Real-world human-LLM Interactions-Prospective blinded versus unblinded expert physician assessments of LLM responses to complex medical dilemmas

Abstract

Current evaluations of large language models (LLMs) in healthcare have largely emphasized theoretical benchmarks and clinician oversight, with limited exploration of real-world physician-AI interaction. In this two-stage prospective study, we assessed physician satisfaction with LLM-generated responses to real clinical queries. This study did not evaluate clinical accuracy, patient outcomes, or patient safety. In the first unblinded stage, physicians used three models - a general-purpose model (GPT-4o), a reasoning-focused model (GPT-o1), and a healthcare-specific model (OpenEvidence) - to address 25 clinical dilemmas - and rated the quality of the responses. In the second blinded stage, the same physicians evaluated responses generated either by an LLM or by a human alone, without knowledge of the source. Across 100 real-world medical responses, median physician scores on a 5-point Likert scale were comparable between unblinded and blinded evaluations (p = 0.90). Satisfaction was not associated with physicians’ resistance to change, nor did it correlate with the accuracy or relevance of cited literature. These findings suggest that physicians did not favor information generated by LLMs over externally provided responses, and that clinician satisfaction alone may not serve as a reliable proxy for validating decision support tools.