77 citations · 97 across the 23 of their papers we have counts for
15 papers · 1 filter
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
David Restrepo, Chenwei Wu, Zhengxu Tang +14
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising…
The use of large language models to enhance cancer clinical trial educational materials
Mingye Gao, Aman Varshney, Shan Chen +15
Cancer clinical trials often face challenges in recruitment and engagement due to a lack of participant-facing informational and educational resources. This study investigated the…
Position Paper On Diagnostic Uncertainty Estimation from Large Language Models: Next-Word Probability Is Not Pre-test Probability
Yanjun Gao, Skatje Myers, Shan Chen +7
Large language models (LLMs) are being explored for diagnostic decision support, yet their ability to estimate pre-test probabilities, vital for clinical decision-making, remains l…
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen +8
Large Language Models (LLMs) hold great promise to revolutionize current clinical systems for their superior capacities on medical text processing tasks and medical licensing exams…
WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation
João Matos, Shan Chen, Siena Placino +13
Multimodal/vision language models (VLMs) are increasingly being deployed in healthcare settings worldwide, necessitating robust benchmarks to ensure their safety, efficacy, and fai…
debiaSAE: Benchmarking and Mitigating Vision-Language Model Bias
Kuleen Sasse, Shan Chen, Jackson Pond +2
As Vision Language Models (VLMs) gain widespread use, their fairness remains under-explored. In this paper, we analyze demographic biases across five models and six datasets. We fi…
Wait, but Tylenol is Acetaminophen... Investigating and Improving Language Models' Ability to Resist Requests for Misinformation
Shan Chen, Mingye Gao, Kuleen Sasse +6
Background: Large language models (LLMs) are trained to follow directions, but this introduces a vulnerability to blindly comply with user requests even if they generate wrong info…
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
Yanjun Gao, Skatje Myers, Shan Chen +5
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Desp…
Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education
Huizi Yu, Jiayan Zhou, Lingyao Li +22
Background: Simulated patient systems are important in medical education and research, providing safe, integrative training environments and supporting clinical decision making. Ad…
Cross-Care: Assessing the Healthcare Implications of Pre-training Data on Language Model Bias
Shan Chen, Jack Gallifant, Mingye Gao +12
Large language models (LLMs) are increasingly essential in processing natural languages, yet their application is frequently compromised by biases and inaccuracies originating in t…
Language Models are Surprisingly Fragile to Drug Names in Biomedical Benchmarks
Jack Gallifant, Shan Chen, Pedro Moreira +7
Medical knowledge is context-dependent and requires consistent reasoning across various natural language expressions of semantically equivalent phrases. This is particularly crucia…
Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine
Rui Yang, Yilin Ning, Emilia Keppo +6
Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-a…
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
David Restrepo, Chenwei Wu, Constanza Vásquez-Venegas +5
The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient o…
Seeds of Stereotypes: A Large-Scale Textual Analysis of Race and Gender Associations with Diseases in Online Sources
Lasse Hyldig Hansen, Nikolaj Andersen, Jack Gallifant +7
Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to p…
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Shan Chen, Jack Gallifant, Marco Guevara +5
Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synth…