Designing and Evaluating an LLM-based Health AI Research Assistant for Hypertension Self-Management

Using Health Claims Metadata Criteria

Conference Paper (2024)
Authors

LPA Simons (TU Delft - Interactive Intelligence)

P.K. Murukannaiah (TU Delft - Interactive Intelligence)

M.A. Neerincx (TU Delft - Interactive Intelligence)

Research Group
Interactive Intelligence
To reference this document use:
https://doi.org/10.18690/um.fov.4.2024.16
More Info
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Publication Year
2024
Language
English
Research Group
Interactive Intelligence
Pages (from-to)
283-298
ISBN (electronic)
978-961-286-871-0
DOI:
https://doi.org/10.18690/um.fov.4.2024.16
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Abstract

Hypertension is a condition affecting most people over 45 years old. Health Self-Management offers many opportunities for prevention and cure. However, most scientific health literature is unknown by health professionals and/or patients. Per year about 200.000 new scientific papers on cardiovascular health appear, which is too much for a human to read. Hence, an LLM-based Health AI research assistant is developed for mining scientific literature on blood pressure and food. A user evaluation was conducted with n=8 participants who just completed an intensive lifestyle intervention for blood pressure self-management. They highlighted several challenges and opportunities for a Health AI, especially regarding claim transparency, data quality and risks of hallucinations. In the discussion we propose seven criteria using metadata and information characteristics to help evaluate ambiguous or conflicting health science claims.

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