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Rethinking PICO in the Machine Learning Era: ML-PICO.

Xinran Liu1,2, James Anstey1, Ron Li3

  • 1Division of Hospital Medicine, University of California, San Francisco, San Francisco, California, United States.

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Summary

Clinicians need a new framework to evaluate machine learning (ML) papers that develop new ML models. The proposed ML-PICO framework aids in assessing these complex medical AI tools.

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Area of Science:

  • Medical informatics
  • Artificial intelligence in healthcare

Background:

  • Machine learning (ML) is increasingly relevant to clinical practice, yet clinicians often lack formal training.
  • Existing clinical research appraisal frameworks like PICO are insufficient for evaluating ML models, which function as diagnostic tests.

Purpose of the Study:

  • To introduce a novel framework, ML-PICO (Machine Learning, Population, Identification, Crosscheck, Outcomes), for systematically assessing medical ML papers.
  • To provide clinicians with a practical tool for evaluating the quality and utility of newly developed ML models in healthcare.

Main Methods:

  • The ML-PICO framework is detailed, outlining its components for appraising ML model development papers.
  • The application of ML-PICO to healthcare ML literature is described.

Main Results:

  • The ML-PICO framework offers a structured approach to critically evaluate ML models.
  • This framework addresses the limitations of traditional PICO when applied to diagnostic ML models.

Conclusions:

  • The ML-PICO framework is essential for clinicians to effectively read and assess medical ML papers.
  • This tool aims to improve the understanding and utilization of ML advancements in clinical settings.