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

doi:10.1016/j.jpi.2023.100347

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

J Pathol Inform. 2023 Nov 4:15:100347. doi: 10.1016/j.jpi.2023.100347. eCollection 2024 Dec.

Authors

Ricardo Gonzalez^{1

2}, Ashirbani Saha^{3

4}, Clinton J V Campbell^{5

6}, Peyman Nejat⁷, Cynthia Lokker⁸, Andrew P Norgan⁹

Affiliations

¹ DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada.
² Division of Computational Pathology and Artificial Intelligence, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
³ Department of Oncology, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
⁴ Escarpment Cancer Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada.
⁵ William Osler Health System, Brampton, Ontario, Canada.
⁶ Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada.
⁷ Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, United States.
⁸ Health Information Research Unit, Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada.
⁹ Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.

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.

Publication types

Review