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To evaluate and compare the diagnostic responses generated by two artificial intelligence models developed 54 years apart and to encourage physicians to explore the use of large language models (LLMs) like GPT-4o in clinical practice.

The digitalization of health records stands to improve decision-making at clinical, administrative, and policy level. Efforts follow various paths and are closely intertwined with health system and organizational configurations. Problems persist in both uptake and use. This study explores the digitalization trajectories of academic health centers (AHCs) to understand tensions between organizational and government strategies and their impact on digital development.

Health professional (HP) trainee burnout is hard to capture. There are many validated quantitative tools to assess trainee burnout, but fewer qualitative methodological tools that can elicit rich and trustworthy qualitative data on HP trainee burnout.

Quantify the effect of ambient artificial intelligence (AI) scribe technology on work experience, clinical operations, and patient experience in pediatric primary care.

Health equity is greatly impacted by the systems and processes with which health systems deliver care. Given the minimal guidance on measurement and reporting of health inequities specific to key population health outcomes, a solution for measurement of health equity is proposed.

Data governance, the policies, and procedures for managing data, is a critical factor for secondary use of clinical data for research.

To develop and apply a reproducible methodology for evaluating generative artificial intelligence (AI) powered systems in health care, addressing the gap between theoretical evaluation frameworks and practical implementation guidance.

This study aimed to systematically evaluate and compare the diagnostic performance of leading large language models (LLMs) in common and complex clinical scenarios, assessing their potential for enhancing clinical reasoning and diagnostic accuracy in authentic clinical decision-making processes.

We analyzed interoperability-related Real World Testing results to identify whether developers are providing meaningful results with the appropriate context to enable stakeholders to understand the Certified Health IT conformance and interoperability when deployed in production environments.