works on

From the 1 of 10 linked papers with an AI index.

activity
20242026
collaborators

10 papers

cs.CV2026

MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation

Yi Lin, Yihao Ding, Elana Benishay +8

MonteRET is an AI system that combines whole‑volume CT features with region‑level anatomical information and knowledge retrieval to automatically generate more complete and clinica…

cs.CL2026

Budget-Aware Routing for Long Clinical Text

Khizar Qureshi, Geoffrey Martin, Yifan Peng

A key challenge for large language models is token cost per query and overall deployment cost. Clinical inputs are long, heterogeneous, and often redundant, while downstream tasks…

cs.CV2026

Establishing dermatopathology encyclopedia DermpathNet with Artificial Intelligence-Based Workflow

Ziyang Xu, Mingquan Lin, Yiliang Zhou +6

Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and dermatopathology trainees. To establ…

cs.CL2026

RSNA Large Language Model Benchmark Dataset for Chest Radiographs of Cardiothoracic Disease: Radiologist Evaluation and Validation Enhanced by AI Labels (REVEAL-CXR)

Yishu Wei, Adam E. Flanders, Errol Colak +35

Multimodal large language models have demonstrated comparable performance to that of radiology trainees on multiple-choice board-style exams. However, to develop clinically useful…

cs.CL2025

A Multi-agent Large Language Model Framework to Automatically Assess Performance of a Clinical AI Triage Tool

Adam E. Flanders, Yifan Peng, Luciano Prevedello +6

Purpose: The purpose of this study was to determine if an ensemble of multiple LLM agents could be used collectively to provide a more reliable assessment of a pixel-based AI triag…

cs.AI2025

Enhancing Health Fact-Checking with LLM-Generated Synthetic Data

Jingze Zhang, Jiahe Qian, Yiliang Zhou +1

Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline t…