A Hazard-Aware Metric for Ordinal Multi-Class Classification in Pathology

Authors

  • David Jin DoD Chief Digital and Artificial Intelligence Office
  • Ariel Kapusta MITRE Corporation
  • Patrick A. Minot MITRE Corporation
  • Niels H. Olson Defense Innovation Unit
  • Joseph H. Rosenthal Henry M. Jackson Foundation for the Advancement of Military Medicine
  • Jansen N. Seheult Mayo Clinic
  • Michelle Stram NYU Grossman School of Medicine

DOI:

https://doi.org/10.1609/aaaiss.v2i1.27680

Keywords:

Medicine, ML, AI, ESI, Ordinal, Metrics, Pathology, Clinical, Error Severity Index, Hazard-aware, AI Systems, Multi-class Classification, Diagnosis, Trustworthy AI, Healthcare, Machine Learning

Abstract

Artificial Intelligence (AI) for decision support and diagnosis in pathology could provide immense value to society, improving patient outcomes and alleviating workload demands on pathologists. However, this potential cannot be realized until sufficient methods for testing and evaluation of such AI systems are developed and adopted. We present a novel metric for evaluation of multi-class classification algorithms for pathology, Error Severity Index (ESI), to address the needs of pathologists and pathology lab managers in evaluating AI systems.
AAAI Fall Symposium 2023 Proceedings Cover

Downloads

Published

2024-01-22

Issue

Vol. 2 No. 1: Proceedings of the 2023 AAAI Fall Symposia

Section

Assured and Trustworthy Human-centered AI (ATHAI)