Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
Lisa C. Adams, Linus Marx, Erik Thiele Orberg, Keno Bressem, Sebastian Ziegelmayer + 6 more
TLDR
Atomic fact-checking, by linking AI claims to source guidelines, substantially boosts clinician trust in LLM oncology recommendations.
Key contributions
- Atomic fact-checking decomposes AI recommendations into individually verifiable claims linked to source guidelines.
- This method significantly increased clinician trust in large language model (LLM) oncology recommendations.
- Clinician trust rose from 26.9% to 66.5% with a large effect size (Cohen's d=0.94).
Why it matters
Building trust in AI for high-stakes clinical decisions is paramount. This paper demonstrates a highly effective method to achieve this, paving the way for safer and more widespread adoption of LLMs in healthcare, particularly oncology.
Original Abstract
Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, increase clinician trust compared to traditional explainability approaches? Findings: In this randomized trial of 356 clinicians generating 7,476 trust ratings, atomic fact-checking produced a large effect on trust (Cohen's d = 0.94), increasing the proportion of clinicians expressing trust from 26.9% to 66.5%. Traditional transparency mechanisms showed a dose-response gradient of improvement over baseline (d = 0.25 to 0.50). Meaning: Decomposing AI recommendations into individually verifiable claims linked to source guidelines produces substantially higher clinician trust than traditional explainability approaches in high-stakes clinical decisions.
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