Professionals' views on providing personalized recurrence risks for de novo mutations: Implications for genetic counseling.

When an apparent de novo (new) genetic change has been identified as the cause of a serious genetic condition in a child, many couples would like to know the risk of this happening again in a future pregnancy. Current practice provides families with a population average risk of 1%-2%. However, this figure is not accurate for any specific couple, and yet, they are asked to make decisions about having another child and/or whether to have prenatal testing.

Ethical preparedness in genomic medicine: how NHS clinical scientists navigate ethical issues.

Much has been published about the ethical issues encountered by clinicians in genetics/genomics, but those experienced by clinical laboratory scientists are less well described. Clinical laboratory scientists now frequently face navigating ethical problems in their work, but how they should be best supported to do this is underexplored. This lack of attention is also reflected in the ethics tools available to clinical laboratory scientists such as guidance and deliberative ethics forums, developed primarily to manage issues arising within the clinic.

"Uninformed consent" in clinical trials with cancer patients: A qualitative analysis of patients' and support persons' communication experiences and needs.

Objective: Cancer patients are often overwhelmed when being informed about clinical trials. However, there is a lack of evidence-based strategies to improve physician-patient communication in this area. This study assessed the experiences and needs of cancer patients and their support persons (SPs) during the informed consent (IC) process prior to participation in clinical trials.

Democratising or disrupting diagnosis? Ethical issues raised by the use of AI tools for rare disease diagnosis.

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.

Artificial intelligence and medical research databases: ethical review by data access committees.

Background: It has been argued that ethics review committees-e.g., Research Ethics Committees, Institutional Review Boards, etc.

The impact of artificial intelligence on the person-centred, doctor-patient relationship: some problems and solutions.

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.

A practical checklist for return of results from genomic research in the European context.

An increasing number of European research projects return, or plan to return, individual genomic research results (IRR) to participants. While data access is a data subject's right under the General Data Protection Regulation (GDPR), and many legal and ethical guidelines allow or require participants to receive personal data generated in research, the practice of returning results is not straightforward and raises several practical and ethical issues. Existing guidelines focusing on return of IRR are mostly project-specific, only discuss which results to return, or were developed outside Europe.

Providing recurrence risk counselling for parents after diagnosis of a serious genetic condition caused by an apparently de novo mutation in their child: a qualitative investigation of the PREGCARE strategy with UK clinical genetics practitioners.

Background: Diagnosis of a child with a genetic condition leads to parents asking whether there is a risk the condition could occur again with future pregnancies. If the cause is identified as an apparent de novo mutation (DNM), couples are currently given a generic, population average, recurrence risk of ~1%-2%, depending on the condition. Although DNMs usually arise as one-off events, they can also originate through the process of mosaicism in either parent; in this instance, the DNM is present in multiple germ cells and the actual recurrence risk could theoretically be as high as 50%.

"I don't think people are ready to trust these algorithms at face value": trust and the use of machine learning algorithms in the diagnosis of rare disease.

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.

Public governance of medical artificial intelligence research in the UK: an integrated multi-scale model.

There is a growing consensus among scholars, national governments, and intergovernmental organisations of the need to involve the public in decision-making around the use of artificial intelligence (AI) in society. Focusing on the UK, this paper asks how that can be achieved for medical AI research, that is, for research involving the training of AI on data from medical research databases. Public governance of medical AI research in the UK is generally achieved in three ways, namely, via lay representation on data access committees, through patient and public involvement groups, and by means of various deliberative democratic projects such as citizens' juries, citizen panels, citizen assemblies, etc.

The ethical challenges of artificial intelligence-driven digital pathology.

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.

Digital/computational phenotyping: What are the differences in the science and the ethics?

The concept of 'digital phenotyping' was originally developed by researchers in the mental health field, but it has travelled to other disciplines and areas. This commentary draws upon our experiences of working in two scientific projects that are based at the University of Oxford's Big Data Institute - The RADAR-AD project and The Minerva Initiative - which are developing algorithmic phenotyping technologies. We describe and analyse the concepts of digital biomarkers and computational phenotyping that underlie these projects, explain how they are linked to other research in digital phenotyping and compare and contrast some of their epistemological and ethical implications.

A Just Standard: The Ethical Management of Incidental Findings in Brain Imaging Research.

Neuroimaging research regularly yields "incidental findings": observations of potential clinical significance in healthy volunteers or patients, but which are unrelated to the purpose or variables of the study.

Understanding the ethical and legal considerations of Digital Pathology.

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.

Feasibility and design of a trial regarding the optimal mode of delivery for preterm birth: the CASSAVA multiple methods study.

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.

Governing AI-Driven Health Research: Are IRBs Up to the Task?

Many are calling for concrete mechanisms of oversight for health research involving artificial intelligence (AI). In response, institutional review boards (IRBs) are being turned to as a familiar model of governance. Here, we examine the IRB model as a form of ethics oversight for health research that uses AI.

Taking it to the bank: the ethical management of individual findings arising in secondary research.

A rapidly growing proportion of health research uses 'secondary data': data used for purposes other than those for which it was originally collected. Do researchers using secondary data have an obligation to disclose individual research findings to participants? While the importance of this question has been duly recognised in the context of primary research (ie, where data are collected from participants directly), it remains largely unexamined in the context of research using secondary data. In this paper, we critically examine the arguments for a moral obligation to disclose individual research findings in the context of primary research, to determine if they can be applied to secondary research.

COVID-19 and Contact Tracing Apps: Ethical Challenges for a Social Experiment on a Global Scale.

Mobile applications are increasingly regarded as important tools for an integrated strategy of infection containment in post-lockdown societies around the globe. This paper discusses a number of questions that should be addressed when assessing the ethical challenges of mobile applications for digital contact-tracing of COVID-19: Which safeguards should be designed in the technology? Who should access data? What is a legitimate role for "Big Tech" companies in the development and implementation of these systems? How should cultural and behavioural issues be accounted for in the design of these apps? Should use of these apps be compulsory? What does transparency and ethical oversight mean in this context? We demonstrate that responses to these questions are complex and contingent and argue that if digital contract-tracing is used, then it should be clear that this is on a trial basis and its use should be subject to independent monitoring and evaluation.

Clinician-researchers and custodians of scarce resources: a qualitative study of health professionals' views on barriers to the involvement of teenagers and young adults in cancer trials.

Background: Equipoise and role conflict have been previously identified as important factors in professionals' engagement with trials, inducing behaviours which can impact on recruitment. We explored these phenomena as potential explanations for the low levels of involvement of teenagers and young adults (TYA) with cancer in clinical trials in oncology.

Methods: We report findings from interviews with 30 purposively sampled direct-care professionals involved in delivering cancer care and/or facilitating clinical trials in Scotland.

Glyceryl trinitrate to reduce the need for manual removal of retained placenta following vaginal delivery: the GOT-IT RCT.

Background: Retained placenta is associated with postpartum haemorrhage and can lead to significant maternal morbidity if untreated. The only effective treatment is the surgical procedure of manual removal of placenta, which is costly, requires skilled staff, requires an operative environment and is unpleasant for women. Small studies suggest that glyceryl trinitrate may be an effective medical alternative.

Nitroglycerin for treatment of retained placenta: A randomised, placebo-controlled, multicentre, double-blind trial in the UK.

Background: Retained placenta following vaginal delivery is a major cause of postpartum haemorrhage. Currently, the only effective treatments for a retained placenta are the surgical procedures of manual removal of placenta (MROP) and uterine curettage, which are not universally available, particularly in low- and middle-income countries. The objective of the trial was to determine whether sublingual nitroglycerin spray was clinically effective and cost-effective for medical treatment of retained placenta following vaginal delivery.

How Should Decision Aids Be Used During Counseling to Help Patients Who Are "Genetically at Risk"?

People with genetic predispositions to disease are faced with uncertainty about whether, when, and to what extent an illness will actually develop. This prognostic uncertainty, combined with knowledge that preventative interventions (eg, risk-reducing surgeries for familial cancer syndromes) could significantly affect people's lives, renders prevention decisions especially challenging. This article illuminates ethical questions about the use of decision aids for people with genetic predispositions and calls for approaching individual decisions in light of ongoing communication and reflection about a person's life goals and values.

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative.

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies.