Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods.

OBJECTIVE: With the recent increase in research into public views on healthcare artificial intelligence (HCAI), the objective of this review is to examine the methods of empirical studies on public views on HCAI. We map how studies provided participants with information about HCAI, and we examine the extent to which studies framed publics as active contributors to HCAI governance. MATERIALS AND METHODS: We searched 5 academic databases and Google Advanced for empirical studies investigating public views on HCAI. We extracted information including study aims, research instruments, and recommendations. RESULTS: Sixty-two studies were included. Most were quantitative (N = 42). Most (N = 47) reported providing participants with background information about HCAI. Despite this, studies often reported participants' lack of prior knowledge about HCAI as a limitation. Over three quarters (N = 48) of the studies made recommendations that envisaged public views being used to guide governance of AI. DISCUSSION: Provision of background information is an important component of facilitating research with publics on HCAI. The high proportion of studies reporting participants' lack of knowledge about HCAI as a limitation reflects the need for more guidance on how information should be presented. A minority of studies adopted technocratic positions that construed publics as passive beneficiaries of AI, rather than as active stakeholders in HCAI design and implementation. CONCLUSION: This review draws attention to how public roles in HCAI governance are constructed in empirical studies. To facilitate active participation, we recommend that research with publics on HCAI consider methodological designs that expose participants to diverse information sources.

How should artificial intelligence be used in Australian health care? Recommendations from a citizens' jury.

OBJECTIVE: To support a diverse sample of Australians to make recommendations about the use of artificial intelligence (AI) technology in health care. STUDY DESIGN: Citizens' jury, deliberating the question: "Under which circumstances, if any, should artificial intelligence be used in Australian health systems to detect or diagnose disease?" SETTING, PARTICIPANTS: Thirty Australian adults recruited by Sortition Foundation using random invitation and stratified selection to reflect population proportions by gender, age, ancestry, highest level of education, and residential location (state/territory; urban, regional, rural). The jury process took 18 days (16 March - 2 April 2023): fifteen days online and three days face-to-face in Sydney, where the jurors, both in small groups and together, were informed about and discussed the question, and developed recommendations with reasons. Jurors received extensive information: a printed handbook, online documents, and recorded presentations by four expert speakers. Jurors asked questions and received answers from the experts during the online period of the process, and during the first day of the face-to-face meeting. MAIN OUTCOME MEASURES: Jury recommendations, with reasons. RESULTS: The jurors recommended an overarching, independently governed charter and framework for health care AI. The other nine recommendation categories concerned balancing benefits and harms; fairness and bias; patients' rights and choices; clinical governance and training; technical governance and standards; data governance and use; open source software; AI evaluation and assessment; and education and communication. CONCLUSIONS: The deliberative process supported a nationally representative sample of citizens to construct recommendations about how AI in health care should be developed, used, and governed. Recommendations derived using such methods could guide clinicians, policy makers, AI researchers and developers, and health service users to develop approaches that ensure trustworthy and responsible use of this technology.

'Integrating Ethics and Equity with Economics and Effectiveness for newborn screening in the genomic age: A qualitative study protocol of stakeholder perspectives.

BACKGROUND: Newborn bloodspot screening is a well-established population health initiative that detects serious, childhood-onset, treatable conditions to improve health outcomes. With genomic technologies advancing rapidly, many countries are actively discussing the introduction of genomic assays into newborn screening programs. While adding genomic testing to Australia's newborn screening program could improve outcomes for infants and families, it must be considered against potential harms, ethical, legal, equity and social implications, and economic and health system impacts. We must ask not only 'can' we use genomics to screen newborns?' but 'should we'?' and 'how much should health systems invest in genomic newborn screening?'. METHODS: This study will use qualitative methods to explore understanding, priorities, concerns and expectations of genomic newborn screening among parents/carers, health professionals/scientists, and health policy makers across Australia. In-depth, semi-structured interviews will be held with 30-40 parents/carers recruited via hospital and community settings, 15-20 health professionals/scientists, and 10-15 health policy makers. Data will be analysed using inductive content analysis. The Sydney Children's Hospital Network Human Research Ethics Committee approved this study protocol [2023/ETH02371]. The Standards for Reporting Qualitative Research will guide study planning, conduct and reporting. DISCUSSION: Few studies have engaged a diverse range of stakeholders to explore the implications of genomics in newborn screening in a culturally and genetically diverse population, nor in a health system underpinned by universal health care. As the first study within a multi-part research program, findings will be used to generate new knowledge on the risks and benefits and importance of ethical, legal, social and equity implications of genomic newborn screening from the perspective of key stakeholders. As such it will be the foundation on which child and family centered criteria can be developed to inform health technology assessments and drive efficient and effective policy decision-making on the implementation of genomics in newborn screening.

