3 citations · 9 across the 12 of their papers we have counts for
6 papers · 2 filters
Generalist Large Language Models Outperform Clinical Tools on Medical Benchmarks
Krithik Vishwanath, Mrigayu Ghosh, Anton Alyakin +3
Specialized clinical AI assistants are rapidly entering medical practice, often framed as safer or more reliable than general-purpose large language models (LLMs). Yet, unlike fron…
Generalist Foundation Models Are Not Clinical Enough for Hospital Operations
Lavender Y. Jiang, Angelica Chen, Xu Han +16
Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversat…
Evaluating the performance and fragility of large language models on the self-assessment for neurological surgeons
Krithik Vishwanath, Anton Alyakin, Mrigayu Ghosh +5
The Congress of Neurological Surgeons Self-Assessment for Neurological Surgeons (CNS-SANS) questions are widely used by neurosurgical residents to prepare for written board examina…
Medical large language models are easily distracted
Krithik Vishwanath, Anton Alyakin, Daniel Alexander Alber +3
Large language models (LLMs) have the potential to transform medicine, but real-world clinical scenarios contain extraneous information that can hinder performance. The rise of ass…
It is Too Many Options: Pitfalls of Multiple-Choice Questions in Generative AI and Medical Education
Shrutika Singh, Anton Alyakin, Daniel Alexander Alber +9
The performance of Large Language Models (LLMs) on multiple-choice question (MCQ) benchmarks is frequently cited as proof of their medical capabilities. We hypothesized that LLM pe…
BPQA Dataset: Evaluating How Well Language Models Leverage Blood Pressures to Answer Biomedical Questions
Chi Hang, Ruiqi Deng, Lavender Yao Jiang +4
Clinical measurements such as blood pressures and respiration rates are critical in diagnosing and monitoring patient outcomes. It is an important component of biomedical data, whi…