Concordance as evidence in the Watson for Oncology decision-support system

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Tupasela , A & Di Nucci , E 2020 , ' Concordance as evidence in the Watson for Oncology decision-support system ' , AI & Society , vol. 35 , no. 4 , pp. 811-818 .

Title: Concordance as evidence in the Watson for Oncology decision-support system
Author: Tupasela, Aaro; Di Nucci, Ezio
Contributor organization: Academic Disciplines of the Faculty of Social Sciences
Date: 2020-12
Language: eng
Number of pages: 8
Belongs to series: AI & Society
ISSN: 0951-5666
Abstract: Machine learning platforms have emerged as a new promissory technology that some argue will revolutionize work practices across a broad range of professions, including medical care. During the past few years, IBM has been testing its Watson for Oncology platform at several oncology departments around the world. Published reports, news stories, as well as our own empirical research show that in some cases, the levels of concordance over recommended treatment protocols between the platform and human oncologists have been quite low. Other studies supported by IBM claim concordance rates as high as 96%. We use the Watson for Oncology case to examine the practice of using concordance levels between tumor boards and a machine learning decision-support system as a form of evidence. We address a challenge related to the epistemic authority between oncologists on tumor boards and the Watson Oncology platform by arguing that the use of concordance levels as a form of evidence of quality or trustworthiness is problematic. Although the platform provides links to the literature from which it draws its conclusion, it obfuscates the scoring criteria that it uses to value some studies over others. In other words, the platform "black boxes" the values that are coded into its scoring system.
Subject: 113 Computer and information sciences
Artificial intelligence
Clinical trials
Decision support
Machine learning
Watson for Oncology
Peer reviewed: Yes
Rights: cc_by
Usage restriction: openAccess
Self-archived version: publishedVersion

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