Making recommendations to subsidize new health technologies in Australia: A qualitative study of decision-makers' perspectives on committee processes.

OBJECTIVES: To explore experiences of, and perspectives on, health technology assessment (HTA) processes used to produce recommendations about subsidizing new medicines, and medical technologies in Australia, from the perspectives of those experienced in these processes. METHODS: Semi-structured interviews with a diverse group of 18 informants currently or previously members of the Pharmaceutical Benefits Advisory Committee (PBAC) or the Medical Services Advisory Committee (MSAC). Participants were interviewed September 2021-February 2022. Transcripts were analyzed using reflexive thematic analysis. RESULTS: 3 major themes were identified: contrasting technical and decision-making stages, resisting reductionist approaches, and navigating decision-making trade-offs. Participants discussed the complexities of the evaluative HTA process, especially when considering uncertainty in the evidence. As part of the current process, a deliberative decision-making stage was considered essential, allowing a flexible approach to decision making to consider factors beyond strength and quality of quantifiable data in the technical evaluation. Participants acknowledged these less-quantifiable factors were sometimes considered implicitly or were difficult to describe and this, paired with commercial in confidence requirements, presented challenges with respect to the desire to increase transparency. CONCLUSION (S): As HTA processes for new medicines and medical technologies in Australia continue to be reviewed, the balance between retaining flexibility during deliberation, confidentiality for sponsors and the public's desire for greater transparency may be a fruitful area for continuing research.

Direct-to-consumer tests advertised online in Australia and their implications for medical overuse: systematic online review and a typology of clinical utility.

OBJECTIVES: The objective of this study is to map the range and variety of direct-to-consumer (DTC) tests advertised online in Australia and analyse their potential clinical utility and implications for medical overuse. DESIGN: Systematic online search of DTC test products in Google and Google Shopping. DTC test advertisements data were collected and analysed to develop a typology of potential clinical utility of the tests at population level, assessing their potential benefits and harms using available evidence, informed by concepts of medical overuse. RESULTS: We identified 484 DTC tests (103 unique products), ranging from $A12.99 to $A1947 in cost (mean $A197.83; median $A148.50). Using our typology, we assigned the tests into one of four categories: tests with potential clinical utility (10.7%); tests with limited clinical utility (30.6%); non-evidence-based commercial 'health checks' (41.9%); and tests whose methods and/or target conditions are not recognised by the general medical community (16.7%). Of the products identified, 56% did not state that they offered pretest or post-test consultation, and 51% did not report analytical performance of the test or laboratory accreditation. CONCLUSIONS: This first-in-Australia study shows most DTC tests sold online have low potential clinical utility, with healthy consumers constituting the main target market. Harms may be caused by overdiagnosis, high rates of false positives and treatment decisions led by non-evidence-based tests, as well as financial costs of unnecessary and inappropriate testing. Regulatory mechanisms should demand a higher standard of evidence of clinical utility and efficacy for DTC tests. Better transparency and reporting of health outcomes, and the development of decision-support resources for consumers are needed.

Childhood vaccine refusal and what to do about it: a systematic review of the ethical literature.

