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Evaluation of machine learning algorithms for health and wellness applications: A tutorial.

Jussi Tohka1, Mark van Gils2

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Summary

This tutorial guides healthcare professionals on reliably validating artificial intelligence (AI) and machine learning (ML) models. It aims to prevent unrealistic expectations and ensure effective real-world application of AI in health and wellness.

Keywords:
Artificial intelligenceBiomedicineDecision support systemsLife sciencesMachine learningPerformance assessment

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Medical Applications
  • Health Informatics and Data Science

Background:

  • Growing interest in healthcare decision support applications driven by data availability and AI advancements.
  • Unrealistic expectations and potential pitfalls in developing and validating AI/ML methods for healthcare.
  • Challenges in achieving reliable, objective, and generalizable performance assessment of data-analysis methods in health settings.

Purpose of the Study:

  • To provide practical guidance on reliably assessing the performance of AI/ML methods in health and wellness.
  • To foster a deeper understanding of common pitfalls and evaluation criteria in healthcare AI.
  • To offer approaches for efficient performance computation and avoidance of common mistakes.

Main Methods:

  • Focus on understanding the underlying issues in performance evaluation for healthcare AI.
  • Presentation of relevant performance evaluation criteria.
  • Discussion of approaches for computing these criteria and avoiding common errors.

Main Results:

  • Highlights the critical need for robust validation in healthcare AI to manage expectations.
  • Identifies key performance evaluation criteria and methods applicable to health settings.
  • Points out common mistakes to avoid during the development and validation lifecycle.

Conclusions:

  • Emphasizes the importance of reliable validation for successful AI adoption in healthcare.
  • Aims to improve the uptake of AI tools by addressing performance assessment challenges.
  • Provides a foundation for better understanding and application of AI/ML in health and wellness.