Clinical applications of machine learning algorithms: beyond the black box

By David Watson, J. Krutzinna, I. Bruce, C. Griffiths, I. McInnes, Michael R. Barnes, Luciano Floridi. British medical journal, 2019

413 citations recorded by the source corpus.

To maximise the clinical benefits of machine learning algorithms, we need to rethink our approach to explanation, argue David Watson and colleagues

Subfields: Artificial Intelligence, Family Practice, Health Informatics

View Paper: https://www.semanticscholar.org/paper/ea65c6b51cafb1c07232bc3c4f57a253f94f92e6

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Citations in the corpus

The ethics of AI in health care: A mapping review. (2020, background): “The vast majority of the population do not have the level of eHealth literacy necessary for this (Kim and Xie, 2017), and those that do (including HCPs) are prevented from gaining this understanding due to the black-box nature of AI-Health algorithms (Watson et al., 2019).”
The explanation game: a formal framework for interpretable machine learning (2020, positive): “The call for more explainable algorithms has been especially urgent in areas like clinical medicine (Watson et al., 2019) and military operations (Gunning, 2017), where user trust is essential and errors could be catastrophic.”
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence (2019, background): “Algorithms are not “just like us” and the temptation to pretend they are can have profound ethical consequences when they are deployed in high-risk domains like finance (Eubanks 2018) and clinical medicine (Watson et al. 2019).”
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, background): “More importantly, the ‘black box’ may undermine patients’ trust in the system.(41) This debate is ongoing, but it is worth noting that many commonly used drugs such as paracetamol have a poorly understood mechanism of action, and most physicians have no more than a basic knowledge of technologies su”
Explainable Artificial Intelligence: a Systematic Review (2020, methodology): “Comprehensibility The quality of the language used by a method for explainability [62, 63, 64, 65, 66, 13, 67, 68, 69]”
Ethics and governance of trustworthy medical artificial intelligence (2023, methodology): “…to machine learning to deep learning, medical AI is currently used in various medical fields such as medical image analysis, disease screening and prediction, clinical decision support, surgical robotics, health management, virtual medical assistants, and aiding in screening drug targets [2–5].”
How to Design AI for Social Good: Seven Essential Factors (2020, background): “Receivers may require explanations about how the AI software came to a decision, especially when they must take action based on that decision (Gregor and Benbasat 1999; Watson et al. 2019).”