BACKGROUND: Parental refusal of routine childhood vaccination remains an ethically contested area. This systematic review sought to explore and characterise the normative arguments made about parental refusal of routine vaccination, with the aim of providing researchers, practitioners, and policymakers with a synthesis of current normative literature. METHODS: Nine databases covering health and ethics research were searched, and 121 publications identified for the period Jan 1998 to Mar 2022. For articles, source journals were categorised according to Australian Standard Field of Research codes, and normative content was analysed using a framework analytical approach. RESULTS: Most of the articles were published in biomedical journals (34%), bioethics journals (21%), and journals that carry both classifications (20%). Two central questions dominated the literature: (1) Whether vaccine refusal is justifiable (which we labelled 'refusal arguments'); and (2) Whether strategies for dealing with those who reject vaccines are justifiable ('response arguments'). Refusal arguments relied on principlism, religious frameworks, the rights and obligations of parents, the rights of children, the medico-legal best interests of the child standard, and the potential to cause harm to others. Response arguments were broadly divided into arguments about policy, arguments about how individual physicians should practice regarding vaccine rejectors, and both legal precedents and ethical arguments for vaccinating children against a parent's will. Policy arguments considered the normative significance of coercion, non-medical or conscientious objections, and possible reciprocal social efforts to offset vaccine refusal. Individual physician practice arguments covered nudging and coercive practices, patient dismissal, and the ethical and professional obligations of physicians. Most of the legal precedents discussed were from the American setting, with some from the United Kingdom. CONCLUSIONS: This review provides a comprehensive picture of the scope and substance of normative arguments about vaccine refusal and responses to vaccine refusal. It can serve as a platform for future research to extend the current normative literature, better understand the role of cultural context in normative judgements about vaccination, and more comprehensively translate the nuance of ethical arguments into practice and policy.

Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis.

INTRODUCTION: Despite the proliferation of Artificial Intelligence (AI) technology over the last decade, clinician, patient, and public perceptions of its use in healthcare raise a number of ethical, legal and social questions. We systematically review the literature on attitudes towards the use of AI in healthcare from patients, the general public and health professionals' perspectives to understand these issues from multiple perspectives. METHODOLOGY: A search for original research articles using qualitative, quantitative, and mixed methods published between 1 Jan 2001 to 24 Aug 2021 was conducted on six bibliographic databases. Data were extracted and classified into different themes representing views on: (i) knowledge and familiarity of AI, (ii) AI benefits, risks, and challenges, (iii) AI acceptability, (iv) AI development, (v) AI implementation, (vi) AI regulations, and (vii) Human - AI relationship. RESULTS: The final search identified 7,490 different records of which 105 publications were selected based on predefined inclusion/exclusion criteria. While the majority of patients, the general public and health professionals generally had a positive attitude towards the use of AI in healthcare, all groups indicated some perceived risks and challenges. Commonly perceived risks included data privacy; reduced professional autonomy; algorithmic bias; healthcare inequities; and greater burnout to acquire AI-related skills. While patients had mixed opinions on whether healthcare workers suffer from job loss due to the use of AI, health professionals strongly indicated that AI would not be able to completely replace them in their professions. Both groups shared similar doubts about AI's ability to deliver empathic care. The need for AI validation, transparency, explainability, and patient and clinical involvement in the development of AI was emphasised. To help successfully implement AI in health care, most participants envisioned that an investment in training and education campaigns was necessary, especially for health professionals. Lack of familiarity, lack of trust, and regulatory uncertainties were identified as factors hindering AI implementation. Regarding AI regulations, key themes included data access and data privacy. While the general public and patients exhibited a willingness to share anonymised data for AI development, there remained concerns about sharing data with insurance or technology companies. One key domain under this theme was the question of who should be held accountable in the case of adverse events arising from using AI. CONCLUSIONS: While overall positivity persists in attitudes and preferences toward AI use in healthcare, some prevalent problems require more attention. There is a need to go beyond addressing algorithm-related issues to look at the translation of legislation and guidelines into practice to ensure fairness, accountability, transparency, and ethics in AI.

Australian women's judgements about using artificial intelligence to read mammograms in breast cancer screening.

