10 citations · 12 across the 2 of their papers we have counts for
3 papers
cs.HC2024
AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
Samuel Schmidgall, Rojin Ziaei, Carl Harris +3
Evaluating large language models (LLM) in clinical scenarios is crucial to assessing their potential clinical utility. Existing benchmarks rely heavily on static question-answering…
cs.CL2024★ 10 cited
Addressing cognitive bias in medical language models
Samuel Schmidgall, Carl Harris, Ime Essien +7
There is increasing interest in the application large language models (LLMs) to the medical field, in part because of their impressive performance on medical exam questions. While…
cs.CL2023★ 2 cited
Language models are susceptible to incorrect patient self-diagnosis in medical applications
Rojin Ziaei, Samuel Schmidgall
Large language models (LLMs) are becoming increasingly relevant as a potential tool for healthcare, aiding communication between clinicians, researchers, and patients. However, tra…