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On this episode of the CMAJ Podcast, Dr. Blair Bigham and Dr. Mojola Omole discuss how artificial intelligence (AI) significantly improves the identification of hospital patients at risk of clinical deterioration compared to physician assessments alone. They are joined by Dr. Amol Verma, a general internist at St. Michael’s Hospital in Toronto, an associate professor at the University of Toronto, and the holder of the Temerty Professorship in AI Research and Education, who shares findings from his recent CMAJ article, “Clinical evaluation of a machine learning-based early warning system for patient deterioration”.

Dr. Verma explains how the AI system, ChartWatch, analyzes over 100 variables from a patient’s electronic medical record to predict deterioration more accurately than traditional early warning scores like the NEWS score. He discusses how the integration of AI into clinical workflows improves patient outcomes by complementing human decision-making, leading to better results than relying on physicians or AI alone.

The episode also looks at the potential future of AI in medicine, with Dr. Verma sharing insights on how AI tools should be thoughtfully integrated to support clinicians without overwhelming them. He stresses the need for AI systems to fit seamlessly into clinical workflows, ensuring patient care remains the priority. While AI is currently a tool to assist clinicians, Dr. Verma argues that the full extent of AI's role in healthcare—and its impact on the physician's place within it—remains ultimately unknowable.

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