3 citations · 9 across the 12 of their papers we have counts for
14 papers
Clinician use of language models diverges from how the models are evaluated
Krithik Vishwanath, Haitong Lin, Anton Alyakin +19
Large language model (LLM) assistants are being deployed to clinicians across health systems, and judgments about their readiness rest largely on benchmark scores, most of them der…
Large language models are vulnerable to incidental information in clinical documentation and reasoning
Krithik Vishwanath, Brandon Ye, Anton Alyakin +4
Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these…
Large Language Models Predict Functional Outcomes after Acute Ischemic Stroke
Anjali K. Kapoor, Anton Alyakin, Jin Vivian Lee +8
Accurate prediction of functional outcomes after acute ischemic stroke can inform clinical decision-making and resource allocation. Prior work on modified Rankin Scale (mRS) predic…
"All You Need" is Not All You Need for a Paper Title: On the Origins of a Scientific Meme
Anton Alyakin
The 2017 paper ''Attention Is All You Need'' introduced the Transformer architecture-and inadvertently spawned one of machine learning's most persistent naming conventions. We anal…
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…
CNS-Obsidian: A Neurosurgical Vision-Language Model Built From Scientific Publications
Anton Alyakin, Jaden Stryker, Daniel Alexander Alber +29
General-purpose VLMs demonstrate impressive capabilities, but their opaque training on uncurated internet data poses critical limitations for high-stakes decision-making, such as i…
MedG-KRP: Medical Graph Knowledge Representation Probing
Gabriel R. Rosenbaum, Lavender Yao Jiang, Ivaxi Sheth +11
Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to…
MedMobile: A mobile-sized language model with clinical capabilities
Krithik Vishwanath, Jaden Stryker, Anton Alyakin +2
Language models (LMs) have demonstrated expert-level reasoning and recall abilities in medicine. However, computational costs and privacy concerns are mounting barriers to wide-sca…
An Analysis of Euclidean vs. Graph-Based Framing for Bilingual Lexicon Induction from Word Embedding Spaces
Kelly Marchisio, Youngser Park, Ali Saad-Eldin +4
Much recent work in bilingual lexicon induction (BLI) views word embeddings as vectors in Euclidean space. As such, BLI is typically solved by finding a linear transformation that…