When the white coat meets the code: medical professionals’ negotiating with artificial intelligence, trust and boundary work

Noncommunicable diseases, such as type 2 diabetes, are the leading cause of death globally. This paper focuses on the boundary work among health professions involved in lifestyle-related preventive measures at Danish hospitals. Drawing on documents, qualitative in-depth interviews and extended conversations conducted with health professionals about goals, dilemmas, and practices related to lifestyle disease prevention, as well as site visits at Danish hospitals, it is identified which professional groups have claimed responsibility for the tasks, how they are defined, and through what kinds of interactions and infighting. The analysis grounds Abbott’s framework of jurisdictions and his meso-level vocabulary in a situated account of professional boundary work and follow the way nurses in particular create, and sometimes stabilize or standardize techniques for prevention of lifestyle-related diseases. It is demonstrated by different forms of boundary work how nurses work to extend, defend or refashion established work boundaries when handling these new tasks.

The boundary work approach has been established as one of the main ways to study controversies in science. However, it has been proposed that it does not meet the power dynamics of the scientific field sufficiently. This article concentrates on the intertwining of boundary work and power. It combines the boundary work approach developed by Thomas Gieryn and the analysis of power in the work of Pierre Bourdieu. Based on a literature review and an analysis of a controversy over therapeutic touch, it finds four forms of boundary work: intradisciplinary, interdisciplinary, between science and society, and between science and other knowledge systems. The article shows how the different forms of boundary work reveal multiple power struggles, hierarchisations and tensions. The controversy appeared in Finnish nursing science when the Finnish Association of Sceptics gave its annual Humbug Award to a book on therapeutic touch. The book was based on a master’s thesis at the University of Tampere department of nursing science. The nursing scholars in the department reacted abruptly and banned certain books, which astonished the students, and a lively public debate followed. In the debate, nursing science was both defended and challenged. The framework of the study shows that the boundary work approach can be used to study different aspects of power. The article proposes that the framework could be used in other controversies as well to study the layers of power in boundary work.

This article discusses how professionals’ efforts to reach policy goals engender boundary work. Analyses of interviews with service professionals in three welfare services in Norway which collaborate to implement the Introduction Program for refugees show how conflicting logics in services pose dilemmas for service professionals, and that political ideals of collaborative governance and integrated services are hard to put into practice. Service professionals resolve these dilemmas by engaging in various forms of boundary work, and the scope for boundary work is conditioned by the different service logics they operate under. Welfare service professionals collaborate to reach three policy goals—qualification of refugees, empowerment of users, and providing equity in services. The analysis shows that conflicting service logics result in boundary work practices that make coordination of, and collaboration between, services difficult, as services do not agree on how to interpret, and share responsibility for enacting, policy goals. The outcome of boundary work practices is a reshuffling of responsibilities—and a redelegation of tasks—which in principle should be shared, onto specific services. Different interpretations of policy goals instigate boundary work among welfare service professionals, which not only involves struggles over jurisdictional boundaries, but also negotiations over whom owns a policy problem, and over how to define and represent the problem. The findings from this study encourage researchers to further explore how policy goals are used as boundary objects in professionals’ negotiations over jurisdictional boundaries, in order to further understand what triggers and shapes boundary work among professionals.

Virtual communities of practice invoke novel forms of boundary work that are newly visible via publicly recorded discourse and failure narratives. This boundary work has critical implications for occupational knowledge, membership, and stratification. Building on social exchange theorization of network gatekeeping, the author tests the assumption that centralized peers are more competitive gatekeepers, in that they react more negatively to remedial narratives. The author tests this theory using empirical data from a virtual entrepreneur community on Reddit. The author finds that a peer’s tenure in the community network is directly related to exclusive, competitive boundary work of remedial members. However, by looking beyond the network structure to the content of the tie, the author finds that exclusive boundary work is not as impactful as inclusive, collaborative boundary work in this open network setting. The author builds on relational cohesion and exchange commitment theory to explain how remedial practitioners circumvent central community gatekeepers through failure narratives that provoke empathy from peripheral peers who experience higher uncertainty than core peers. Understanding these dynamics is critical to promoting recovery from failure and vitality of the community of practice.

Journalists, it is widely admitted, are engaged in new forms of boundary work on social media platforms, seeking to uphold their influence over news dissemination. This study focuses on music festivals as a case study to examine journalists' endeavors in maintaining their authority on social media. We analyze Twitter coverage of music festivals in France during the summer of 2018, systematically collecting data from 16 festivals of varying sizes and musical genres. Through this analysis, we investigate journalists' engagement and evaluate the trading of authority with other stakeholders through mentioning practices. Our findings challenge the prevailing notion of journalists as primary arbiters of authority on social media platforms. Despite their conspicuous presence during music festivals on Twitter, journalists emerge as relatively passive participants compared to other stakeholders in the music scene. Moreover, their ability to assert or receive authority from the broader public sphere is limited. This study sheds light on the bounded nature of journalists' boundary work on social media platforms, emphasizing the evolving dynamics of authority within digital information ecosystems.

Medical professionalism in the new millennium: a physician charter.

A key limiting factor in the scale up and sustainability of HIV care and treatment programmes is the global shortage of trained health care workers. This paper discusses why it is important to move beyond conceptualising health care workers simply as 'inputs' in the delivery of HIV treatment and care, and to also consider their roles as partners and agents in the process of health care. It suggests a framework for thinking about their roles and responses in HIV care, considers the current evidence base, and concludes by identifying key areas for future research on health care workers' responses in HIV treatment and care in low and middle income settings.

There is a growing focus on achieving “openness” in the design and transformation of organizations, in which the enabling role of ICTs is considered increasingly central. However, bureaucratic organizations with rigid structures continue to face significant challenges in moving towards more open forms of organizing. In this paper, we contribute to our understanding of these challenges by building on existing conceptualizations of openness as a form of boundary work that transforms by challenging both internal and external organizational boundaries. In particular, we draw on a performative view derived from actor-network theory to analyze a case study of ICT-based administrative reforms in a judicial system. Building on our case analysis, we develop a typology of the various roles that ICTs can play in both enabling and constraining ongoing boundary work within the context of their implementation. We thus present a view of ICT-enabled open organizing as a process where ICTs contribute to problematizing, shifting, and augmenting ongoing boundary work. This view highlights the inherently equivocal nature of the role of ICTs in transformations towards higher levels of openness.

