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- McKay, Francis, et al.
(author)
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Artificial intelligence and medical research databases: ethical review by data access committees
- 2023
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In: BMC Medical Ethics. - : BMC. - 1472-6939. ; 24:1
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Research review (peer-reviewed)abstract
- BackgroundIt has been argued that ethics review committees-e.g., Research Ethics Committees, Institutional Review Boards, etc.- have weaknesses in reviewing big data and artificial intelligence research. For instance, they may, due to the novelty of the area, lack the relevant expertise for judging collective risks and benefits of such research, or they may exempt it from review in instances involving de-identified data.Main bodyFocusing on the example of medical research databases we highlight here ethical issues around de-identified data sharing which motivate the need for review where oversight by ethics committees is weak. Though some argue for ethics committee reform to overcome these weaknesses, it is unclear whether or when that will happen. Hence, we argue that ethical review can be done by data access committees, since they have de facto purview of big data and artificial intelligence projects, relevant technical expertise and governance knowledge, and already take on some functions of ethical review. That said, like ethics committees, they may have functional weaknesses in their review capabilities. To strengthen that function, data access committees must think clearly about the kinds of ethical expertise, both professional and lay, that they draw upon to support their work.ConclusionData access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise.
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| 2. |
- McKay, Francis, et al.
(author)
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Inalienable data: Ethical imaginaries of de-identified health data ownership
- 2023
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In: SSM-QUALITATIVE RESEARCH IN HEALTH. - : ELSEVIER. - 2667-3215. ; 4
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Journal article (peer-reviewed)abstract
- Many legal, ethical, and regulatory frameworks allow de-identified health data to be shared for research without patients opt-in consent. However, there may be public concerns about this practice, as people may feel they should have some say in how such data is used. This paper introduces the concept of the "inalienability of deidentified data," to describe a key assumption underlying that public concern and preference. The assumption, derived from ethnographic research with public and professional stakeholders in AI driven medical image analysis over the past two years, refers to a sense of felt ownership over de-identified health data, even where the subject has been obscured as referent and no clear legal rights of data ownership otherwise exist. The concept is important to medical ethics because it underpins public expectations regarding the rights people should have over the sharing of medical data (including expectations for consent). We note that where those expectations go counter to current legal and bioethical frameworks for de-identified data sharing, they provide a challenge for public support of big data and artificial intelligence driven health research.
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| 3. |
- McKay, Francis, et al.
(author)
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The ethical challenges of artificial intelligence-driven digital pathology
- 2022
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In: The journal of pathology. Clinical research. - : Wiley. - 2056-4538. ; 8:3, s. 209-216
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Research review (peer-reviewed)abstract
- Digital pathology - the digitalisation of clinical histopathology services through the scanning and storage of pathology slides - has opened up new possibilities for health care in recent years, particularly in the opportunities it brings for artificial intelligence (Al)-driven research. Recognising, however, that there is little scholarly debate on the ethics of digital pathology when used for Al research, this paper summarises what it sees as four key ethical issues to consider when deploying Al infrastructures in pathology, namely, privacy, choice, equity, and trust. The themes are inspired from the authors experience grappling with the challenge of deploying an ethical digital pathology infrastructure to support Al research as part of the National Pathology Imaging Cooperative (NPIC), a collaborative of universities, hospital trusts, and industry partners largely located across the North of England. Though focusing on the UK case, internationally, few pathology departments have gone fully digital, and so the themes developed here offer a heuristic for ethical reflection for other departments currently making a similar transition or planning to do so in the future. We conclude by promoting the need for robust public governance mechanisms in Al-driven digital pathology.
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