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The article discusses the potential risks of integrating large language models (LLMs) into electronic health records (EHRs), focusing on how LLM-generated text might degrade the quality of medical documentation. Topics discussed include the potential for LLMs to reduce information density, hinder clinical reasoning, and create chart clutter, as well as the challenges of relying on Artificial Intelligence (AI)-generated content for decision support in healthcare.

The document titled "THE AUTHORS REPLY" is a correspondence published in the New England Journal of Medicine. The authors discuss the influence of commercial interests on AI models and the need for third-party review, disclosure, and regulatory guidelines. They emphasize the importance of addressing the global alignment problem and suggest efforts such as the Responsible AI for Social and Ethical Healthcare (RAISE) symposium. The authors also acknowledge the value of clinical experience in assessing and preventing tragic outcomes, while recognizing the potential of AI to democratize access to clinical expertise without replacing the doctor-patient relationship. [Extracted from the article]

BACKGROUND Adjustment for race is discouraged in lung-function testing, but the implications of adopting race-neutral equations have not been comprehensively quantified. METHODS We obtained longitudinal data from 369,077 participants in the National Health and Nutrition Examination Survey, U.K. Biobank, the Multi-Ethnic Study of Atherosclero- sis, and the Organ Procurement and Transplantation Network. Using these data, we compared the race-based 2012 Global Lung Function Initiative (GLI-2012) equations with race-neutral equations introduced in 2022 (GLI-Global). Evaluated outcomes included national projections of clinical, occupational, and financial reclassifica- tions; individual lung-allocation scores for transplantation priority; and concordance statistics (C statistics) for clinical prediction tasks. RESULTS Among the 249 million persons in the United States between 6 and 79 years of age who are able to produce high-quality spirometric results, the use of GLI-Global equa- tions may reclassify ventilatory impairment for 12.5 million persons, medical im- pairment ratings for 8.16 million, occupational eligibility for 2.28 million, grading of chronic obstructive pulmonary disease for 2.05 million, and military disability compensation for 413,000. These potential changes differed according to race; for example, classifications of nonobstructive ventilatory impairment may change dra- matically, increasing 141% (95% confidence interval [CI], 113 to 169) among Black persons and decreasing 69% (95% CI, 63 to 74) among White persons. Annual disability payments may increase by more than $1 billion among Black veterans and decrease by $0.5 billion among White veterans. GLI-2012 and GLI-Global equa- tions had similar discriminative accuracy with regard to respiratory symptoms, health care utilization, new-onset disease, death from any cause, death related to respiratory disease, and death among persons on a transplant waiting list, with differences in C statistics ranging from -0.008 to 0.011. CONCLUSIONS The use of race-based and race-neutral equations generated similarly accurate predic- tions of respiratory outcomes but assigned different disease classifications, occupa- tional eligibility, and disability compensation for millions of persons, with effects diverging according to race. (Funded by the National Heart Lung and Blood Institute and the National Institute of Environmental Health Sciences.). [ABSTRACT FROM AUTHOR]

Several letters to the editor are presented in response to the article "Urinary-Cell mRNA Profile and Acute Cellular Rejection in Kidney Allografts," by M. Suthanthiran et al. in the July 4, 2013 issue.