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Journal of pathology informatics
Dec, 2024;15 100347.

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities.

Ricardo Gonzalez, Ashirbani Saha, Clinton J V Campbell, Peyman Nejat, Cynthia Lokker, Andrew P Norgan
Author Information
  1. Ricardo Gonzalez: DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada.
  2. Ashirbani Saha: Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
  3. Clinton J V Campbell: William Osler Health System, Brampton, Ontario, Canada.
  4. Peyman Nejat: Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
  5. Cynthia Lokker: Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
  6. Andrew P Norgan: Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
PMID: 38162950 DOI: 10.1016/j.jpi.2023.100347

Abstract

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.

Keywords

Artificial intelligence Computer-assisted Image processing Learning health system Machine learning Pathology

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