Ethical challenges of using artificial intelligence in suicide prevention: a literature review

Background/Objectives: Assisted suicide and suicide prevention remain subjects of intense societal, political, and professional-ethical debate in Germany. Nurses working in residential and home-based long-term care (LTC) play a pivotal role in responding to requests for assisted suicide and in supporting suicide prevention. While international research has explored diverse ethical perspectives and challenges related to these issues, little is known about how LTC nurses in Germany experience and navigate them. This study examines German LTC nurses’ ethical perspectives on assisted suicide and suicide prevention and explores the associated ethical challenges. Methods: A qualitative design employing both in-person and online focus groups was used. Data were analyzed following Mayring’s qualitative content analysis. Results: Twelve focus groups with a total of 96 nurses working in residential and home-based LTC were conducted between February and September 2025. Findings show that nurses perceive assisted suicide and suicide prevention as ethically complex and emotionally demanding. Three overarching themes emerged: (1) Intuitive and Emotional Reactions, (2) Ethical Perception and Ethical Reflection, and (3) Ethical Challenges. Conclusions: This study offers new insights into the diverse ethical perspectives of German LTC nurses on assisted suicide and suicide prevention. It extends existing knowledge through its explicit focus on the ethical issues and implications involved, both in residential and home-based LTC. The ethical challenges identified may enhance understanding of the factors underlying the development of moral distress in Germany and other countries where assisted suicide is a legal option. To help nurses navigate these ethically demanding situations, strategies at multiple levels are required. These include continuous ethics education, an open ethical culture, role definitions and clear professional guidance, alongside societal support for equitable access to general healthcare and suicide prevention services.

Artificial intelligence (AI) is a relatively new medical resource with the potential to revolutionize current practices in the prevention and treatment of disease. AI has been defined as computer programs accomplishing tasks traditionally associated with human intelligence such as learning and solving problems. As the ethical benefits of increased efficiency and productivity of AI systems are being realized, the consequences of implementing such transformative technologies has raised ethical and regulatory questions across the globe. AI represents a tool to address longstanding issues in healthcare delivery and can achieve a caliber of healthcare quality that was previously beyond our grasp. However, AI systems may incorporate and often amplify existing patterns of practice, including societal biases and inequitable healthcare practices. Surmounting these ethical and regulatory challenges represents the next frontier in the successful implementation of AI to promote human development and wellbeing. In this study, we examined the current literature and analyzed the scope of practice around the ethical and regulatory issues surrounding AI in medicine and its application to healthcare. Knowledge integration was performed across disciplines relevant to the potential role for AI in facilitating progress, innovation, and quality assurance in healthcare. Thematic analysis was conducted on qualitative data pertaining to both ethical and regulatory challenges concerning the implementation of AI into healthcare practices. The project provided exposure to the innovative field of AI and various strategies related to ethical issues, regulatory laws, quality improvement, and healthcare management. We explored both the reliability and current limitations of AI in order to create best practices guidelines designed to facilitate the successful incorporation of AI into healthcare fields. Ethical challenges of AI such as risk management, data security, and a lack of transparency span all sectors working to implement these new technologies. All medical disciplines working to leverage the potential applications of AI struggle with the ethical challenges of informed consent, autonomy, accountability, biases, and equitable healthcare delivery. The field of laboratory medicine and pathology was a pioneer in the implementation of AI technology. Laboratory medicine and pathology face additional hurdles when ensuring accurate interpretation of results such as unequal contexts, opportunity costs, and low levels of acceptable risk and uncertainty. Rather than an all-or-nothing approach, we suggest a stepwise, transparent, and patient-centered approach with clear boundaries to the incorporation of new tools. The AI-assisted era of medical care will be transformative but will never be void of all risk or ethical challenges. This work represents the first of many steps in using AI technology to optimize healthcare delivery in a way that protects and strengthens the ethical values of medical care.

The rapid deployment of Artificial Intelligence (AI) in Anti-Money Laundering (AML) practices within the financial industry presents significant ethical and governance challenges that must be navigated effectively. As financial institutions increasingly adopt AI technologies to enhance their AML efforts, concerns regarding data privacy, algorithmic bias, and transparency emerge. This review explores the ethical implications of AI in AML and offers governance strategies to mitigate risks while ensuring compliance with regulatory frameworks. One of the primary ethical challenges in deploying AI for AML is the potential for algorithmic bias. AI systems trained on historical data may inadvertently perpetuate existing biases, leading to discriminatory practices in transaction monitoring and customer profiling. This raises serious concerns about fairness and equity in the financial sector. Addressing algorithmic bias requires the implementation of rigorous testing and validation processes to ensure AI systems function impartially across diverse populations. Data privacy is another critical issue. The extensive data collection required for effective AML monitoring raises questions about the protection of sensitive customer information. Financial institutions must establish robust data governance frameworks that prioritize privacy and comply with regulations such as the General Data Protection Regulation (GDPR). Ensuring transparency in how data is used and providing clear communication to customers about data practices is essential for building trust. Effective governance frameworks are crucial in navigating these ethical challenges. Financial institutions should adopt a multi-disciplinary approach that includes ethical guidelines, compliance measures, and risk management strategies. Establishing oversight committees can help ensure that AI deployment aligns with ethical standards and regulatory requirements. Furthermore, ongoing training for employees on the ethical use of AI in AML can foster a culture of responsibility and accountability. This review highlights the need for a balanced approach to AI deployment in AML, emphasizing the importance of ethical considerations and governance structures. As the financial industry continues to evolve, addressing these challenges will be essential for maintaining trust, ensuring compliance, and leveraging AI’s potential to enhance AML practices effectively.

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. This is given the for useful and applications and the for in sectors of the However, a few have made the development of their the most of these are and each of which has demonstrated results of increasing still a long way from the most discussed impact of AI and automation is on and the future of This is not In in the of the excitement about AI and and concerns about their impact on a on and the was that such technologies were important for growth and and "the that but not Most recent of this including those I have been involved have and that over time, more are than are that it is the and the and the of will the In their essay AI & and John discuss these for work and further, in & the of & to discuss the with to and and as well as the opportunities that are especially in developing In "The Turing The & of Artificial Intelligence," discusses how the use of human benchmarks in the development of AI the of AI that rather than human He that the AI's development will take in this and resulting for will on the for companies, and a that the that more will be than too much from of the and does not far enough into the future and at what AI will be capable The for AI could from of that in the is and labor and ability to are and and until automation has mostly physical and but that AI will be on more cognitive and tasks based on and, if early examples are even tasks are not of the In other are now in the world machines that that learn and that their ability to do these is to a range of problems they can will be with the range to which the human has been This was and Allen Newell in that this time could be different usually two that new labor will in which will by other humans for their own even when machines may be capable of these as well as or even better than The other is that AI will create so much and all without the for human and the of will be to for when that will the that once the first time since his creation will be with his his to use his from how to the which science and interest will have for to live and and However, most researchers that we are not to a future in which the of will and that until then, there are other and that be in the labor now and in the such as and other and how humans work increasingly capable that and John and discuss in this are not the only of the by AI. 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 &

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.

Suicide research and prevention are complex. Many practical, methodological and ethical challenges must be overcome to implement effective suicide prevention interventions. Implementation science can offer insights into what works, why and in what context. Yet, there are limited real-world examples of the application of implementation science in suicide prevention. This study aimed to identify approaches to employ principles of implementation science to tackle important challenges in suicide prevention. A questionnaire about promoting implementation science for suicide prevention was developed through thematic analysis of stakeholder narratives. Statements were categorised into six domains: research priorities, practical considerations, approach to intervention design and delivery, lived experience engagement, dissemination and the way forward. The questionnaire (n=52 statements-round 1; n=44 statements-round 2; n=9 statements-round 3) was administered electronically to a panel (n=62-round 1, n=48-round 2; n=45-round 3) of international experts (suicide researchers, leaders, project team members, lived experience advocates). Statements were rated on a Likert scale based on an understanding of importance and priority of each item. Statements endorsed by at least 85% of the panel would be included in the final guidelines. Eighty-two of the 90 statements were endorsed. Recommendations included broadening research inquiries to understand overall programme impact; accounting for resources in the translation of evidence into practice; embedding implementation science in intervention delivery and design; meaningfully engaging lived experience; considering channels for dissemination of implementation-related findings and focusing on next steps needed to routinely harness the strengths of implementation science in suicide prevention research, practice and training. An interdisciplinary panel of suicide prevention experts reached a consensus on optimal strategies for using implementation science to enhance the effectiveness of policies and programmes aimed at reducing suicide.

