5 Topic Commentaries

What it takes to scale AI in health care

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  John D. Halamka, MD

  Emergency Medicine; Clinical Informatics

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  Mayo Clinic

  Our responsibility is to ensure these technologies translate into better care for patients. With trusted data and strong governance, AI can help healthcare professionals make more informed decisions in real time, accelerate discovery and improve outcomes.

  Source
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  Cui Tao, PhD

  Biomedical Informatics

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  Mayo Clinic

  If AI is going to become a part of real clinical care, we need rigorous validation, a governance framework, workflow integration and translational maintenance. And that translation from innovation to responsible implementation is exactly where the field is heading next.

  Source
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  Robert M. Califf, MD, MACC

  Cardiology

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  U.S. Food and Drug Administration (FDA)

  The FDA is committed to supporting innovative approaches for the development of medical products by providing an agile, risk-based framework that promotes innovation and ensures the agency’s robust scientific and regulatory standards are met. With the appropriate safeguards in place, artificial intelligence has transformative potential to advance clinical research and accelerate medical product development to improve patient care.

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  Peter D. Stetson, MD, MA

  Internal Medicine; Clinical Informatics

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  Memorial Sloan Kettering Cancer Center

  Our analysis shows governance and quality assurance of AI models are feasible at scale, but require key components for success.

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  Niraj L. Sehgal, MD, MPH

  Hospital Medicine; Internal Medicine

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  Stanford Health Care

  As people get more comfortable using AI-powered technology, it creates a fertile ground to continue adapting other tools for the workplace that support providers, freeing them to better care for patients.

  Source