies with common themes of health disparities (72%), AL/ML bias (28%) and biases in input data (18%). To properly evaluate disparities that exist we recommend a strengthened effort to generate unbiased equitable data, improved understanding of the limitations of AI/ML tools, and rigorous regulation with continuous monitoring of the clinical outcomes of deployed tools.
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569513 | PMC |
| http://dx.doi.org/10.1371/journal.pdig.0000313 | DOI Listing |
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