Current Artificial Intelligence Large Language Models Exhibit Sycophantic Behavior in Orthopaedic Contexts

BACKGROUND: The use of large language models (LLMs) is increasingly common. However, LLMs may exhibit sycophancy, echoing users' beliefs while avoiding contradiction. In the present study, we describe sycophancy in general-purpose LLMs when applied to orthopaedic contexts. METHODS: We investigated sycophancy in 2 general-purpose LLMs. We evaluated performance on 3 tasks: (1) accuracy on benchmark answering: LLMs were tested on validated benchmark orthopaedic questions, with correct and incorrect cues, and the change in accuracy and sycophancy error rate were determined; (2) user belief agreement: LLMs were provided with ambiguous statements and a user belief, and LLM agreement, contradiction, and uncertainty were described; and (3) false information detection: false information was placed within a task prompt to measure noncontradiction and propagation rates. RESULTS: Baseline factual accuracy on benchmark questioning was 78%, decreasing with correct hints (71%) (p = 0.49). With incorrect hints, LLM accuracy declined significantly (48%) (p < 0.001), with a sycophancy error rate of 52%. Presented with user beliefs about an indefinite, controversial statement, models echoed user beliefs in 56%, expressed uncertainty in 12%, and contradicted users in 32% of statements. In noncontradiction tasks, models perpetuated incorrect attributions 99% of the time yet reliably corrected statistical distortions 97% of the time. CONCLUSIONS: Although popular general-purpose LLMs have useful orthopaedic applications, they exhibit sycophancy, with a tendency toward agreement and without recognition of ambiguity. This is a key weakness to be addressed. Findings should be interpreted cautiously given the variability in model design, prompting, and models evaluated. CLINICAL RELEVANCE: The tendency of general-purpose LLMs to agree without recognizing clinical ambiguity may limit their reliability in orthopaedic applications.