From Lifestyle Medicine to AI: ACOG Addresses Workforce Sustainability

Physician well-being and the sustainability of the obstetrics and gynecology workforce emerged as prominent themes at the 2026 ACOG Annual Clinical & Scientific Meeting, with dedicated sessions exploring the systemic determinants of burnout and the strategies available to address them. In the second segment of this program, Maryam Siddiqui, MD, vice chair for clinical affairs and section chief for general obstetrics and gynecology at the University of Chicago Medicine, and Katrina Lee, MD, an assistant professor of obstetrics and gynecology at the University of Chicago Medicine, discuss the conference's lifestyle medicine session, which reframed the conversation around clinician health from individual responsibility to institutional accountability—emphasizing structural changes, such as improved access to nutritious food during overnight shifts, as more effective interventions than exhortations to individual behavior change.

The discussion also addresses a well-attended session on artificial intelligence in obstetrics and gynecology, presented by Melissa Wong, MD. Siddiqui and Lee noted that the session offered a nuanced examination of AI's trajectory in clinical medicine, with attention to both its potential to reduce administrative burden and the risks posed by biased or unrepresentative training data. The presenters emphasized that AI tools are only as reliable as the data and the clinical expertise that inform them, and that obstetrician-gynecologists have a professional responsibility to engage in the development of these technologies to ensure they reflect the populations they serve.

Siddiqui and Lee expressed particular resonance with the session's framing of AI as a tool to restore rather than replace the physician-patient relationship—one that might free clinicians from prior authorization letters and documentation tasks so they can devote more time to the substantive work of care. Both physicians stressed the importance of intentionality in AI adoption, cautioning against a net-zero efficiency model in which time saved is immediately absorbed by new demands, and underscoring the need for ongoing vigilance against perpetuating systemic inequities through flawed algorithms.