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A proposal for developing a platform that evaluates algorithmic equity and accuracy.

Paul Cerrato1, John Halamka2, Michael Pencina3

  • 1Paul Cerrato is Senior Research Analyst/Communications Specialist, Mayo Clinic Platform; John Halamka is President of Mayo Clinic Platform, Mayo Clinic Rochester, Rochester, Minnesota, USA cerrato.paul@mayo.edu.

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Summary
This summary is machine-generated.

Healthcare artificial intelligence (AI) shows promise, but evidence for equitable and accurate algorithms is lacking. This study proposes solutions, including bias evaluation and standardized AI testing, to ensure trustworthy AI in medicine.

Keywords:
BMJ health informaticsartificial intelligencecomputer-assisteddecision makingdeep learninginformatics

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Health Equity

Background:

  • The rapid advancement of artificial intelligence (AI) in healthcare outpaces scientific validation of its equity and accuracy.
  • Current machine learning algorithms face scrutiny regarding biases related to race, gender, and socioeconomic status.
  • A lack of prospective studies and multisite validation hinders the reliable assessment of AI tools.

Purpose of the Study:

  • To examine algorithmic biases and accuracy issues in healthcare AI.
  • To propose solutions for ensuring the equitable and accurate deployment of AI in medical diagnostics and therapeutics.
  • To introduce standardized evaluation methods for healthcare AI products and services.

Main Methods:

  • Analysis of algorithmic biases concerning race, gender, and socioeconomic status.
  • Review of accuracy challenges, including the need for prospective studies and multisite validation.
  • Description of a project evaluating 35.1 billion healthcare records for bias (Mayo Clinic, Duke University, Change Healthcare).

Main Results:

  • Identification of significant gaps in scientific evidence supporting the equity and accuracy of healthcare AI.
  • Proposal of 'Ingredients' style labels for AI algorithms to enhance transparency.
  • Development of a comprehensive AI evaluation and testing system for clinical use.

Conclusions:

  • Urgent need for rigorous scientific evidence to support healthcare AI claims.
  • Proposed solutions aim to improve clinician judgment and trust in AI technologies.
  • Standardized testing protocols are crucial for assessing AI input data, dataset composition, validation techniques, bias, and performance.