Jul 2026· Frontiers in Digital Health· Vol 8· 0 citations· 26 references
Medicine
TL;DR
OrL-HNS patients are largely familiar with LLMs and frequently use them, but their trust and confidence regarding health information provided by LLMs alone is limited, suggesting that clinician-led LLM use may be acceptable to many patients.
Abstract
Objectives Large language models (LLMs) are increasingly discussed for use in clinical practice. Beyond their performance, patients' acceptance is crucial for their implementation. We investigated ORL-HNS patients' familiarity with AI/LLMs, use patterns, and trust in LLM-based medical information and recommendations. Methods In this single-centre prospective survey at a German university hospital, ORL-HNS patients with and without malignant disease completed a 15-item questionnaire. Results A total of 123 patients, 20 (16%) with and 103 (84%) without malignant disease, participated in the study. Most patients were familiar with the term AI (96%, n = 118) and LLMs (78%, n = 96). Overall, 72/123 (59%) reported using LLMs. One third (33%, n = 40) retrieved “Health information”, rating the LLMs with median Likert scores for comprehensibility 5 [IQR 4, 6], conciseness 5 [IQR 3, 6] and coherence 5 [IQR 3, 6]. However, perceived medical accuracy received a median rating of 4 [IQR 3, 5], significantly lower than comprehensibility (p < 0.05). With respect to the confidence in the recommendations exclusively by LLMs [median 2 (IQR 2, 3.5)] received significantly lower ratings than doctors [median 5 (IQR 5, 6)] and doctors also using LLMs [median 5 (IQR 4, 6)], p < 0.0001 respectively. Conclusion ORL-HNS patients are largely familiar with LLMs and frequently use them, but their trust and confidence regarding health information provided by LLMs alone is limited. Patients show the greatest confidence in doctors' recommendations. Yet they reported similar confidence in physician recommendations and physician recommendations supported by LLMs, suggesting that clinician-led LLM use may be acceptable to many patients.
LLMs demonstrated similar guideline concordance, suggesting patients can expect comparable accuracy across platforms, and LLMs generally improved FKGL scores compared to the AAO-HNS CPG, they demonstrated lower FRE scores, indicating mixed results on overall readability.
Hetal Lad, Emily S Kwon, Ayushi Chadha et al.· Journal of Otorhinolaryngolo...· 0 citations
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Chen Li, Yu-Ting Hu, Xiao-Yan Wang et al.· Frontiers in Physiology· 0 citations
Final recommendation concordance did not meet the protocol-defined benchmark, ChatGPT-4o never altered an MTB decision, and clinically relevant errors occurred even among highly concordant outputs, showing that concordance alone does not guarantee safety.
J. De la Torre-Trillo, A. Munuera, M. D. Ureña et al.· Clinical and Translational O...· 0 citations
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