Top three priorities for artificial intelligence integration into emergency, critical, and perioperative medicine: an interdisciplinary clinical expert consensus

Abstract

BACKGROUND: Artificial intelligence (AI) is increasingly applied in emergency, critical, and perioperative medicine, yet its implementation remains limited and fragmented. Variability in digital maturity, governance, and clinical readiness continues to challenge large-scale adoption. METHODS: A multidisciplinary expert consensus was conducted to identify key priorities for the safe and effective integration of AI in high-acuity settings. The consensus process included an independent literature review, group discussion, and blinded online voting. Priorities that reached at least 70% agreement on a 9-point Likert scale were considered consensual. RESULTS: Three priorities reached the predefined consensus threshold: 1. Digitalization and sharing of healthcare data (92.3% agreement): Digitalize the Emergency, Critical, and Perioperative Department patient journey by adopting a shared standard structure for electronic medical records that is optimized for data sharing and interoperability. 2. Efficacy and validation of AI models (93.4% agreement): Use only AI models that have demonstrated impact on patient outcomes, decision-making processes, or risk stratification validated through prospective studies or randomized clinical trials. 3. AI education of healthcare professionals (100% agreement): Healthcare professionals must acquire a digital health literacy level appropriate for their specific role, with individuals with leadership and management roles having more in-depth knowledge. CONCLUSIONS: The consensus identifies three strategic priorities to guide the integration of AI in high-acuity settings. Together, they outline a pragmatic roadmap for translating AI potential into safe and clinically meaningful practice.

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