Objective: Mammographic screening for breast cancer is an early use case for artificial intelligence (AI) in healthcare. This is an active area of research, mostly focused on the development and evaluation of individual algorithms. A growing normative literature argues that AI systems should reflect human values, but it is unclear what this requires in specific AI implementation scenarios. Our objective was to understand women's values regarding the use of AI to read mammograms in breast cancer screening. Methods: We ran eight online discussion groups with a total of 50 women, focused on their expectations and normative judgements regarding the use of AI in breast screening. Results: Although women were positive about the potential of breast screening AI, they argued strongly that humans must remain as central actors in breast screening systems and consistently expressed high expectations of the performance of breast screening AI. Women expected clear lines of responsibility for decision-making, to be able to contest decisions, and for AI to perform equally well for all programme participants. Women often imagined both that AI might replace radiographers and that AI implementation might allow more women to be screened: screening programmes will need to communicate carefully about these issues. Conclusions: To meet women's expectations, screening programmes should delay implementation until there is strong evidence that the use of AI systems improves screening performance, should ensure that human expertise and responsibility remain central in screening programmes, and should avoid using AI in ways that exacerbate inequities.

Measures of socioeconomic advantage are not independent predictors of support for healthcare AI: subgroup analysis of a national Australian survey.

Objectives: Applications of artificial intelligence (AI) have the potential to improve aspects of healthcare. However, studies have shown that healthcare AI algorithms also have the potential to perpetuate existing inequities in healthcare, performing less effectively for marginalised populations. Studies on public attitudes towards AI outside of the healthcare field have tended to show higher levels of support for AI among socioeconomically advantaged groups that are less likely to be sufferers of algorithmic harms. We aimed to examine the sociodemographic predictors of support for scenarios related to healthcare AI.Methods: The Australian Values and Attitudes toward AI survey was conducted in March 2020 to assess Australians' attitudes towards AI in healthcare. An innovative weighting methodology involved weighting a non-probability web-based panel against results from a shorter omnibus survey distributed to a representative sample of Australians. We used multinomial logistic regression to examine the relationship between support for AI and a suite of sociodemographic variables in various healthcare scenarios.Results: Where support for AI was predicted by measures of socioeconomic advantage such as education, household income and Socio-Economic Indexes for Areas index, the same variables were not predictors of support for the healthcare AI scenarios presented. Variables associated with support for healthcare AI included being male, having computer science or programming experience and being aged between 18 and 34 years. Other Australian studies suggest that these groups may have a higher level of perceived familiarity with AI.Conclusion: Our findings suggest that while support for AI in general is predicted by indicators of social advantage, these same indicators do not predict support for healthcare AI.

Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives.

BACKGROUND: There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented and marginalised groups (eg, based on gender or race). OBJECTIVES: Our objectives are to canvas the range of strategies stakeholders endorse in attempting to mitigate algorithmic bias, and to consider the ethical question of responsibility for algorithmic bias. METHODOLOGY: The study involves in-depth, semistructured interviews with healthcare workers, screening programme managers, consumer health representatives, regulators, data scientists and developers. RESULTS: Findings reveal considerable divergent views on three key issues. First, views on whether bias is a problem in healthcare AI varied, with most participants agreeing bias is a problem (which we call the bias-critical view), a small number believing the opposite (the bias-denial view), and some arguing that the benefits of AI outweigh any harms or wrongs arising from the bias problem (the bias-apologist view). Second, there was a disagreement on the strategies to mitigate bias, and who is responsible for such strategies. Finally, there were divergent views on whether to include or exclude sociocultural identifiers (eg, race, ethnicity or gender-diverse identities) in the development of AI as a way to mitigate bias. CONCLUSION/SIGNIFICANCE: Based on the views of participants, we set out responses that stakeholders might pursue, including greater interdisciplinary collaboration, tailored stakeholder engagement activities, empirical studies to understand algorithmic bias and strategies to modify dominant approaches in AI development such as the use of participatory methods, and increased diversity and inclusion in research teams and research participant recruitment and selection.

Transvaginal mesh in Australia: An analysis of news media reporting from 1996 to 2021.

