16 citations · 54 across the 20 of their papers we have counts for
20 papers
Performance of large language models in the optical diagnosis of colorectal polyps
Joshua C. Vences, William T. Tran, Nikko Gimpaya +15
Background and Study Aims: Accurate optical diagnosis of colorectal polyps guides resection strategy and surveillance, with multimodal large language models (MLLMs) showing potenti…
Fast Computation of Free-Support Wasserstein Medians
Kisung You, Mauro Giuffré, Dennis Shung
The Wasserstein median is a robust alternative to the Wasserstein barycenter for averaging probability measures, but exact empirical computation can be expensive. A natural metric-…
multivariateGPT: a decoder-only transformer for multivariate categorical and numeric data
Andrew J. Loza, Jun Yup Kim, Shangzheng Song +4
Real-world processes often generate data that are a mix of categorical and numeric values that are recorded at irregular and informative intervals. Discrete token-based approaches…
LAMP: Extracting Local Decision Surfaces From Large Language Models
Ryan Chen, Youngmin Ko, Zeyu Zhang +5
We introduce LAMP (Local Attribution Mapping Probe), a method that shines light onto a black-box language model's decision surface and studies how reliably a model maps its stated…
Learning over von Mises-Fisher Distributions via a Wasserstein-like Geometry
Kisung You, Dennis Shung, Mauro Giuffrè
We introduce a novel, geometry-aware distance metric for the family of von Mises-Fisher (vMF) distributions, which are fundamental models for directional data on the unit hypersphe…
MedSimAI: Simulation and Formative Feedback Generation to Enhance Deliberate Practice in Medical Education
Yann Hicke, Jadon Geathers, Kellen Vu +8
Medical education faces challenges in providing scalable, consistent clinical skills training. Simulation with standardized patients (SPs) develops communication and diagnostic ski…
Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium
Amin Adibi, Xu Cao, Zongliang Ji +39
The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam,…
Benchmarking Generative AI for Scoring Medical Student Interviews in Objective Structured Clinical Examinations (OSCEs)
Jadon Geathers, Yann Hicke, Colleen Chan +5
Objective Structured Clinical Examinations (OSCEs) are widely used to assess medical students' communication skills, but scoring interview-based assessments is time-consuming and p…
Integrating Expert Judgment and Algorithmic Decision Making: An Indistinguishability Framework
Rohan Alur, Loren Laine, Darrick K. Li +3
We introduce a novel framework for human-AI collaboration in prediction and decision tasks. Our approach leverages human judgment to distinguish inputs which are algorithmically in…
Trajectory Flow Matching with Applications to Clinical Time Series Modeling
Xi Zhang, Yuan Pu, Yuki Kawamura +4
Modeling stochastic and irregularly sampled time series is a challenging problem found in a wide range of applications, especially in medicine. Neural stochastic differential equat…
Me LLaMA: Foundation Large Language Models for Medical Applications
Qianqian Xie, Qingyu Chen, Aokun Chen +15
Recent advancements in large language models (LLMs) like ChatGPT and LLaMA show promise in medical applications, yet challenges remain in medical language comprehension. This study…
Assessing the Usability of GutGPT: A Simulation Study of an AI Clinical Decision Support System for Gastrointestinal Bleeding Risk
Colleen Chan, Kisung You, Sunny Chung +10
Applications of large language models (LLMs) like ChatGPT have potential to enhance clinical decision support through conversational interfaces. However, challenges of human-algori…
Auditing for Human Expertise
Rohan Alur, Loren Laine, Darrick K. Li +3
High-stakes prediction tasks (e.g., patient diagnosis) are often handled by trained human experts. A common source of concern about automation in these settings is that experts may…
On the spherical Laplace distribution
Kisung You, Dennis Shung
The von Mises-Fisher (vMF) distribution has long been a mainstay for inference with data on the unit hypersphere in directional statistics. The performance of statistical inference…
On the Wasserstein median of probability measures
Kisung You, Dennis Shung, Mauro Giuffrè
The primary choice to summarize a finite collection of random objects is by using measures of central tendency, such as mean and median. In the field of optimal transport, the Wass…
Comparing multiple latent space embeddings using topological analysis
Kisung You, Ilmun Kim, Ick Hoon Jin +2
The latent space model is one of the well-known methods for statistical inference of network data. While the model has been much studied for a single network, it has not attracted…
MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data
Michal Gerasimiuk, Dennis Shung, Alexander Tong +6
A major challenge in embedding or visualizing clinical patient data is the heterogeneity of variable types including continuous lab values, categorical diagnostic codes, as well as…
Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance
Alexander Tong, Guillaume Huguet, Dennis Shung +5
In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many case…
Rdimtools: An R package for Dimension Reduction and Intrinsic Dimension Estimation
Kisung You
Discovering patterns of the complex high-dimensional data is a long-standing problem. Dimension Reduction (DR) and Intrinsic Dimension Estimation (IDE) are two fundamental thematic…
Making Logic Learnable With Neural Networks
Tobias Brudermueller, Dennis L. Shung, Adrian J. Stanley +2
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally…