AI in medical diagnosis : AI prediction & human judgment
Göndöcs, Dóra and Dörfler, Viktor (2024) AI in medical diagnosis : AI prediction & human judgment. Artificial Intelligence in Medicine, 149. 102769. ISSN 1873-2860 (https://doi.org/10.1016/j.artmed.2024.102769)
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Abstract
AI has long been regarded as a panacea for decision-making and many other aspects of knowledge work; as something that will help humans get rid of their shortcomings. We believe that AI can be a useful asset to support decision-makers, but not that it should replace decision-makers. Decision-making uses algorithmic analysis, but it is not solely algorithmic analysis; it also involves other factors, many of which are very human, such as creativity, intuition, emotions, feelings, and value judgments. We have conducted semi-structured open-ended research interviews with 17 dermatologists to understand what they expect from an AI application to deliver to medical diagnosis. We have found four aggregate dimensions along which the thinking of dermatologists can be described: the ways in which our participants chose to interact with AI, responsibility, 'explainability', and the new way of thinking (mindset) needed for working with AI. We believe that our findings will help physicians who might consider using AI in their diagnosis to understand how to use AI beneficially. It will also be useful for AI vendors in improving their understanding of how medics want to use AI in diagnosis. Further research will be needed to examine if our findings have relevance in the wider medical field and beyond.
ORCID iDs
Göndöcs, Dóra and Dörfler, Viktor ORCID: https://orcid.org/0000-0001-8314-4162;-
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Item type: Article ID code: 87881 Dates: DateEvent31 March 2024Published23 January 2024Published Online14 January 2024Accepted20 June 2023SubmittedSubjects: Science > Mathematics > Electronic computers. Computer science > Other topics, A-Z > Human-computer interaction Department: Strathclyde Business School > Management Science Depositing user: Pure Administrator Date deposited: 23 Jan 2024 16:31 Last modified: 17 Dec 2024 19:42 URI: https://strathprints.strath.ac.uk/id/eprint/87881