Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Abstract Machine learning (ML) is becoming an integral aspect of several domains in
medicine. Yet, most pathologists and laboratory professionals remain unfamiliar with such …

A long-running goal of the clinical NLP community is the extraction of important variables
trapped in clinical notes. However, roadblocks have included dataset shift from the general …

Electronic health records (EHR) contain a lot of valuable information about individual
patients and the whole population. Besides structured data, unstructured data in EHRs can …

Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

In this study, we investigated the potential of ChatGPT, a large language model developed
by OpenAI, for the clinical named entity recognition task defined in the 2010 i2b2 challenge …

Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

Definition Machine learning (ML) is a study of computer algorithms for automation through
experience. ML is a subset of artificial intelligence (AI) that develops computer systems …

Importance The study highlights the potential of large language models, specifically GPT-3.5
and GPT-4, in processing complex clinical data and extracting meaningful information with …