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AI and clinical decision making: The limitations and risks of computational reductionism in bowel cancer screening


Ameen, S and Wong, M-C and Yee, K-C and Turner, P, AI and clinical decision making: The limitations and risks of computational reductionism in bowel cancer screening, Applied Sciences, 12, (7) pp. 1-25. ISSN 2076-3417 (2022) [Refereed Article]

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© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) license, (

DOI: doi:10.3390/app12073341


Advances in artificial intelligence in healthcare are frequently promoted as ‘solutions’ to improve the accuracy, safety, and quality of clinical decisions, treatments, and care. Despite some diagnostic success, however, AI systems rely on forms of reductive reasoning and computational determinism that embed problematic assumptions about clinical decision-making and clinical practice. Clinician autonomy, experience, and judgement are reduced to inputs and outputs framed as binary or multi-class classification problems benchmarked against a clinician’s capacity to identify or predict disease states. This paper examines this reductive reasoning in AI systems for colorectal cancer (CRC) to highlight their limitations and risks: (1) in AI systems themselves due to inherent biases in (a) retrospective training datasets and (b) embedded assumptions in underlying AI architectures and algorithms; (2) in the problematic and limited evaluations being conducted on AI systems prior to system integration in clinical practice; and (3) in marginalising socio-technical factors in the context-dependent interactions between clinicians, their patients, and the broader health system. The paper argues that to optimise benefits from AI systems and to avoid negative unintended consequences for clinical decision-making and patient care, there is a need for more nuanced and balanced approaches to AI system deployment and evaluation in CRC.

Item Details

Item Type:Refereed Article
Keywords:artificial intelligence, colorectal cancer, machine learning, socio-technical design, patient outcomes
Research Division:Biomedical and Clinical Sciences
Research Group:Clinical sciences
Research Field:Gastroenterology and hepatology
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Ameen, S (Mr Saleem Ameen)
UTAS Author:Wong, M-C (Dr Ming Wong)
UTAS Author:Yee, K-C (Dr Kwang Yee)
UTAS Author:Turner, P (Associate Professor Paul Turner)
ID Code:155511
Year Published:2022
Web of Science® Times Cited:2
Deposited By:Medicine
Deposited On:2023-02-26
Last Modified:2023-03-22
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