This dialogue is from an early scene in the 2014 film Ex Machina, in which Nathan has invited Caleb to determine whether Nathan has succeeded in creating artificial intelligence.1 The achievement of powerful artificial general intelligence has long held a grip on our imagination not only for its exciting as well as worrisome possibilities, but also for its suggestion of a new, uncharted era for humanity. In opening his 2021 BBC Reith Lectures, titled "Living with Artificial Intelligence," Stuart Russell states that "the eventual emergence of general-purpose artificial intelligence [will be] the biggest event in human history."2Over the last decade, a rapid succession of impressive results has brought wider public attention to the possibilities of powerful artificial intelligence. In machine vision, researchers demonstrated systems that could recognize objects as well as, if not better than, humans in some situations. Then came the games. Complex games of strategy have long been associated with superior intelligence, and so when AI systems beat the best human players at chess, Atari games, Go, shogi, StarCraft, and Dota, the world took notice. It was not just that Als beat humans (although that was astounding when it first happened), but the escalating progression of how they did it: initially by learning from expert human play, then from self-play, then by teaching themselves the principles of the games from the ground up, eventually yielding single systems that could learn, play, and win at several structurally different games, hinting at the possibility of generally intelligent systems.3Speech recognition and natural language processing have also seen rapid and headline-grabbing advances. Most impressive has been the emergence recently of large language models capable of generating human-like outputs. Progress in language is of particular significance given the role language has always played in human notions of intelligence, reasoning, and understanding. While the advances mentioned thus far may seem abstract, those in driverless cars and robots have been more tangible given their embodied and often biomorphic forms. Demonstrations of such embodied systems exhibiting increasingly complex and autonomous behaviors in our physical world have captured public attention.Also in the headlines have been results in various branches of science in which AI and its related techniques have been used as tools to advance research from materials and environmental sciences to high energy physics and astronomy.4 A few highlights, such as the spectacular results on the fifty-year-old protein-folding problem by AlphaFold, suggest the possibility that AI could soon help tackle science's hardest problems, such as in health and the life sciences.5While the headlines tend to feature results and demonstrations of a future to come, AI and its associated technologies are already here and pervade our daily lives more than many realize. Examples include recommendation systems, search, language translators - now covering more than one hundred languages - facial recognition, speech to text (and back), digital assistants, chatbots for customer service, fraud detection, decision support systems, energy management systems, and tools for scientific research, to name a few. In all these examples and others, AI-related techniques have become components of other software and hardware systems as methods for learning from and incorporating messy real-world inputs into inferences, predictions, and, in some cases, actions. As director of the Future of Humanity Institute at the University of Oxford, Nick Bostrom noted back in 2006, "A lot of cutting-edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore."6As the scope, use, and usefulness of these systems have grown for individual users, researchers in various fields, companies and other types of organizations, and governments, so too have concerns when the systems have not worked well (such as bias in facial recognition systems), or have been misused (as in deepfakes), or have resulted in harms to some (in predicting crime, for example), or have been associated with accidents (such as fatalities from self-driving cars).7Dædalus last devoted a volume to the topic of artificial intelligence in 1988, with contributions from several of the founders of the field, among others. Much of that issue was concerned with questions of whether research in AI was making progress, of whether AI was at a turning point, and of its foundations, mathematical, technical, and philosophical-with much disagreement. However, in that volume there was also a recognition, or perhaps a rediscovery, of an alternative path toward AI - the connectionist learning approach and the notion of neural nets-and a burgeoning optimism for this approach's potential. Since the 1960s, the learning approach had been relegated to the fringes in favor of the symbolic formalism for representing the world, our knowledge of it, and how machines can reason about it. Yet no essay captured some of the mood at the time better than Hilary Putnam's "Much Ado About Not Very Much." Putnam questioned the Dædalus issue itself: "Why a whole issue of Dædalus? Why don't we wait until AI achieves something and then have an issue?" He concluded:This volume of Dædalus is indeed the first since 1988 to be devoted to artificial intelligence. This volume does not rehash the same debates; much else has happened since, mostly as a result of the success of the machine learning approach that was being rediscovered and reimagined, as discussed in the 1988 volume. This issue aims to capture where we are in AI's development and how its growing uses impact society. The themes and concerns herein are colored by my own involvement with AI. Besides the television, films, and books that I grew up with, my interest in AI began in earnest in 1989 when, as an undergraduate at the University of Zimbabwe, I undertook a research project to model and train a neural network.9 I went on to do research on AI and robotics at Oxford. Over the years, I have been involved with researchers in academia and labs developing AI systems, studying AI's impact on the economy, tracking AI's progress, and working with others in business, policy, and labor grappling with its opportunities and challenges for society.10The authors of the twenty-five essays in this volume range from AI scientists and technologists at the frontier of many of AI's developments to social scientists at the forefront of analyzing AI's impacts on society. The volume is organized into ten sections. Half of the sections are focused on AI's development, the other half on its intersections with various aspects of society. In addition to the diversity in their topics, expertise, and vantage points, the authors bring a range of views on the possibilities, benefits, and concerns for society. I am grateful to the authors for accepting my invitation to write these essays.Before proceeding further, it may be useful to say what