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Toward a responsible future: recommendations for AI-enabled clinical decision support.

Steven Labkoff1,2, Bilikis Oladimeji3, Joseph Kannry4

  • 1Quantori, Boston, MA 02142, United States.

Journal of the American Medical Informatics Association : JAMIA
|September 26, 2024
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in healthcare requires robust frameworks for safe and trustworthy clinical decision support (CDS) systems. Recommendations include validation, monitoring, and training to ensure responsible AI integration.

Keywords:
algorithmic transparencyartificial intelligenceclinical decision supportclinician AI competenciespatient safety

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Integrating AI into healthcare offers significant potential for clinical decision-making.
  • Key challenges include ensuring AI trustworthiness, mitigating bias, and maintaining patient safety.
  • Lack of standardized methodologies for AI tool evaluation hinders effective pre- and post-deployment assessment.

Purpose of the Study:

  • To propose practical methods, rules, and guidelines for the safe and effective development, testing, supervision, and use of AI in clinical decision support (CDS) systems.
  • To address challenges in the trusted application of AI-enabled CDS in medical practice.

Main Methods:

  • A working group, co-sponsored by the Division of Clinical Informatics at Beth Israel Deaconess Medical Center and the American Medical Informatics Association, convened in May 2023.
  • Consensus-building involved webinars and a 2-day workshop in September 2023, with over 200 industry stakeholders.
  • Qualitative analysis and a 4-month iterative consensus process were used to identify challenges and propose solutions.

Main Results:

  • Key recommendations focus on building safe and trustworthy AI-CDS systems.
  • Developing robust validation, verification, and certification processes is crucial.
  • Establishing national-level safety monitoring and reporting mechanisms, alongside end-user training, is essential.

Conclusions:

  • A comprehensive framework emphasizing trustworthiness, transparency, and safety is necessary for AI-enabled CDS.
  • This framework must cover model training, explainability, validation, certification, monitoring, and continuous evaluation.
  • Responsible AI integration requires collective stakeholder effort, robust safety measures, and ongoing innovation, with future steps including piloting trust mechanisms and establishing best practices.