VQV Signal
This study evaluates three large language model chatbots—Claude Sonnet 5, Gemini 3.1 Pro, and ChatGPT GPT-5.5—on their ability to retrieve relevant clinical studies for medical questions. It addresses a gap in prior research by focusing on the quality of retrieved studies rather than citation fabri...
This study evaluates three large language model chatbots—Claude Sonnet 5, Gemini 3.1 Pro, and ChatGPT GPT-5.5—on their ability to retrieve relevant clinical studies for medical questions. It addresses a gap in prior research by focusing on the quality of retrieved studies rather than citation fabri...
AI-assisted summary based on the listed source.
Large language model (LLM) chatbots are increasingly used to answer clinical questions with citations to relevant clinical studies. Prior research has largely focused on citation fabrication, leaving a gap in evaluating the quality of retrieved studies and the factors driving their selection. In this study, we...
Understanding how well AI chatbots retrieve expert-level clinical studies is crucial for their reliable use in medical decision-making. This research helps clarify factors influencing study selection by different LLMs, informing their deployment in healthcare.
VQV organizes public signals from inspectable sources. It does not independently verify the underlying report.
Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to Consumer AI, connected to arXiv.
No login, cookies, social SDKs, or automatic posting.