The exceptionally rapid development of highly flexible, reusable artificial intelligence (AI)
models is likely to usher in newfound capabilities in medicine. We propose a new paradigm …

Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …

Federated learning (FL), which provides a collaborative training scheme for distributed data
sources with privacy concerns, has become a burgeoning and attractive research area. Most …

Abstract Electronic Health Records (EHRs), which contain patients' medical histories in
various multi-modal formats, often overlook the potential for joint reasoning across imaging …

Background Comorbidity, frailty, and decreased cognitive function lead to a higher risk of
death in elderly patients (more than 65 years of age) during acute medical events. Early and …

Abstract Background The triglyceride-glucose (TyG) index was significantly associated with
insulin resistance (IR). Several studies have validated the effect of TyG index on …

While the general machine learning (ML) community has benefited from public datasets,
tasks, and models, the progress of ML in healthcare has been hampered by a lack of such …

Abstract We propose the Multimodal Clinical Benchmark for Emergency Care (MC-BEC), a
comprehensive benchmark for evaluating foundation models in Emergency Medicine using …

This study investigates the transformative potential of Large Language Models (LLMs), such
as OpenAI ChatGPT, in medical imaging. With the aid of public data, these models, which …

Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …