An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study.

Journal: Journal of medical Internet research
PMID: 37672333

Abstract

BACKGROUND: Predicting the likelihood of success of weight loss interventions using machine learning (ML) models may enhance intervention effectiveness by enabling timely and dynamic modification of intervention components for nonresponders to treatment. However, a lack of understanding and trust in these ML models impacts adoption among weight management experts. Recent advances in the field of explainable artificial intelligence enable the interpretation of ML models, yet it is unknown whether they enhance model understanding, trust, and adoption among weight management experts.

Authors

  • Glenn J Fernandes
    Department of Computer Science, Northwestern University, Evanston, IL, United States.
  • Arthur Choi
    Department of Computer Science, Kennesaw State University, Kennesaw, GA, United States.
  • Jacob Michael Schauer
    Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
  • Angela F Pfammatter
    Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
  • Bonnie J Spring
    Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States.
  • Adnan Darwiche
    Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, United States.
  • Nabil I Alshurafa
    Department of Computer Science, Northwestern University, Evanston, IL, United States.

Keywords

External Resources

Popular Topics
Recent Journals