Harnessing AI in critical care: opportunities, challenges and key steps for success

BACKGROUND: The integration of artificial intelligence (AI) into critical care offers significant potential to enhance early diagnosis, predict patient deterioration, personalise treatment and inform clinical decision-making. Despite this promise, AI adoption in the intensive care unit (ICU) faces challenges, as illustrated by the limited number of AI tools which have been approved for clinical use and/or successfully deployed in critical care. METHODS: Aims of the review are to provide a synthesis of research on AI in critical care; assess approved tools; and consider challenges and opportunities, focusing on the different phases of the AI algorithm lifecycle in the ICU, including data collection, modelling, validation, implementation and post-deployment monitoring. Peer-reviewed publications were searched using terms relevant to AI and critical care spanning the years 2000-2025. RESULTS: Research on AI applications in the ICU is characterised by significant limitations including suboptimal data quality, retrospective analyses and a paucity of prospective validation studies. The few AI algorithms that have received Food and Drug Administration approval for use in the ICU have not gained widespread clinical adoption due, in part, to issues such as lack of user trust, integration challenges, unclear clinical impact or performance drift. Overcoming these barriers will require a structured approach that addresses the key challenges identified in the AI lifecycle, including the integration of real-world data, post-deployment performance monitoring, governance and ethical considerations. A successful implementation pathway should consider realistic goal-setting, greater model explainability, improved workflow integration and active end-user involvement. CONCLUSIONS: Advancing critical care with AI will require special attention to interdisciplinary collaboration, robust validation frameworks and adaptive governance models. The need for rigorous scientific evaluation needs to be balanced with the pressure for rapid deployment. Ensuring transparency, safety and alignment with clinical workflows will be critical to achieving meaningful AI integration in critical care.