The Future AI in Healthcare: A Tsunami of False Alarms or a Product of Experts?

• Type of Material: Article
• Authors: Gari Clifford, Emory University
• Language: English
• Date: 2020-07-27
• Publisher: Association for Computing Machinery (ACM)
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: The authors 2020
• License: Creative Commons Attribution 4.0 International
• Title of Journal or Parent Work: Computers and Society
• ISSN: 0095-2737
• Grant/Funding Information: This work was funded by the Gordon and Betty Moore Foundation, by the National Science Foundation under award number 1822378 (Leveraging Heterogeneous Data Across International Borders in a Privacy Preserving Manner for Clinical Deep Learning) and the National Institutes of Health-sponsored Research Resource for Complex Physiologic Signals (www.physionet.org) (R01GM104987).
• Keywords: affordable; board games; machine learning; accessible; AI; computational power; data storage
• Research Categories: Artificial Intelligence; Computer Science

Abstract

Recent significant increases in affordable and accessible computational power and data storage have enabled machine learning to provide almost unbelievable classification and prediction performances compared to well-trained humans. There have been some promising (but limited) results in the complex healthcare landscape, particularly in imaging. This promise has led some individuals to leap to the conclusion that we will solve an ever-increasing number of problems in human health and medicine by applying `artificial intelligence' to `big (medical) data'. The scientific literature has been inundated with algorithms, outstripping our ability to review them effectively. Unfortunately, I argue that most, if not all of these publications or commercial algorithms make several fundamental errors. I argue that because everyone (and therefore every algorithm) has blind spots, there are multiple `best' algorithms, each of which excels on different types of patients or in different contexts. Consequently, we should vote many algorithms together, weighted by their overall performance, their independence from each other, and a set of features that define the context (i.e., the features that maximally discriminate between the situations when one algorithm outperforms another). This approach not only provides a better performing classifier or predictor but provides confidence intervals so that a clinician can judge how to respond to an alert. Moreover, I argue that a sufficient number of (mostly) independent algorithms that address the same problem can be generated through a large international competition/challenge, lasting many months and define the conditions for a successful event. Finally, I propose introducing the requirement for major grantees to run challenges in the final year of funding to maximize the value of research and select a new generation of grantees.