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Abstract Background It 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 body Focusing 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. Conclusion Data access committees can undertake ethical review of medical research databases provided they enhance that review function through professional and lay ethical expertise.

Abstract Artificial intelligence (AI) is often cited as a possible solution to current issues faced by healthcare systems. This includes the freeing up of time for doctors and facilitating person-centred doctor-patient relationships. However, given the novelty of artificial intelligence tools, there is very little concrete evidence on their impact on the doctor-patient relationship or on how to ensure that they are implemented in a way which is beneficial for person-centred care. Given the importance of empathy and compassion in the practice of person-centred care, we conducted a literature review to explore how AI impacts these two values. Besides empathy and compassion, shared decision-making, and trust relationships emerged as key values in the reviewed papers. We identified two concrete ways which can help ensure that the use of AI tools have a positive impact on person-centred doctor-patient relationships. These are (1) using AI tools in an assistive role and (2) adapting medical education. The study suggests that we need to take intentional steps in order to ensure that the deployment of AI tools in healthcare has a positive impact on person-centred doctor-patient relationships. We argue that the proposed solutions are contingent upon clarifying the values underlying future healthcare systems.

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 de-identified 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.

Abstract Background As the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning. Methods Semi-structured qualitative interviews (n = 20) with stakeholders (clinical geneticists, data scientists, bioinformaticians, industry and patient support group spokespersons) who design and/or work with computational phenotyping (CP) systems. The method of constant comparison was used to analyse the interview data. Results Interviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides—epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology’s reliability and accuracy and the person using/developing them is trusted. Conclusion This study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294.

Computational phenotyping (CP) technology uses facial recognition algorithms to classify and potentially diagnose rare genetic disorders on the basis of digitised facial images. This AI technology has a number of research as well as clinical applications, such as supporting diagnostic decision-making. Using the example of CP, we examine stakeholders’ views of the benefits and costs of using AI as a diagnostic tool within the clinic. Through a series of in-depth interviews (n ​= ​20) with: clinicians, clinical researchers, data scientists, industry and support group representatives, we report stakeholder views regarding the adoption of this technology in a clinical setting. While most interviewees were supportive of employing CP as a diagnostic tool in some capacity we observed ambivalence around the potential for artificial intelligence to overcome diagnostic uncertainty in a clinical context. Thus, while there was widespread agreement amongst interviewees concerning the public benefits of AI assisted diagnosis, namely, its potential to increase diagnostic yield and enable faster more objective and accurate diagnoses by up skilling non specialists and thereby enabling access to diagnosis that is potentially lacking, interviewees also raised concerns about ensuring algorithmic reliability, expunging algorithmic bias and that the use of AI could result in deskilling the specialist clinical workforce. We conclude that, prior to widespread clinical implementation, on-going reflection is needed regarding the trade-offs required to determine acceptable levels of bias and conclude that diagnostic AI tools should only be employed as an assistive technology within the dysmorphology clinic.

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 (AI)‐driven research. Recognising, however, that there is little scholarly debate on the ethics of digital pathology when used for AI research, this paper summarises what it sees as four key ethical issues to consider when deploying AI 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 AI 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 AI‐driven digital pathology.

Plain English summary How might the public be authentically involved in decisions about medical data sharing for artificial intelligence (AI) research? In this paper, we highlight three ways in which public views are used to improve such decisions, namely, through lay representation on data access committees, through patient and public involvement groups, and through a variety of public engagement events we call “citizen forums.” Though each approach has common strengths and weaknesses, we argue that they are unable to support each other due to a lack of proper integration. We therefore propose combining them so that they work in a more coordinated way. The combined model, we argue, could be useful for improving the trustworthiness of big data and AI related medical research in the future.

Abstract Digital Pathology (DP) is a platform which has the potential to develop a truly integrated and global pathology community. The generation of DP data at scale creates novel challenges for the histopathology community in managing, processing, and governing the use of these data. The current understanding of, and confidence in, the legal and ethical aspects of DP by pathologists is unknown. We developed an electronic survey (e‐survey), comprising 22 questions, with input from the Royal College of Pathologists (RCPath) Digital Pathology Working Group. The e‐survey was circulated via e‐mail and social media (Twitter) through the RCPath Digital Pathology Working Group network, RCPath Trainee Committee network, the Pathology image data Lake for Analytics, Knowledge and Education (PathLAKE) digital pathology consortium, National Pathology Imaging Co‐operative (NPIC), local contacts, and to the membership of both The Pathological Society of Great Britain and Ireland and the British Division of the International Academy of Pathology (BDIAP). Between 14 July 2020 and 6 September 2020, we collected 198 responses representing a cross section of histopathologists, including individuals with experience of DP research. We ascertained that, in the UK, DP is being used for diagnosis, research, and teaching, and that the platform is enabling data sharing. Our survey demonstrated that there is often a lack of confidence and understanding of the key issues of consent, legislation, and ethical guidelines. Of 198 respondents, 82 (41%) did not know when the use of digital scanned slide images would fall under the relevant legislation and 93 (47%) were ‘Not confident at all’ in their interpretation of consent for scanned slide images in research. With increasing uptake of DP, a working knowledge of these areas is essential but histopathologists often express a lack of confidence in these topics. The need for specific training in these areas is highlighted by the findings of this study.

This paper is based upon interviews with twenty-seven women and men who have an inherited risk of developing gastric cancer and have had their stomach removed as a preventative measure. We describe what happens when bodily processes – digestion - are disrupted by the removal of the stomach. Interviewees' who had undergone prophylactic total gastrectomy experienced changes to the lived experience of hunger and appetite. The interviewees' accounts of life post-surgery suggested that private sensations of hunger (i.e. internalised feelings of hunger) disappeared following gastrectomy and in most cases never reappeared. The majority of interviewees described an alternative sense of hunger as developing over time. This externalised or disembodied hunger was described as triggered by a range of extracorporeal or external bodily criteria rather than internal sensations. We argue that this externally motivated desire for food, generated from without rather than within, serves the same purpose as internally generated feelings of hunger - it encourages eating, which sustains the body. Interviewees reported not only having to learn when to consume food following PTG, but also what to eat by adapting to what they perceived as their body's food tolerances and changing tastes. We argue that these changes in the lived experience of hunger and appetite may affect individuals' ability to participate in commensal relationships following gastrectomy, and that this may have negative consequences for identity.

Background: Around 60,000 babies are born preterm (prior to 37 weeks’ gestation) each year in the UK. There is little evidence on the optimal birth mode (vaginal or caesarean section). Objective: The overall aim of the CASSAVA project was to determine if a trial to define the optimal mode of preterm birth could be carried out and, if so, determine what sort of trial could be conducted and how it could best be performed. We aimed to determine the specific groups of preterm women and babies for whom there are uncertainties about the best planned mode of birth, and if there would be willingness to recruit to, and participate in, a randomised trial to address some, but not all, of these uncertainties. This project was conducted in response to a Heath Technology Assessment programme commissioning call (17/22 ‘Mode of delivery for preterm infants’). Methods: We conducted clinician and patient surveys (n = 224 and n = 379, respectively) to identify current practice and opinion, and a consensus survey and Delphi workshop (n = 76 and n = 22 participants, respectively) to inform the design of a hypothetical clinical trial. The protocol for this clinical trial/vignette was used in telephone interviews with clinicians (n = 24) and in focus groups with potential participants (n = 13). Results: Planned sample size and data saturation was achieved for all groups except for focus groups with participants, as this had to be curtailed because of the COVID-19 pandemic and data saturation was not achieved. There was broad agreement from parents and health-care professionals that a trial is needed. The clinician survey demonstrated a variety of practice and opinion. The parent survey suggested that women and their families generally preferred vaginal birth at later gestations and caesarean section for preterm infants. The interactive workshop and Delphi consensus process confirmed the need for more evidence (hence the case for a trial) and provided rich information on what a future trial should entail. It was agreed that any trial should address the areas with most uncertainty, including the management of women at 26–32 weeks’ gestation, with either spontaneous preterm labour (cephalic presentation) or where preterm birth was medically indicated. Clear themes around the challenges inherent in conducting any trial emerged, including the concept of equipoise itself. Specific issues were as follows: different clinicians and participants would be in equipoise for each clinical scenario, effective conduct of the trial would require appropriate resources and expertise within the hospital conducting the trial, potential participants would welcome information on the trial well before the onset of labour and minority ethnic groups would require tailored approaches. Conclusion: Given the lack of evidence and the variation of practice and opinion in this area, and having listened to clinicians and potential participants, we conclude that a trial should be conducted and the outlined challenges resolved. Future work: The CASSAVA project could be used to inform the design of a randomised trial and indicates how such a trial could be carried out. Any future trial would benefit from a pilot with qualitative input and a study within a trial to inform optimal recruitment. Limitations: Certainty that a trial could be conducted can be determined only when it is attempted. Trial registration: Current Controlled Trials ISRCTN12295730. Funding: This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment; Vol. 25, No. 61. See the NIHR Journals Library website for further project information.