68 citations · 123 across the 15 of their papers we have counts for
6 papers · 1 filter
Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?
Jiyao Yang, Yang Liu, Zhenyue Qin +2
The rapid advancement of generative AI raises concerns about the misuse of Multimodal LLMs (MLLMs) for large-scale disinformation campaigns on social media. Despite existing resear…
A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis
Fan Ma, Mauro Giuffrè, Donald Wright +12
Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against…
Benchmarking AI Agents for Addressing Scientific Challenges Across Scales
Tianyu Liu, Allen Xin Wang, Antonia Panescu +30
AI agents are increasingly being developed to accelerate scientific discovery, yet their practical capabilities in real research settings remain poorly understood. Existing benchma…
AI-generated data contamination erodes pathological variability and diagnostic reliability
Hongyu He, Shaowen Xiang, Ye Zhang +15
Generative artificial intelligence (AI) is rapidly populating medical records with synthetic content, creating a feedback loop where future models are increasingly at risk of train…
EHR-RAG: Bridging Long-Horizon Structured Electronic Health Records and Large Language Models via Enhanced Retrieval-Augmented Generation
Lang Cao, Qingyu Chen, Yue Guo
Electronic Health Records (EHRs) provide rich longitudinal clinical evidence that is central to medical decision-making, motivating the use of retrieval-augmented generation (RAG)…
EHRNavigator: A Multi-Agent System for Patient-Level Clinical Question Answering over Heterogeneous Electronic Health Records
Lingfei Qian, Mauro Giuffre, Yan Wang +11
Clinical decision-making increasingly relies on timely and context-aware access to patient information within Electronic Health Records (EHRs), yet most existing natural language q…