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Recently, large language models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and question-answering (QA) situations, these models frequently struggle in domains that require precision, such as medical applications, due to their lack of domain-specific knowledge. In this article, we describe the procedure for building a powerful, open-source language model specifically designed for medicine applications, termed as PMC-LLaMA.

The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations.

This study evaluates ChatGPT's symptom-checking accuracy across a broad range of diseases using the Mayo Clinic Symptom Checker patient service as a benchmark.

Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic EHRs, supporting tailored learning activities in informatics education to enhance educational utility through a DEIA [...]

In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.

The main objective of this study is to evaluate the ability of the Large Language Model ChatGPT to accurately answer USMLE board style medical ethics questions compared to medical knowledge based questions. This study has the additional objectives of comparing the overall accuracy of GPT-3.5 to GPT-4 and to assess the variability of responses given by each version.

Clinical Decision Support (CDS) tools have a mixed record of effectiveness, often due to inadequate alignment with clinical workflows and poor usability. While there's a consensus that usability testing methods address these issues, in practice, usability testing is generally only used for selected projects (such as funded research studies). There is a critical need for CDS operations to apply usability testing to all CDS implementations.

Choice architecture refers to the design of decision environments, which can influence healthcare decision-making. Nudges are subtle adjustments in these environments that guide decisions toward desired outcomes. For example, Computerized Provider Order Entry (CPOE) within Electronic Health Records (EHR) recommends frequencies for interventions such as nursing assessments and medication administrations, but these can link to around-the-clock schedules without clinical necessity.

Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of [...]