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Articles by David Chartash
Consensus framework for the validation of generative AI: call for collaborators on the Validation Accords
There is growing interest in integrating generative artificial intelligence (GAI) models into healthcare systems1. Although large language models (LLMs) have dominated the field, other GAI technology such as large multimodal models are gaining popularity2. Researchers are applying GAI models for clinical note-writing and to produce clinical guidance for patients in terms of health prevention, screening, diagnosis, prognosis, triage, surveillance, and treatment3,4.
Staffing Trends Amid Boarding Challenges: A Five-Year Analysis (2019–2023)
Presentations: Society of Academic Emergency Medicine, 2025. Supervising Editor: Jesse M. Pines M.Blenden, R.Ghosh, D.Chartash, et al., “Staffing Trends Amid Boarding Challenges: A Five-Year Analysis (2019–2023),” Academic Emergency Medicine33, no. 4 (2026): e70281, https://doi.org/10.1111/acem.70281.
A Snapshot of Family Medicine Physician Engagement with State Policy: Findings from the 2023 CERA Survey
Advocacy Chi-Square Test Family Medicine Health Policy Logistic Regression Non-Parametric Statistics Physicians Social Change Introduction The American Medical Association recommends physicians “advocate for social, economic, educational, and political changes that ameliorate suffering and contribute to human well-being.”1 One way to fulfill this obligation is through civic engagement with health policy (advocacy).
Family Medicine Must Prepare for Artificial Intelligence
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Dispelling the magic of artificial intelligence in medical education
• , , . A systematic review of large language models and their implications in medical education . Med Educ. ; - . doi:10.1111/medu.15402 • . Profiles of the future; an inquiry into the limits of the possible . Rev. ed. Harper & Row; : . • , , , , , . What do medical students actually need to know about artificial intelligence . Npj Digital Med. ; ( ): . doi:10.1038/s41746-020-0294-7 • , , , .
The Scope of Multimorbidity in Family Medicine: Identifying Age Patterns Across the Lifespan
Abstract Introduction: Multimorbidity rates are both increasing in prevalence across age ranges, and also increasing in diagnostic importance within and outside the family medicine clinic. Here we aim to describe the course of multimorbidity across the lifespan. Methods: This was a retrospective cohort study across 211,953 patients from a large northeastern health care system. Past medical histories were collected in the form of ICD-10 diagnostic codes.
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