Artificial Intelligence and Enhanced Recovery After Surgery as Patient Safety Strategies for Perioperative Care in Resource-Limited Settings

Artificial intelligence (AI) and Enhanced Recovery After Surgery (ERAS) are increasingly discussed as strategies for improving perioperative quality, efficiency, and recovery, but their implementation in resource limited settings requires a clear patient safety framework. In this commentary, resource limited settings refer to perioperative systems constrained by shortages of trained workforce, essential equipment, monitoring capacity, reliable data infrastructure, financing, or governance support. This commentary argues that AI and ERAS should be viewed as complementary rather than competing approaches. ERAS provides a structured pathway for standardizing perioperative care, while AI may support risk stratification, clinical decision support, monitoring, adherence tracking, and operational efficiency. Perioperative care is a suitable field for AI integration because it is time-sensitive, multidisciplinary, data-rich, and highly dependent on coordinated decisions across the preoperative, intraoperative, and postoperative continuum. However, digital tools cannot compensate for absent safety infrastructure, weak governance, or poor-quality data. A pragmatic strategy for low-resource settings is therefore to first establish essential perioperative safety standards, implement context-adapted ERAS elements, and then selectively deploy ethically governed AI applications with human oversight and local validation. Framed in this way, AI-supported ERAS pathways may strengthen patient safety, recovery, and risk management while supporting broader health system resilience.