Maximising the Opportunities of Artificial Intelligence for People Living With Cancer

BackgroundAs artificial intelligence (AI) becomes increasingly embedded in clinical decision-making and preventive care, it is urgent to address ethical concerns such as bias, privacy, and transparency to protect clinician and patient populations. Although prior research has examined the perspectives of medical AI stakeholders, including clinicians, patients, and health system leaders, far less is known about how medical AI developers and researchers understand and engage with ethical challenges as they develop AI tools. This gap is consequential because developers’ ethical awareness, decision-making, and institutional environments influence how AI tools are conceptualized and deployed in practice. Thus, it is essential to understand how developers perceive these issues and what supports they identify as necessary for ethical AI development.ObjectiveThe objectives of the study were twofold: (1) to examine medical AI developers’ and researchers’ knowledge, attitudes, and experiences with AI ethics; and (2) to identify recommendations to enhance and strengthen interpersonal and institutional ethics-focused training and support.MethodsWe conducted 2 semistructured focus groups (60-90 minutes each) in 2024 with 13 AI developers and researchers affiliated with 5 US-based academic institutions. Participants’ work spanned a wide variety of medical AI applications, including Alzheimer disease prediction, clinical imaging, electronic health records analysis, digital health, counseling and behavioral health, and genotype–phenotype modeling. Focus groups were conducted via Microsoft Teams, recorded, and transcribed verbatim. We applied conventional qualitative content analysis to inductively identify emerging concepts, categories, and themes. Coding was performed independently by 3 researchers, with consensus reached through iterative team meetings.ResultsThe analysis identified four key themes: (1) AI ethics knowledge acquisition: participants reported learning about ethics informally through peer-reviewed literature, reviewer feedback, social media, and mentorship rather than through structured training; (2) ethical encounters: participants described recurring ethical challenges related to data bias, patient privacy, generative AI use, commercialization pressures, and a tendency for research environments to prioritize model accuracy over ethical reflection; (3) reflections on ethical implications: participants expressed concern about downstream effects on patient care and clinician autonomy, and model generalizability, noting that rapid technological innovation outpaces regulatory and evaluative processes; and (4) strategies to mitigate ethical concerns: recommendations included clearer institutional guidelines, ethics checklists, interdisciplinary collaboration, multi-institutional data sharing, enhanced institutional review board support, and the inclusion of bioethicists as members of the AI research team.ConclusionsMedical AI developers and researchers recognize significant ethical challenges in their work but lack structured training, resources, and institutional mechanisms to address them. Findings of this study underscore the need for institutions to consider embedding ethics into research processes through practical tools, mentorship, and interdisciplinary partnerships. Strengthening these supports is essential to preparing the next generation of developers to design and deploy ethical AI in health care.

HighlightsWhat are the main findings?Sri Lankan adolescents’ and teachers’ understanding of mental health is predominantly rooted in Buddhist perspectives.School environment plays a central role in exacerbating risk factors to poor mental health in adolescents.Sri Lankan adolescents have more knowledge of informal support sources rather than formal support for mental health-related concerns.What are the implications of the main findings?There is an opportunity for mental health promotion in Sri Lanka to leverage culturally contextualised language and frameworks.Schools are a key place to promote mental health to adolescents by integrating mental health programmes into the school curriculum and mental health promotion into routine educational practice.Background/Objectives: Across geographical and cultural contexts, how individuals identify, communicate and help-seek for distress is often shaped by how mental health itself is understood. Insight into how adolescents and adults in their routine environment, such as teachers, understand mental health is crucial for developing context-specific mental health promotion strategies to young people. Sri Lanka, a country that navigates the dual legacies of pre-and-post-colonial mental health frameworks, has this need. The aim was to explore Sri Lankan school-going adolescents’ and their teachers’ perspectives of mental health and its determinants. Methods: Semi-structured interviews were conducted with 28 school-going adolescents in grades 10–12/13 and 14 of their school teachers, from seven secondary schools in Gampaha District, Sri Lanka. Interviews were transcribed, translated, coded inductively and analysed thematically. Results: All participants drew on culturally meaningful language that is rooted in Buddhist perspectives to conceptualise mental health. Causes and risk factors of poor mental health were attributed to individual, immediate environmental and structural factors. School environment played a central role in exacerbating other risk factors. Adolescents exhibited more knowledge of informal care avenues for mental health-related concerns. Conclusions: Findings highlight several implications including opportunities to leverage culturally contextualised language/frameworks when promoting mental health to Sri Lankan adolescents, diversifying mental health research and initiating school-based mental health programmes that integrate mental health promotion into routine educational practice to transform learning institutions across Sri Lanka to become mental health-promoting schools.