we mean by artificial intelligence. The headlines and increasing pervasiveness of AI and its associated technologies have led to some conflation and confusion about what exactly counts as AI. This has not been helped by the current trend-among researchers in science and the humanities, startups, established companies, and even governments-to associate anything involving not only machine learning, but data science, algorithms, robots, and automation of all sorts with AI. This could simply reflect the hype now associated with AI, but it could also be an acknowledgment of the success of the current wave of AI and its related techniques and their wide-ranging use and usefulness. I think both are true; but it has not always been like this. In the period now referred to as the AI winter, during which progress in AI did not live up to expectations, there was a reticence to associate most of what we now call AI with AI.Two types of definitions are typically given for AI. The first are those that suggest that it is the ability to artificially do what intelligent beings, usually human, can do. For example, artificial intelligence is:The human abilities invoked in such definitions include visual perception, speech recognition, the capacity to reason, solve problems, discover meaning, generalize, and learn from experience. Definitions of this type are considered by some to be limiting in their human-centricity as to what counts as intelligence and in the benchmarks for success they set for the development of AI (more on this later). The second type of definitions try to be free of human-centricity and define an intelligent agent or system, whatever its origin, makeup, or method, as:This type of definition also suggests the pursuit of goals, which could be given to the system, self-generated, or learned.13 That both types of definitions are employed throughout this volume yields insights of its own.These definitional distinctions notwithstanding, the term AI, much to the chagrin of some in the field, has come to be what cognitive and computer scientist Marvin Minsky called a "suitcase word."14 It is packed variously, depending on who you ask, with approaches for achieving intelligence, including those based on logic, probability, information and control theory, neural networks, and various other learning, inference, and planning methods, as well as their instantiations in software, hardware, and, in the case of embodied intelligence, systems that can perceive, move, and manipulate objects.Three questions cut through the discussions in this volume: 1) Where are we in AI's development? 2) What opportunities and challenges does AI pose for society? 3) How much about AI is really about us?Notions of intelligent machines date all the way back to antiquity.15 Philosophers, too, among them Hobbes, Leibnitz, and Descartes, have been dreaming about AI for a long time; Daniel Dennett suggests that Descartes may have even anticipated the Turing Test.16 The idea of computation-based machine intelligence traces to Alan Turing's invention of the universal Turing machine in the 1930s, and to the ideas of several of his contemporaries in the mid-twentieth century. But the birth of artificial intelligence as we know it and the use of the term is generally attributed to the now famed Dartmouth summer workshop of 1956. The workshop was the result of a proposal for a two-month summer project by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon whereby "An attempt will be made to find how to make machines use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves."17In their respective contributions to this volume, "From So Simple a Beginning: Species of Artificial Intelligence" and "If We Succeed," and in different but complementary ways, Nigel Shadbolt and Stuart Russell chart the key ideas and developments in AI, its periods of excitement as well as the aforementioned AI winters. The current AI spring has been underway since the 1990s, with headline-grabbing breakthroughs appearing in rapid succession over the last ten years or so: a period that Jeffrey Dean describes in the title of his essay as a "golden decade," not only for the pace of AI development but also its use in a wide range of sectors of society, as well as areas of scientific research.18 This period is best characterized by the approach to achieve artificial intelligence through learning from experience, and by the success of neural networks, deep learning, and reinforcement learning, together with methods from probability theory, as ways for machines to learn.19A brief history may be useful here: In the 1950s, there were two dominant visions of how to achieve machine intelligence. One vision was to use computers to create a logic and symbolic representation of the world and our knowledge of it and, from there, create systems that could reason about the world, thus exhibiting intelligence akin to the mind. This vision was most espoused by Allen Newell and Hebert Simon, along with Marvin Minsky and others. Closely associated with it was the "heuristic search" approach that supposed intelligence was essentially a problem of exploring a space of possibilities for answers. The second vision was inspired by the brain, rather than the mind, and sought to achieve intelligence by learning. In what became known as the connectionist approach, units called perceptrons were connected in ways inspired by the connection of neurons in the brain. At the time, this approach was most associated with Frank Rosenblatt. While there was initial excitement about both visions, the first came to dominate, and did so for decades, with some successes, including so-called expert systems.Not only did this approach benefit from championing by its advocates and plentiful funding, it came with the suggested weight of a long intellectual tradition-exemplified by Descartes, Boole, Frege, Russell, and Church, among others-that sought to manipulate symbols and to formalize and axiomatize knowledge and reasoning. It was only in the late 1980s that interest began to grow again in the second vision, largely through the work of David Rumelhart, Geoffrey Hinton, James McClelland, and others. The history of these two visions and the associated philosophical ideas are discussed in Hubert Dreyfus and Stuart Dreyfus's 1988 Dædalus essay "Making a Mind Versus Modeling the Brain: Artificial Intelligence Back at a Branchpoint."20 Since then, the approach to intelligence based on learning, the use of statistical methods, back-propagation, and training (supervised and unsupervised) has come to characterize the current dominant approach.Kevin Scott, in his essay "I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale," reminds us of the work of Ray Solomonoff and others linking information and probability theory with the idea of machines that can not only learn, but compress and potentially generalize what they learn, and the emerging realization of this in the systems now being built and those to come. The success of the machine learning approach has benefited from the boon in the availability of data to train the algorithms thanks to the growth in the use of the Internet and other applications and services. In research, the data explosion has been the result of new scientific instruments and observation platforms and data-generating breakthroughs, for example, in astronomy and in genomics. Equally important has been the co-evolution of the software and hardware used, especially chip architectures better suited to the parallel computations involved in data- and compute-intensive neural networks and other machine learning approaches, as Dean discusses.Several authors delve into progress in key subfields of AI.21 In their essay, "Searching for Computer Vision North Stars," Fei-Fei Li and Ranjay Krishna chart developments in machine vision and the creation of standard data sets such as ImageNet that could be used for benchmarking performance. In their respective essays "Human Language Understanding & Reasoning" and "The Curious Case of Commonsense Intelligence," Chris Manning and Yejin Choi discuss different eras and ideas in natural language processing, including the recent emergence of large language models comprising hundreds of billions of parameters and that use transformer architectures and self-supervised learning on vast amounts of data.22 The resulting pretrained models are impressive in their capacity to take natural language prompts for which they have not been trained specifically and generate human-like outputs, not only in natural language, but also images, software code, and more, as Mira Murati discusses and illustrates in "Language & Coding Creativity." Some have started to refer to these large language models as foundational models in that once they are trained, they are adaptable to a wide range of tasks and outputs.23 But despite their unexpected performance, these large language models are still early in their development and have many shortcomings and limitations that are highlighted in this volume and elsewhere, including by some of their developers.24In "The Machines from Our Future," Daniela Rus discusses the progress in robotic systems, including advances in the underlying technologies, as well as in their integrated design that enables them to operate in the physical world. She highlights the limitations in the "industrial" approaches used thus far and suggests new ways of conceptualizing robots that draw on insights from biological systems. In robotics, as in AI more generally, there has always been a tension as to whether to copy or simply draw inspiration from how humans and other biological organisms achieve intelligent behavior. Elsewhere, AI researcher Demis Hassabis and colleagues have explored how neuroscience and AI learn from and inspire each other, although so far more in one than the other, as and have the success of the current approaches to AI, there are still many shortcomings and as well as problems in It is useful to on one such as when AI does not as or or or that can to or when it on or information about the world, or when it has such as of all of which can to a of public shortcomings have captured the attention of the wider public and as well as among there is an on AI and In recent years, there has been a of to principles and approaches to AI, as well as involving and such as the on AI, that to best important has been the of with to and - in the and developing AI in both and as has been well in recent This is an important in its own but also with to the of the resulting AI and, in its intersections with more the other there are limitations and problems associated with the that AI is not capable of if could to more more or more general AI. In their Turing deep learning and Geoffrey took of where deep learning and highlighted its current such as the with In the case of natural language processing, Manning and Choi the challenges in and despite the of large language Elsewhere, and have the notion that large language models do anything learning, or In & of in a and discuss the problems in systems, the as how to reason about other their systems, and well as challenges in both and especially when the include both humans and Elsewhere, and others a useful of the problems in there is a growing among many that we do not have for the of AI systems, especially as they become more capable and the of use although AI and its related techniques are to be powerful tools for research in science, as examples in this volume and recent examples in which AI not only help results but also by design and become what some have AI to science and and to and challenges for the possibility that more powerful AI could to new in science, as well as progress in some of challenges and has long been a key for many at the frontier of AI research to more capable the of each of AI, the of more general problems that to the possibility of more capable AI learning, reasoning, of and and of these and other problems that could to more capable systems the of whether current characterized by deep learning, the of and and more foundational and and reinforcement or whether different approaches are in such as cognitive agent approaches or or based on logic and probability theory, to name a few. whether and what of approaches be the AI is but many the current along with of and learning architectures have to their about the of the current approaches is associated with the of whether artificial general intelligence can be and if how and Artificial general intelligence is in to what is called that AI and for tasks and goals, such as The development of on the other aims for more powerful AI - at as powerful as is generally to problem or and, in some the capacity to and improve as well as set and its own and the of and when will be is a for most that its achievement have and as is often in and such as A through and The to Ex and it is or there is growing among many at the frontier of AI research that we for the possibility of powerful with to and and with humans, its and use, and the possibility that of could and that we these into how we approach the development of of the research and development, and in AI is of the AI and in its what Nigel Shadbolt the of AI. 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Russell a of the potentially from artificial general intelligence, once a of or ten But even we to general-purpose AI, the opportunities for companies and, for the and growth as well as from AI and its related technologies are more than to pursuit and by companies and in the development, and use of AI. At the many the is it is generally that is a in AI, as by its growth in AI research, and as highlighted in several will have for companies and given the of such technologies as discussed by and others the may in the way of approaches to AI and (such as whether they are companies or as and have have the to to in AI. The role of AI in intelligence, systems, autonomous even and other of increasingly In &

