activity
20242026
most citedA Framework for Longitudinal Health AI Agents

1 citations · 1 across the 3 of their papers we have counts for

collaborators
Showing cs.CLShow all

6 papers · 1 filter

cs.CL2025

Medical Hallucinations in Foundation Models and Their Impact on Healthcare

Yubin Kim, Hyewon Jeong, Shan Chen +24

Hallucinations in foundation models arise from autoregressive training objectives that prioritize token-likelihood optimization over epistemic accuracy, fostering overconfidence an…

cs.CL2025

Substance over Style: Evaluating Proactive Conversational Coaching Agents

Vidya Srinivas, Xuhai Xu, Xin Liu +5

While NLP research has made strides in conversational tasks, many approaches focus on single-turn responses with well-defined objectives or evaluation criteria. In contrast, coachi…

cs.CL2024

A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making

Yubin Kim, Chanwoo Park, Hyewon Jeong +7

Medical Decision-Making (MDM) is a multi-faceted process that requires clinicians to assess complex multi-modal patient data patient, often collaboratively. Large Language Models (…

cs.CL2024

MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

Yubin Kim, Chanwoo Park, Hyewon Jeong +7

Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open…

cs.CL2024

Can AI Relate: Testing Large Language Model Response for Mental Health Support

Saadia Gabriel, Isha Puri, Xuhai Xu +2

Large language models (LLMs) are already being piloted for clinical use in hospital systems like NYU Langone, Dana-Farber and the NHS. A proposed deployment use case is psychothera…

cs.CL2024

Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data

Yubin Kim, Xuhai Xu, Daniel McDuff +2

Large language models (LLMs) are capable of many natural language tasks, yet they are far from perfect. In health applications, grounding and interpreting domain-specific and non-l…