A new study reveals that while generative AI (GAI) tools can accelerate data collection for health policies, they exhibit significant inaccuracies in interpretation, particularly in African and Eastern Mediterranean regions. The study, published in PLOS ONE, compared the performance of a GAI tool against human subject-matter experts in identifying and interpreting two key health policies: vaccination and quarantine/isolation.
By Dr. Sanchari Sinha Dutta, Ph.D.Reviewed by Lily Ramsey, LLMDec 17 2024 While AI tools accelerate data collection , a study reveals significant inaccuracies in policy interpretation, particularly in African and Eastern Mediterranean regions.
Recently emerging generative artificial intelligence technology has developed tools for rapidly screening and examining vast amounts of medical data. Important observations The GAI tool used in the study significantly increased the data collection efficacy for both vaccination and isolation and quarantine policy datasets. The tool reduced the time required for vaccination data collection by 88% and increased the efficiency by 90%.
The study found significant systematic inaccuracies and imprecision of the GAI tool after filtering out the responses on the lack of legal vaccination requirements that were in concordance between the expert and the tool. In this assessment, the expert and the tool were in agreement at 67%. Similar to vaccination responses, a variation in concordance regarding quarantine and isolation responses was observed across the WHO regions.
Artificial Intelligence Health Policy Data Collection Accuracy Interpretation
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