Strategies for Partnering with Health Care Settings to Increase Physical Activity Promotion

BackgroundArtificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them. MethodsA systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review. ResultsThe literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users. ConclusionThis is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.

Professional autonomy is often described as a claim of professionals that has to serve primarily their own interests. However, it can also be seen as an element of a professional ideal that can function as a standard for professional, i.e. medical practice. This normative understanding of the medical profession and professional autonomy faces three threats today. 1) Internal erosion of professional autonomy due to a lack of internal quality control by the medical profession; 2) the increasing upward pressure on health care expenses that calls for a health care policy that could imply limitations for the professional autonomy of physicians; 3) a distorted understanding of the profession as being based on a formal type of knowledge and related technology, in which other normative dimensions of medical practice are neglected and which frustrates meaningful communication between physicians and patients. To answer these threats a normative structure analysis of medical practice is presented, that indicates which principles and norms are constitutive for medical practice. It is concluded that professional autonomy, normatively understood, should be maintained to avoid the lure of the technological imperative and to protect patients against third parties' pressure to undertreatment. However, this professional autonomy can only be maintained if members of the profession subject their activities and decisions to a critical evaluation by other members of the profession and by patients and if they continue to critically reflect on the values that regulate today's medicine.

Background: This paper aims to outline an ethical overview of the potential challenges related to AI technologies in the doctor–patient relationship. Methods: This study is structured as a narrative review of the literature (2015–2025), based on searches conducted in the main scientific databases (PubMed, Scopus, Web of Science, Google Scholar), supplemented by official documents issued by the following international organizations: World Health Organization (WHO), United Nations Educational, Scientific and Cultural Organization (UNESCO), and the World Medical Association (WMA), as well as key regulatory frameworks of the European Union, China, and the United States. The selection included academic contributions, guidelines, and institutional reports relevant to the clinical applications of AI and their ethical and regulatory implications. Specifically, the analysis herein presented is grounded on four key aspects: the rationale for AI in patient care, informed consent about AI use, confidentiality, and the impact on the therapeutic alliance and medical professionalism. Results and Conclusions: Depending on their application, AI systems may offer benefits regarding the management of administrative burdens and in supporting clinical decisions. However, their applications in diagnostics, particularly in fields as radiology and dermatology, may also adversely impact the patient–doctor relationship and professional autonomy. Specifically, the implementation of these systems, including generative AI, may lead to increased healthcare costs and jeopardise the patient–doctor relationships by exposing patients’ confidentiality to new risks and reducing space for healthcare empathy and personalisation. The future of the medical profession and the doctor–patient relationship will largely depend on the types of artificial intelligence that are integrated into clinical practice and how effectively such additions are reconciled with core ethical values on which healthcare rests within our systems and societies.

BackgroundThe study aimed to explore the views of general practitioners (GPs), nurses and physiotherapists towards extending the role of sickness certification beyond the medical profession in primary care.MethodsFifteen GPs, seven nurses and six physiotherapists were selected to achieve varied respondent characteristics including sex, geographical location, service duration and post-graduate specialist training. Constant-comparative qualitative analysis of data from 28 semi-structured telephone interviews was undertaken.ResultsThe majority of respondents supported the extended role concept; however members of each professional group also rejected the notion. Respondents employed four different legitimacy claims to justify their views and define their occupational boundaries in relation to sickness certification practice. Condition-specific legitimacy, the ability to adopt a holistic approach to sickness certification, system efficiency and control-related arguments were used to different degrees by each occupation. Practical suggestions for the extension of the sickness certification role beyond the medical profession are underpinned by the sociological theory of professional identity.ConclusionsExtending the authority to certify sickness absence beyond the medical profession is not simply a matter of addressing practical and organisational obstacles. There is also a need to consider the impact on, and preferences of, the specific occupations and their respective boundary claims. This paper explores the implications of extending the sick certification role beyond general practice. We conclude that the main policy challenge of such a move is to a) persuade GPs to relinquish this role (or to share it with other professions), and b) to understand the ‘boundary work’ involved.

