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 …

The success of foundation models such as ChatGPT and AlphaFold has spurred significant
interest in building similar models for electronic medical records (EMRs) to improve patient …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Importance There is increased interest in and potential benefits from using large language
models (LLMs) in medicine. However, by simply wondering how the LLMs and the …

Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

Objectives Patient representation learning refers to learning a dense mathematical
representation of a patient that encodes meaningful information from Electronic Health …

Foundation large language models (LLMs) have shown an impressive ability to solve tasks
across a wide range of fields including health. To effectively solve personalized health tasks …

Background AI platforms are equipped with advanced‎ algorithms that have the potential to
offer a wide range of‎ applications in healthcare services. However, information about the …

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 …