This paper explores ethical issues raised by whole slide image-based computational pathology. After briefly giving examples drawn from some recent literature of advances in this field, we consider some ethical problems it might be thought to pose. These arise from (1) the tension between artificial intelligence (AI) research—with its hunger for more and more data—and the default preference in data ethics and data protection law for the minimisation of personal data collection and processing; (2) the fact that computational pathology lends itself to kinds of data fusion that go against data ethics norms and some norms of biobanking; (3) the fact that AI methods are esoteric and produce results that are sometimes unexplainable (the so-called ‘black box’problem) and (4) the fact that computational pathology is particularly dependent on scanning technology manufacturers with interests of their own in profit-making from data collection. We shall suggest that most of these issues are resolvable.
- human tissue
- information technology
- scientific research
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Contributors TS will act as guarantor. He satisfies all of the BMJ criteria for authorship. NR satisfies all those criteria but has been particularly involved with section I. CV satisfies all the criteria, and has been particularly involved with section III.
Funding This study was funded by Innovate UK (Grant number: 18181).
Competing interests All authors are members of the Innovate-UK funded Pathlake Centre of Excellence consortium. The PathLAKE (Pathology image data Lake for Analytics, Knowledge and Education) is a cross-faculty research consortium comprising researchers from the University of Warwick, University Hospitals Coventry and Warwickshire NHS Trust, and Royal Philips to create a national centre of excellence in AI in pathology, linked to five digitised NHS pathology labs. The cutting-edge AI technologies will assist pathologists in diagnosing cancer more efficiently and selecting the optimal treatment for cancer patients.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; externally peer reviewed.
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