RapidAI Review for Stroke Detection What Is the Issue? Stroke is a sudden loss of neurologic function caused by poor or interrupted blood flow within the brain. It is 1 of the leading causes of death and a major cause of disability in Canada. For patients with suspected stroke, prompt evaluation using CT imaging and other tests can help to determine the type of stroke, to assess the severity of damage, and to guide treatment decisions. RapidAI is an artificial intelligence (AI)–enabled software platform that facilitates the viewing, processing, and analysis of CT images to aid clinicians in assessing patients with suspected stroke. Understanding the potential benefits and harms of using RapidAI is important to clarify its role in stroke detection. What Did We Do? We sought to identify, synthesize, and critically appraise literature evaluating the effectiveness, accuracy, and cost-effectiveness of RapidAI for detecting large-vessel occlusion (LVO) (i.e., ischemic stroke) and intracranial hemorrhage (ICH) (i.e., hemorrhagic stroke). We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published up to July 22, 2024. We screened citations for inclusion based on predefined criteria, critically appraised the included studies, narratively summarized the findings, and assessed the certainty of evidence. Our methods were guided by the Scottish Health Technologies Group’s health technology assessment (HTA) framework. We highlighted and reflected on the ethical and equity implications of using RapidAI for stroke detection, found in the clinical literature, integrating these considerations throughout the review. We engaged a patient contributor who had experienced a hemorrhagic stroke, to learn about her experience, perspectives, and priorities. Additionally, we incorporated feedback from clinical and ethics experts, the manufacturer, and other interested parties. What Did We Find? We found 2 cohort studies and 11 diagnostic accuracy studies that assessed the effectiveness and accuracy of RapidAI for detecting stroke. Among these, 3 studies evaluated RapidAI as it is intended to be used in clinical practice (i.e., to complement clinician interpretation of CT images), while the remaining 10 studies assessed RapidAI as a standalone intervention. The patient contributor identified important outcomes for stroke care, including improving speed and accuracy of diagnosis, minimizing the damaging effects of stroke, and reducing mortality rates. She also highlighted ethical considerations regarding the use of AI in health care, such as providing data privacy and equitable access, as well as informing patients about the use of AI technologies in the care pathway. Low-certainty evidence suggests that evaluation of CT angiography images by Rapid LVO combined with clinician interpretation, compared to clinician interpretation alone, may result in clinically important reductions in radiology-report turnaround time in patients with suspected stroke. For detecting ICH, low-certainty evidence suggests that Rapid ICH combined with clinician interpretation, using clinician interpretation as a reference standard, has a sensitivity of 92% (95% confidence interval [CI], 78% to 98%) and a specificity of 100% (95% CI, 98% to 100%). However, estimates of sensitivity and specificity for detecting LVO varied, based on studies using different modules of RapidAI as a standalone intervention, providing only indirect accuracy data. The effects of RapidAI on other time-to-intervention metrics, measures of physical and cognitive function, and response to therapy (e.g., reperfusion rates) were very uncertain. We did not identify any evidence on the effects of RapidAI on many important clinical outcomes, including patient harms, mortality, health-related quality of life, length of hospital stay, or health care resource implications. We did not find any studies on the cost-effectiveness of RapidAI for detecting stroke that met our selection criteria for this review. Ethical and equity considerations related to patient autonomy, privacy, transparency, access, and algorithmic bias have implications across the technology life cycle when using RapidAI for detecting stroke. What Does This Mean? RapidAI has the potential to improve acute stroke care by creating efficiencies in the diagnostic process. However, the impact of RapidAI on many outcomes, including those that are important to patients, is uncertain due to limitations of the available evidence. To improve the certainty of findings, there is a need for evidence from robustly conducted studies at lower risk of bias that enrol diverse patient populations and measure outcomes that are important to patients, with improved reporting. The cost-effectiveness of RapidAI for stroke detection is currently unknown. In addition to the evidence on the effectiveness and accuracy of RapidAI for detecting stroke, decision-makers may wish to reflect on the ethical and equity considerations that arise during the deployment of