Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare

By Jean Feng, Rachael V. Phillips, Ivana Malenica, Andrew M. Bishara, Alan E. Hubbard, Leo Anthony Celi, Romain Pirracchio. npj Digital Medicine, 2022

332 citations recorded by the source corpus.

Machine learning (ML) and artificial intelligence (AI) algorithms have the potential to derive insights from clinical data and improve patient outcomes. However, these highly complex systems are sensitive to changes in the environment and liable to performance decay. Even after their successful integration into clinical practice, ML/AI algorithms should be continuously monitored and updated to ensure their long-term safety and effectiveness. To bring AI into maturity in clinical care, we advocate for the creation of hospital units responsible for quality assurance and improvement of these algorithms, which we refer to as “AI-QI” units. We discuss how tools that have long been used in hospital quality assurance and quality improvement can be adapted to monitor static ML algorithms. On the other hand, procedures for continual model updating are still nascent. We highlight key considerations when choosing between existing methods and opportunities for methodological innovation.

Subfields: Artificial Intelligence, Epidemiology, Health Informatics

View Paper: https://www.semanticscholar.org/paper/bc561f967f300a14d61c35247a908318956a502b

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In Artificial Intelligence

  1. Mohamad Forouzanfar
  2. Shiva Maleki Varnosfaderani

In Health Informatics

  1. Mohamad Forouzanfar
  2. Shiva Maleki Varnosfaderani

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