Evaluating Evidence-Based Communication through Generative AI using a Cross-Sectional Study with Laypeople Seeking Screening Information

Abstract

<title>Abstract</title> Large language models (LLMs) are used to seek health information. We investigate the prompt-dependent compliance of LLMs with evidence-based health communication guidelines and evaluate the efficacy of a minimal behavioral intervention for boosting laypeople’s prompting. Study 1 systematically varied prompt informedness, topic, and LLMs to evaluate LLM compliance. Study 2 randomized 300 UK participants to interact with LLMs under standard or boosted prompting conditions. Independent blinded raters assessed LLM response with 2 instruments. Study 1 found that LLMs failed evidence-based health communication standards, even with informed prompting. The quality of responses was found to be contingent upon prompt informedness. Study 2 revealed that laypeople frequently generated poor-quality responses; however, a simple boost improved response quality, though it remained below optimal standards. These findings underscore the inadequacy of LLMs as a standalone health communication tool. It is imperative to enhance LLM interfaces, integrate them with evidence-based frameworks, and teach prompt engineering. <bold>Study Registration</bold>: German Clinical Trials Register (DRKS) (Reg. No.: DRKS00035228) <bold>Ethical Approval</bold>: Ethics Committee of the University of Potsdam (Approval No. 52/2024)

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