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Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.

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This study found that evidence for predictive machine learning (ML) algorithms in primary care is often lacking, especially regarding quality criteria. Adopting guidelines like the Dutch AIPA can improve transparency and trust for wider implementation.

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Area of Science:

  • Health Informatics
  • Artificial Intelligence in Healthcare
  • Primary Care Research

Background:

  • Primary healthcare faces challenges from aging populations and staff shortages.
  • Predictive machine learning (ML) offers potential solutions but faces concerns regarding transparency and reporting of validation and implementation effectiveness.
  • There is a need to systematically assess the evidence base for ML algorithms in primary care.

Purpose of the Study:

  • To systematically identify predictive ML algorithms used in primary care.
  • To evaluate the public availability of evidence across the AI life cycle for these algorithms.
  • To assess adherence to reporting guidelines for predictive ML algorithms in primary care.

Main Methods:

  • A comprehensive literature search was conducted across multiple databases (PubMed, Embase, Web of Science, etc.) from January 2000 to July 2023.
  • Searches included peer-reviewed literature and registration databases for FDA- and CE-marked predictive ML algorithms.
  • Evidence availability was assessed against the Dutch AI predictive algorithm (AIPA) guideline requirements across all AI life cycle phases.

Main Results:

  • 43 predictive ML algorithms were identified; 25 were commercially available (FDA/CE-marked).
  • Most algorithms focused on cardiovascular diseases and diabetes, with 81% published in the last 5 years.
  • Evidence availability was lowest for algorithm preparation (19%) and impact assessment (30%), with peer-reviewed literature showing higher availability (45%) than regulatory databases (29%).

Conclusions:

  • There is a critical need to enhance the availability of evidence concerning the quality criteria of predictive ML algorithms in primary care.
  • Implementing the Dutch AIPA guideline can promote transparent and standardized reporting.
  • Improved reporting can foster end-user trust and facilitate the large-scale adoption of these technologies.