AI-enabled technologies, such as those related to autonomy, privacy, transparency, and explainability of machine-learning models, and the need for considerations related to equity and access in their design, development, and deployment. AI Implementation Review What Is the Issue? Globally, we are seeing a widespread increase in the interest, development, and use of artificial intelligence (AI)–enabled medical devices. Comprehensive evaluation through health technology assessment (HTA) can ensure that digital health technologies (DHTs), including AI-enabled medical devices, are adequately equipped to balance benefits and harms, while being interoperable and equitably accessible to people living in Canada. In the UK, a checklist called Digital Technology Assessment Criteria (DTAC) is used as an add-on component to HTAs to capture additional considerations for the implementation of DHTs. The 5 core areas of DTAC are clinical safety, data protection, technical security, interoperability, and usability and accessibility. In Canada, we currently do not have a DTAC equivalent that can be used as an add-on to traditional HTA. This implementation review is needed to assist health systems in Canada in preparing for the uptake of AI-enabled medical devices, as these technologies pose new challenges. We assessed whether the safeguards and assessment criteria captured by DTAC and other AI-related resources are in place to inform decision-making around the digital infrastructure elements of implementation. What Did We Do? We conducted an implementation review, using a phased approach, to determine whether DTAC can be applied to the health care context in Canada to inform the implementation of DHTs and to identify any additional implementation considerations specific to the use of AI-enabled medical devices in Canada. We integrated ethics and equity considerations across both phases of the review. In phase 1, we applied DTAC to the health care context in Canada by determining whether we have equivalent or similar measures, strategies, and policies in place to implement DHTs safely. In phase 2, an information specialist searched for literature to identify implementation guidance specific to AI and relevant to Canada to supplement DTAC. One reviewer screened publications for inclusion based on predefined criteria, incorporated relevant information into tables, and summarized the findings narratively. We leveraged patient engagement activities conducted in a concurrent Canada’s Drug Agency review of a specific AI-enabled medical device in stroke detection to learn from a patient contributor with lived experience of a hemorrhagic stroke. We learned about her experience, perspectives, priorities, and thoughts about using AI in clinical decision-making. What Did We Find? With some caveats, we found that many of DTAC’s assessment criteria have equivalent or similar guidance for the health care context in Canada. Some exceptions are derived from the differences in Canada’s current governance and health care structure. Further investigation is required to understand whether certain policies in Canada provide sufficient coverage to fulfill DTAC’s criteria (e.g., clinical safety). We identified several considerations for implementing AI-enabled medical devices, with many having underlying ethical and equity implications. Much of the identified guidance emphasizes implementation considerations that apply to the AI system’s entire life cycle, including the most prevalent consideration: ensuring AI-enabled medical devices are monitored, maintained, and sustainable. Examples of additional considerations include AI data governance and data protection; transparency and explainability; and inclusiveness, equity, and minimization of bias. The patient contributor highlighted several considerations relevant for this review, such as data protection and privacy as well as accessibility and equity. What Does This Mean? We have identified key considerations for AI-enabled medical devices that health care decision-makers may consider for the safe and successful implementation of AI in health care in Canada. While Canada has DTAC-equivalent or similar measures, strategies, or policies in place, we identified a need for a checklist like DTAC that senior decision-makers can use. This checklist could be an adaptation of DTAC and could include additional implementation considerations for AI-enabled medical devices to ensure that these technologies meet the minimum baseline standards set out by DTAC and inform the next steps for the safe and successful implementation of AI-enabled medical devices in Canada. This implementation review for all AI-enabled medical devices is to be used alongside reviews of specific AI technologies, including the concurrent review of RapidAI, and will serve as a foundational report to be tailored for each AI topic and updated with the latest developments in the regulation and other aspects of management of AI in the context of Canada.