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Updating methods for artificial intelligence-based clinical prediction models: a scoping review.

Lotta M Meijerink1, Zoë S Dunias1, Artuur M Leeuwenberg1

  • 1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

Journal of Clinical Epidemiology
|December 11, 2024
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Summary

This study reviews methods for updating artificial intelligence (AI)-based clinical prediction models with new data. It highlights various techniques, primarily focusing on neural networks, to adapt models for diverse healthcare applications.

Keywords:
Artificial intelligenceKnowledge transferMachine learningModel updatingPrediction modelsTransfer learning

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Area of Science:

  • * Medical Artificial Intelligence (AI)
  • * Clinical Prediction Modeling
  • * Machine Learning in Healthcare

Background:

  • * AI-based prediction models are vital in healthcare but may not generalize to new settings.
  • * Developing new models for each context is inefficient and wasteful.
  • * Updating existing AI models offers a practical solution for improved performance and resource utilization.

Purpose of the Study:

  • * To provide a comprehensive overview of methods for updating AI-based clinical prediction models.
  • * To categorize and describe existing model updating techniques and their use cases.
  • * To guide researchers in adapting AI models to new data and clinical scenarios.

Main Methods:

  • * Comprehensive literature search of Scopus and Embase up to August 2022.
  • * Focused on AI-based prediction models updated with new data in the medical domain.
  • * Excluded regression-based updating methods; categorized identified AI updating methods.

Main Results:

  • * 78 articles were included, predominantly focusing on neural network updates (93.6%) using medical images (65.4%).
  • * Common use cases include adapting broad models to specialized tasks, addressing data drift, and handling cross-center variations.
  • * Identified methods categorized into neural network-specific (92.3%), ensemble-specific (2.5%), and model-agnostic (9.0%).

Conclusions:

  • * Numerous methods exist for updating AI-based prediction models across various use cases.
  • * Updating methods for non-neural network AI models (e.g., random forests) are under-researched in clinical settings.
  • * This review serves as guidance to improve the reuse, quality, and efficiency of AI models in healthcare.