Designing and Evaluating an LLM-based Health AI Research Assistant for Hypertension Self-Management
Using Health Claims Metadata Criteria
L.P.A. Simons (TU Delft - Interactive Intelligence)
Pradeep Murukannaiah (TU Delft - Interactive Intelligence)
M.A. Neerincx (TU Delft - Interactive Intelligence)
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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.