Application Status and Intelligent Development Prospects of Case-Based Learning in Standardized Training for Obstetric Residents: A Narrative Review.

Artificial intelligence (AI) is entering clinical workforces at a pivotal moment in team-based care and the transition to value-based care, bringing transformative potential to enhance and augment care teams, not by replacing clinicians, but by amplifying their expertise and extending their reach. AI already shows promise in improving diagnostic accuracy, strengthening care coordination, and increasing documentation efficiency. Yet without clear reimbursement pathways, governance standards, and safeguards for equity and safety, AI risks widening existing disparities and creating opaque decision processes that undermine clinician trust. Realizing AI's benefits will depend on implementing tools transparently, auditing them rigorously, and ensuring they align with value-based care rather than functioning as unregulated add-ons to already burdened workflows. Adoption, however, remains uneven because the U.S. healthcare system still relies heavily on fee-for-service reimbursement and lacks clear mechanisms to evaluate, compensate, or govern AI-enabled clinical work. As payment shifts toward value-based care (VBC), AI could strengthen team-based, outcomes-oriented practice, but only if supporting structures evolve. Emerging evidence suggests that scalable implementation depends on four interdependent elements: (1) reimbursement pathways that recognize clinician-reviewed AI outputs, (2) EHR integration that supports auditability and workflow fit, (3) governance standards that promote accuracy, safety, and equity, and (4) ongoing performance monitoring to assess generalizability and detect bias (). This paper examines how these elements shape AI's role within VBC and the policy conditions needed for safe and equitable adoption. The broader context for AI adoption is the restructuring of U.S. healthcare financing. Fee-for-service (FFS) historically rewarded service volume, fragmented care, and overlooked behavioral and social drivers of health (). AI is increasingly viewed not as a replacement for clinicians but as a computational extension of team-based care.