activity
20242026
collaborators

7 papers

cs.IR2026

Grounded in Consensus, In Step With Emerging Science: A Consensus-Anchored Multi-Corpus Clinical Chatbot for Long COVID

Yining Wu, Philip DiGiacomo, Ying Ding +1

Long COVID (LC) poses a challenge for clinical decision support because relevant evidence is distributed across sources with different update cycles, evidentiary roles, and levels…

cs.AI2026

DreamKG: A KG-Augmented Conversational System for People Experiencing Homelessness

Javad M Alizadeh, Genhui Zheng, Chiu C Tan +7

People experiencing homelessness (PEH) face substantial barriers to accessing timely, accurate information about community services. DreamKG addresses this through a knowledge grap…

cs.AI2025

CARE-RAG - Clinical Assessment and Reasoning in RAG

Deepthi Potluri, Aby Mammen Mathew, Jeffrey B DeWitt +4

Access to the right evidence does not guarantee that large language models (LLMs) will reason with it correctly. This gap between retrieval and reasoning is especially concerning i…

cs.AI2025

Demo: Guide-RAG: Evidence-Driven Corpus Curation for Retrieval-Augmented Generation in Long COVID

Philip DiGiacomo, Haoyang Wang, Jinrui Fang +3

As AI chatbots gain adoption in clinical medicine, developing effective frameworks for complex, emerging diseases presents significant challenges. We developed and evaluated six Re…

cs.AI2024

Integrating Social Determinants of Health into Knowledge Graphs: Evaluating Prediction Bias and Fairness in Healthcare

Tianqi Shang, Weiqing He, Tianlong Chen +4

Social determinants of health (SDoH) play a crucial role in patient health outcomes, yet their integration into biomedical knowledge graphs remains underexplored. This study addres…

cs.CV2024

Path-RAG: Knowledge-Guided Key Region Retrieval for Open-ended Pathology Visual Question Answering

Awais Naeem, Tianhao Li, Huang-Ru Liao +9

Accurate diagnosis and prognosis assisted by pathology images are essential for cancer treatment selection and planning. Despite the recent trend of adopting deep-learning approach…