Electronic Health Records (EHRs) play an important role in the healthcare system. However,
their complexity and vast volume pose significant challenges to data interpretation and …

Objectives This article aims to examine how generative artificial intelligence (AI) can be
adopted with the most value in health systems, in response to the Executive Order on AI …

Prompt engineering is a critical technique in the field of natural language processing that
involves designing and optimizing the prompts used to input information into models, aiming …

Background Large language models (LLMs) have shown remarkable capabilities in natural
language processing (NLP), especially in domains where labeled data are scarce or …

Many areas of medicine would benefit from deeper, more accurate phenotyping, but there
are limited approaches for phenotyping using clinical notes without substantial annotated …

We present Clinical Prediction with Large Language Models (CPLLM), a method that
involves fine-tuning a pre-trained Large Language Model (LLM) for predicting clinical …

When the first transformer-based language models were published in the late 2010s,
pretraining with general text and then fine-tuning the model on a task-specific dataset often …

Existing AI-based medicine prediction systems require substantial training time, computing
resources, and extensive labeled data, yet they often lack scalability. To bridge these gaps …

Abstract Automatic International Classification of Diseases (ICD) coding aims to assign
multiple ICD codes to a medical note with an average of 3,000+ tokens. This task is …

The advancement of Artificial Intelligence (AI) has catalyzed revolutionary changes across
various sectors, notably in healthcare. Among the significant developments in this field are …