INTRODUCTION: Transvaginal mesh (mesh) surgeries have been used to treat stress urinary incontinence (incontinence) and/or pelvic organ prolapse (prolapse). In Australia, as in many other countries, the harms caused by mesh eventually prompted individual and collective attempts to achieve redress. The rise of mesh surgery as a procedure, the experience of mesh-affected women and the formal inquiries and legal actions that followed all occurred in social, cultural and discursive contexts. One strategy to understand these contexts is to track how the mesh and key actors in the mesh stories have been portrayed in mass media sources. We conducted a media analysis of the most highly read Australian newspapers and online news media platforms, focusing on how mesh and the interaction of stakeholders in mesh stories were presented to the Australian public. METHOD: We searched systematically in the top 10 most-read print and online media outlets in Australia. We included all articles that mentioned mesh, from the date of first use of mesh in Australia to the date of our final search (1996-2021). RESULT: After early scant media reporting focusing on the benefits of mesh procedures, major Australian medicolegal processes created a hook to shift reporting about mesh. The news media then played a significant role in redressing women's experienced epistemic injustice, including by amplifying previously ignored evidence of harm. This created an opportunity for previously unreported suffering to be revealed to powerful actors, in settings beyond the immediate control and epistemic authority of healthcare stakeholders, validating women's testimony and creating new hermeneutic resources for understanding mesh. Over time, media reports show healthcare stakeholders responding sympathetically to these new understandings in public discourse, contrasting with their statements in earlier media coverage. CONCLUSION: We argue that mass media reporting, in synergy with medicolegal actions and the Australian Senate Inquiry, appears to have provided women with greater epistemic justice, giving their testimony privileged epistemic status such that it was considered by powerful actors. Although medical reporting is not recognised in the hierarchy of evidence embedded in the medical knowledge system, in this case, media reporting appears to have contributed to shaping medical knowledge in significant ways. PATIENT OR PUBLIC CONTRIBUTION: We used publicly available data, print and online media outlets, for our analysis. Therefore, this manuscript does not contain the direct contribution of patients, service users, caregivers, people with lived experience or members of the public.

Utopia versus dystopia: Professional perspectives on the impact of healthcare artificial intelligence on clinical roles and skills.

BACKGROUND: Alongside the promise of improving clinical work, advances in healthcare artificial intelligence (AI) raise concerns about the risk of deskilling clinicians. This purpose of this study is to examine the issue of deskilling from the perspective of diverse group of professional stakeholders with knowledge and/or experiences in the development, deployment and regulation of healthcare AI. METHODS: We conducted qualitative, semi-structured interviews with 72 professionals with AI expertise and/or professional or clinical expertise who were involved in development, deployment and/or regulation of healthcare AI. Data analysis using combined constructivist grounded theory and framework approach was performed concurrently with data collection. FINDINGS: Our analysis showed participants had diverse views on three contentious issues regarding AI and deskilling. The first involved competing views about the proper extent of AI-enabled automation in healthcare work, and which clinical tasks should or should not be automated. We identified a cluster of characteristics of tasks that were considered more suitable for automation. The second involved expectations about the impact of AI on clinical skills, and whether AI-enabled automation would lead to worse or better quality of healthcare. The third tension implicitly contrasted two models of healthcare work: a human-centric model and a technology-centric model. These models assumed different values and priorities for healthcare work and its relationship to AI-enabled automation. CONCLUSION: Our study shows that a diverse group of professional stakeholders involved in healthcare AI development, acquisition, deployment and regulation are attentive to the potential impact of healthcare AI on clinical skills, but have different views about the nature and valence (positive or negative) of this impact. Detailed engagement with different types of professional stakeholders allowed us to identify relevant concepts and values that could guide decisions about AI algorithm development and deployment.

Considering potential benefits, as well as harms, from the COVID-19 disruption to cancer screening and other healthcare services.

Since 2020, hundreds of thousands of more deaths than expected have been observed across the globe. Amid the coronavirus 2019 (COVID-19) pandemic, current research priorities are to control the spread of infection and minimise loss of life. However, there may be future opportunities to learn from the pandemic to build a better healthcare system that delivers maximum health benefits with minimum harm. So far, much research has focused on foregone benefits of healthcare services such as cancer screening during the pandemic. A more balanced approach is to recognise that all healthcare services have potential harms as well as benefits. In this way, we may be able to use pandemic 'natural experiments' to identify cases where a reduction in a healthcare service has not been harmful to the population and some instances where this may have even been beneficial.

