• Minerva anestesiologica · Nov 2022

    The past, the present and the future of machine learning and artificial intelligence in anesthesia and Post Anesthesia Care Units (PACU).

    • Romain Pirracchio.
    • Department of Anesthesia and Perioperative Medicine, Zuckerberg San Francisco General Hospital and Trauma Center, University of California San Francisco, San Francisco, CA, USA - romain.pirracchio@ucsf.edu.
    • Minerva Anestesiol. 2022 Nov 1; 88 (11): 961969961-969.

    AbstractOver the past decade, artificial intelligence (AI) has largely penetrated our daily life. Hence, our expectations regarding clinical AI are very high. However, in healthcare and especially in perioperative medicine, the impact of AI is still relatively limited. This is in contrast with an exponential increase in the academic investment and productivity in this field of data science. Implementation challenges are numerous, including technological and regulatory challenges. In addition, the clinical and economic impact of deploying clinical AI at scale is still lacking. However, if these implementation challenges are properly addressed, the potential of AI to profoundly transform our practice is real. If successfully implemented and integrated into the clinical workflow, AI-assisted perioperative medicine could become more preventative and personalized. However, AI implementation is not the final step. New challenges will follow implementation including algorithm maintenance, continuous monitoring, and improvement.

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