Document Type
Article
Publication Date
1-9-2023
Publication Title
Online Journal Of Public Health Informatics
Keywords
explainable artificial intelligence, XAI, artificial intelligence, AI, AI medicine, pathology informatics, radiology informatics
ISSN
1947-2579
Volume
15
First Page
e50934
Abstract
Artificial intelligence (AI) applied to medicine offers immense promise, in addition to safety and regulatory concerns. Traditional AI produces a core algorithm result, typically without a measure of statistical confidence or an explanation of its biological-theoretical basis. Efforts are underway to develop explainable AI (XAI) algorithms that not only produce a result but also an explanation to support that result. Here we present a framework for classifying XAI algorithms applied to clinical medicine: An algorithm’s clinical scope is defined by whether the core algorithm output leads to observations (eg, tests, imaging, clinical evaluation), interventions (eg, procedures, medications), diagnoses, and prognostication. Explanations are classified by whether they provide empiric statistical information, association with a historical population or populations, or association with an established disease mechanism or mechanisms. XAI implementations can be classified based on whether algorithm training and validation took into account the actions of health care providers in response to the insights and explanations provided or whether training was performed using only the core algorithm output as the end point. Finally, communication modalities used to convey an XAI explanation can be used to classify algorithms and may affect clinical outcomes. This framework can be used when designing, evaluating, and comparing XAI algorithms applied to medicine.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
NSUWorks Citation
Gniadek, Thomas; Kang, Jason; Theparee, Talent; and Krive, Jacob, "Framework for Classifying Explainable Artificial Intelligence (XAI) Algorithms in Clinical Medicine" (2023). HPD Articles. 297.
https://nsuworks.nova.edu/hpd_facarticles/297
DOI
10.2196/50934
Copyright
©Thomas Gniadek, Jason Kang, Talent Theparee, Jacob Krive
Comments
Funding: The authors acknowledge the support of the NorthShore University Health System in providing the time, encouragement, and article processing fee (APF) funding to complete this manuscript.
Conflict of Interest: J Kang is employed by Abbott Laboratories in their Tranafusion Medicine business unit. TG is employed by Fenwal, a Fresenius Kabi company. The knowledge shared in the manuscript is not influenced by any of these companies. All the other authors declare no conflicts of interest.