Reimagining consumer involvement: Resilient system indicators in the COVID-19 pandemic response in New South Wales, Australia.

BACKGROUND: Reflections on the response to the COVID-19 pandemic often evoke the concept of 'resilience' to describe the way health systems adjusted and adapted their functions to withstand the disturbance of a crisis, and in some cases, improve and transform in its wake. Drawing from this, this study focuses on the role of consumer representatives in healthcare services in initiating changes to the way they participated in the pandemic response in the state of New South Wales in Australia. METHODS: In-depth interviews were conducted with two cohorts of consumer representatives. Cohort A included experienced and self-identified consumer leaders, who worked together in a COVID-19 Consumer Leaders Taskforce; Cohort B included participants outside of this group, and purposively included consumer representatives from rural and regional areas, and culturally and linguistically diverse communities. RESULTS: The pause in consumer engagement to support health service decision-making in responding to the pandemic forced consumer representatives to consider alternative approaches to participate. Some initiated networking with each other, forming new collaborations to produce consumer-led research and guidelines on pandemic-related patient care. Others mobilized support from community and politicians to lobby for specific healthcare issues in their local areas. CONCLUSION: The response to the COVID-19 pandemic made visible the brittle nature of previous engagement processes of involving consumers in organizational design and governance. However, the momentum for proactive self-organization in an unexpected crisis created space for consumer representatives to reset and reimagine their role as active partners in health services. Their ability to adapt and adjust ways of working are key assets for a resilient health system. PATIENT OR PUBLIC CONTRIBUTION: This project is a collaborative study between academic researchers and health consumer (patient and public) representatives. It followed the principles of codesign and coresearch, whereby both consumer representatives and academic researchers contributed equally to all stages of the project. The study was cofunded by both academic institutions and consumer representative organizations.

Public views on ethical issues in healthcare artificial intelligence: protocol for a scoping review.

BACKGROUND: In recent years, innovations in artificial intelligence (AI) have led to the development of new healthcare AI (HCAI) technologies. Whilst some of these technologies show promise for improving the patient experience, ethicists have warned that AI can introduce and exacerbate harms and wrongs in healthcare. It is important that HCAI reflects the values that are important to people. However, involving patients and publics in research about AI ethics remains challenging due to relatively limited awareness of HCAI technologies. This scoping review aims to map how the existing literature on publics' views on HCAI addresses key issues in AI ethics and governance. METHODS: We developed a search query to conduct a comprehensive search of PubMed, Scopus, Web of Science, CINAHL, and Academic Search Complete from January 2010 onwards. We will include primary research studies which document publics' or patients' views on machine learning HCAI technologies. A coding framework has been designed and will be used capture qualitative and quantitative data from the articles. Two reviewers will code a proportion of the included articles and any discrepancies will be discussed amongst the team, with changes made to the coding framework accordingly. Final results will be reported quantitatively and qualitatively, examining how each AI ethics issue has been addressed by the included studies. DISCUSSION: Consulting publics and patients about the ethics of HCAI technologies and innovations can offer important insights to those seeking to implement HCAI ethically and legitimately. This review will explore how ethical issues are addressed in literature examining publics' and patients' views on HCAI, with the aim of determining the extent to which publics' views on HCAI ethics have been addressed in existing research. This has the potential to support the development of implementation processes and regulation for HCAI that incorporates publics' values and perspectives.

Direct-to-consumer detection of atrial fibrillation in a smartwatch electrocardiogram: Medical overuse, medicalisation and the experience of consumers.