This study aims to explore and analyze the ethical and legal challenges posed by the use of artificial intelligence (AI) in digital businesses, highlighting the need for robust guidelines and frameworks to guide responsible AI implementation. Adopting a qualitative research approach, data were collected through semi-structured interviews with 20 experts from diverse fields related to digital businesses and AI. The content analysis method was employed to identify main themes, categories, and concepts within the ethical and legal realms of AI usage. Two main themes emerged: ethical challenges and legal challenges. Ethical challenges encompassed categories such as privacy violations, discriminatory practices, and AI accountability. Legal challenges included intellectual property rights, data regulations, and surveillance and control issues. The analysis revealed a complex landscape of ethical dilemmas and legal uncertainties that digital businesses face when implementing AI technologies. The study concludes that navigating the ethical and legal challenges of AI in digital businesses requires a multi-faceted approach, emphasizing the importance of developing comprehensive ethical guidelines and legal frameworks. These measures should ensure AI technologies are used in ways that respect human rights and promote fairness and transparency in the digital economy.

In India, suicide research has largely concentrated on the prevalence, method, psychological, and demographic risk factors. Suicide processes, paradigms, prevention strategies, and other features of suicide that are common in the West may not be applicable in India. It is vital to study potential underlying processes, various suicide prevention methods, and suicide prevention in general, as well as what more work has to be done in the Indian context. Suicide, on the other hand, is a cross-sectoral public health issue that demands collaboration across all key sectors, and its prevention should engage all stakeholders in India.

Introduction: Clinical research is a key area in which the use of AI in healthcare data seen a significant increase, even though met with great ethical, legal and regulatory challenges. Artificial Intelligence (AI) concerns the ability of algorithms encoded in technology to learn from data, to be able to perform automated tasks without every step in the process being explicitly to be programmed by a human. AI development relies on big data collected from clinical trials to train algorithms, that requires careful consideration of consent, data origin and ethical standards. When data is acquired from third-party sources, transparency about collection methods, geographic origin and anonymization standards becomes critical. While consent forms used in clinical trials can offer clearer terms for data use, ambiguity remains about how this data can be reused for AI purposes after the trial ends. There are very few or no laws on the use of AI especially in developing countries. Also, there are a lot of misconceptions on the global use of AI. Statement of Objectives: Artificial intelligence as an innovative technology has contributed to a shift in paradigm in conducting clinical research. Unfortunately, AI faces ethical, and regulatory challenges especially in limited resource countries where the technology is still to be consolidated. One of the main concerns of AI involves data re-identification, in which anonymized data can potentially be traced back to individuals, especially when linked with other datasets. Data ownership is also a complex and often controversial area within the healthcare sector. AI developers needs to clearly explain the value of data collection to hospitals and cybersecurity teams to ensure that they understand how the data will be secured and used ethically Methodology: The World Health Organization (WHO) recognizes that AI holds great promise for clinical health research and in the practice of medicine, biomedical and pharmaceutical sciences. WHO also recognizes that, to fully maximize the contribution of AI, there is the need to address the ethical, legal and regulatory challenges for the health care systems, practitioners and beneficiaries of medical and public health services. In this study we have pulled data from accessible websites, peered reviewed open-access publications that deal with the ethical and regulatory concerns of AI, that we have discussed in this writeup. We have attempted to place our focus on the development of AI and applications with particular bias in the ethical and regulatory concerns. We have discussed and given an insight on whether AI can advance the interests of patients and communities within the framework of collective effort to design and implement ethically defensible laws and policies and ethically designed AI technologies. Finally, we have investigated the potential serious negative consequences of ethical principles and human rights obligations if they are not prioritized by those who fund, design, regulate or use AI technologies for health research. Results: From our data mining and access to multiple documentations, vital information has been pooled together by a systematic online search to show that AI is contributing significantly in the growth of global clinical research and advancement of medicine. However, we observed many ethical and regulatory challenges that has impacted health research in developing economies. Ethical challenges include AI and human rights, patient’s privacy, safety and liability, informed consent and data ownership, bias and fairness. For the legal and regulatory challenges, we observed issues with data security compliance, data monitoring and maintenance, transparency and accountability, data collection, data storage and use. The role of third-party vendors in AI healthcare solutions and finally AI development and integration into the health systems has also been reviewed. Conclusion: The advancement of AI, coupled with the innovative digital health technology has made a significant contribution to address some challenges in clinical research, within the domain of medicine, biomedical and pharmaceutical products development. Despite the challenging ethical and regulatory challenges AI has impacted significant innovation and technology in clinical research, especially within the domain of drug discovery and development, and clinical trials studies.

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To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care. AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care. A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing. The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing. The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards. Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing. AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance. Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.