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Importance: Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed. Observations: While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings. Conclusion and Relevance: The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed. This article outlines the need for a nationwide public-private network of assurance laboratories that would aid in the evaluation and development of artificial intelligence (AI) in health care. [ABSTRACT FROM AUTHOR]

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 applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost. Observations: Applications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks. Conclusions and Relevance: The creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments. This special communication discusses whether large language models (LLMs) are being trained with the right kind of self-supervision and if the purported value propositions of using LLMs in medicine are being verified. [ABSTRACT FROM AUTHOR]

This Viewpoint discusses a recent executive order by US President Joe Biden about the development and implementation of AI, including the role of government vs the private sector and how the order may affect health care. [ABSTRACT FROM AUTHOR]

This Viewpoint reviews conventional ways of assessing performance of machine learning models to diagnose or predict outcomes, but emphasizes that if machine learning is to improve patient care the models must be evaluated for their utility in improving clinical decisions taking into account the range of decisions clinicians can take, the cost and efficacy of those options, and the likelihood that patients will follow the recommended decisions. [ABSTRACT FROM AUTHOR]

In this Viewpoint, Verghese and colleagues discuss the promise and challenges of integrating artificial intelligence (AI) into clinical care and argue that AI will always need guidance by physicians if it is to improve the care of patients. [ABSTRACT FROM AUTHOR]

The article discusses the lack of U.S. Centers for Disease Control and Prevention (CDC) funding for research on gun-related deaths in relation to the funding and publication of research on the topic in relation to U.S. gun violence mortality. Topics include the number of publications on various causes of death, the lack of funding for injury research, and the disproportionality of lower funding for gun violence research.

The authors offer insights on the evolution of electronic health records (EHR). They discuss the reason behind the increase in physician's dependence on EHR for diagnosis, illness monitoring and treatment, the failure of the evolution of EHRs to keep pace with technology used to track and visualize information in other domains of modern life, and shortcomings of EHRs.

This study describes the prevalence of SARS-CoV-2 co-infection with noncoronavirus respiratory pathogens in a sample of symptomatic patients undergoing PCR testing in March 2020. [ABSTRACT FROM AUTHOR]