The Apple Watch Electrocardiogram (ECG) is a digital feature that detects signs of atrial fibrillation (AFib), a heart arrhythmia that can lead to stroke. Unlike AFib detection offered in a clinical setting to patients or those at higher risk, the Apple Watch ECG is a direct-to-consumer (DTC) product marketed to the healthy as a self-performed medical test. The feature is incorporated in the design as one of many applications in a multifunctional smartwatch. The Apple Watch ECG signals the movement of consumer wearables into the domain of medical devices, and the highly contested practice of AFib screening. This article examines how this technology produces new avenues for medical overuse among people who are unlikely to gain clinical benefit, and who as active consumers become medicalised via taking part in disease-specific monitoring. Interviews with Apple Watch ECG consumers suggest their strongly trust in the Apple brand is amplified by the promissory quality of screening and technical innovation. What would otherwise be a costly clinical procedure is condensed into an accessible function in a wearable device. Consequently, AFib screening loses its clinical purpose, and is instead refashioned into a component of healthy lifestyle behaviour. Repeat self-screening becomes 'bundled' with similar health monitoring practices routinised in consumer wearables. Active uptake supports a market for the product and adds to the reputation of the Apple brand such that consumers become complicit in legitimising a medical practice that has limited clinical justification.

Pragmatic pluralism: Mutual tolerance of contested understandings between orthodox and alternative practitioners in autologous stem cell transplantation.

High-dose chemotherapy and autologous stem cell transplantation (ASCT) is used to treat some advanced malignancies. It is a traumatic procedure, with a high complication rate and significant mortality. ASCT patients and their carers draw on many sources of information as they seek to understand the procedure and its consequences. Some seek information from beyond orthodox medicine. Alternative beliefs and practices may conflict with conventional understanding of the theory and practice of ASCT, and 'contested understandings' might interfere with patient adherence to the strict and demanding protocols required for successful ASCT.The present study, conducted in Sydney, Australia, examines narrative-style interviews with 10 sequentially recruited ASCT patients and nine of their carers conducted at the time of transplant and three months later. Transcripts were read for instances of mention of alternative advice, and for instances of contested understanding of information relevant to the transplant.Patients and carer pairs expressed closely concordant views about alternative advice. Five pairs were consulting alternative practitioners. Contested understanding was expressed in four domains-understandings of the transplant itself and its underlying theory, of the relationship between the components of the 'transplant', of the nature and role of stem cells, and of beliefs about bodily function and life-style. Contested understandings of the transplant treatment were expressed as predominantly personal interpretations of orthodox informationPatients and carers seemed to recognise that alternative and conventional systems were discordant, yet they were able to separate the two, and adhere to each practice without prejudicing their medical treatment. A single case of late, post-transplant repudiation of Western medicine is discussed to emphasise some of the possible determinants of dissonance when it does occur.

Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection.

INTRODUCTION: Artificial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval cancers, without contributing substantially to overdiagnosis. Studies suggesting that AI has comparable or greater accuracy than radiologists commonly employ 'enriched' datasets in which cancer prevalence is higher than in population screening. Routine screening outcome metrics (cancer detection and recall rates) cannot be estimated from these datasets, and accuracy estimates may be subject to spectrum bias which limits generalisabilty to real-world screening. We aim to address these limitations by comparing the accuracy of AI and radiologists in a cohort of consecutive of women attending a real-world population breast cancer screening programme. METHODS AND ANALYSIS: A retrospective, consecutive cohort of digital mammography screens from 109 000 distinct women was assembled from BreastScreen WA (BSWA), Western Australia's biennial population screening programme, from November 2016 to December 2017. The cohort includes 761 screen-detected and 235 interval cancers. Descriptive characteristics and results of radiologist double-reading will be extracted from BSWA outcomes data collection. Mammograms will be reinterpreted by a commercial AI algorithm (DeepHealth). AI accuracy will be compared with that of radiologist single-reading based on the difference in the area under the receiver operating characteristic curve. Cancer detection and recall rates for combined AI-radiologist reading will be estimated by pairing the first radiologist read per screen with the AI algorithm, and compared with estimates for radiologist double-reading. ETHICS AND DISSEMINATION: This study has ethical approval from the Women and Newborn Health Service Ethics Committee (EC00350) and the Curtin University Human Research Ethics Committee (HRE2020-0316). Findings will be published in peer-reviewed journals and presented at national and international conferences. Results will also be disseminated to stakeholders in Australian breast cancer screening programmes and policy makers in population screening.