The Artificial Third: A Broad View of the Effects of Introducing Generative Artificial Intelligence on Psychotherapy.

Unlabelled: This paper explores a significant shift in the field of mental health in general and psychotherapy in particular following generative artificial intelligence's new capabilities in processing and generating humanlike language. Following Freud, this lingo-technological development is conceptualized as the "fourth narcissistic blow" that science inflicts on humanity. We argue that this narcissistic blow has a potentially dramatic influence on perceptions of human society, interrelationships, and the self. We should, accordingly, expect dramatic changes in perceptions of the therapeutic act following the emergence of what we term the artificial third in the field of psychotherapy. The introduction of an artificial third marks a critical juncture, prompting us to ask the following important core questions that address two basic elements of critical thinking, namely, transparency and autonomy: (1) What is this new artificial presence in therapy relationships? (2) How does it reshape our perception of ourselves and our interpersonal dynamics? and (3) What remains of the irreplaceable human elements at the core of therapy? Given the ethical implications that arise from these questions, this paper proposes that the artificial third can be a valuable asset when applied with insight and ethical consideration, enhancing but not replacing the human touch in therapy.

Algor-ethics: charting the ethical path for AI in critical care.

The integration of Clinical Decision Support Systems (CDSS) based on artificial intelligence (AI) in healthcare is groundbreaking evolution with enormous potential, but its development and ethical implementation, presents unique challenges, particularly in critical care, where physicians often deal with life-threating conditions requiring rapid actions and patients unable to participate in the decisional process. Moreover, development of AI-based CDSS is complex and should address different sources of bias, including data acquisition, health disparities, domain shifts during clinical use, and cognitive biases in decision-making. In this scenario algor-ethics is mandatory and emphasizes the integration of 'Human-in-the-Loop' and 'Algorithmic Stewardship' principles, and the benefits of advanced data engineering. The establishment of Clinical AI Departments (CAID) is necessary to lead AI innovation in healthcare, ensuring ethical integrity and human-centered development in this rapidly evolving field.

Charting the future of patient care: A strategic leadership guide to harnessing the potential of artificial intelligence.

Artificial Intelligence (AI) applications have the potential to revolutionize conventional healthcare practices, creating a more efficient and patient-centred approach with improved outcomes. This guide discuses eighteen AI-based applications in clinical decision-making, precision medicine, operational efficiency, and predictive analytics, including a real-world example of AI's role in public health during the early stages of the COVID-19 pandemic. Additionally, we address ethical questions, transparency, data privacy, bias, consent, accountability, and liability, and the strategic measures that must be taken to align AI with ethical principles, legal frameworks, legacy information technology systems, and employee skills and knowledge. We emphasize the importance of informed and strategic approaches to harness AI's potential and manage its challenges. Moreover, this guide underscores the importance of evaluating and integrating new skills and competencies to navigate and use AI-based technologies in healthcare management, such as technological literacy, long-term strategic vision, change management skills, ethical decision-making, and alignment with patient needs.

[Analysis of the challenges and dilemmas that bioethics of the 21st century will face in the digital health era]. / Análisis de retos y dilemas que deberá afrontar la bioética del siglo xxi, en la era de la salud digital.

The medical history underscores the significance of ethics in each advancement, with bioethics playing a pivotal role in addressing emerging ethical challenges in digital health (DH). This article examines the ethical dilemmas of innovations in DH, focusing on the healthcare system, professionals, and patients. Artificial Intelligence (AI) raises concerns such as confidentiality and algorithmic biases. Mobile applications (Apps) empower but pose challenges of access and digital literacy. Telemedicine (TM) democratizes and reduces healthcare costs but requires addressing the digital divide and interconsultation dilemmas; it necessitates high-quality standards with patient information protection and attention to equity in access. Wearables and the Internet of Things (IoT) transform healthcare but face ethical challenges like privacy and equity. 21st-century bioethics must be adaptable as DH tools demand constant review and consensus, necessitating health science faculties' preparedness for the forthcoming changes.

Ethical considerations for artificial intelligence in dermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

The use of artificial intelligence (AI) in dermatology is rapidly increasing, with applications in dermatopathology, medical dermatology, cutaneous surgery, microscopy/spectroscopy and the identification of prognostic biomarkers (characteristics that provide information on likely patient health outcomes). However, with the rise of AI in dermatology, ethical concerns have emerged. We reviewed the existing literature to identify applications of AI in the field of dermatology and understand the ethical implications. Our search initially identified 202 papers, and after we went through them (screening), 68 were included in our review. We found that ethical concerns are related to the use of AI in the areas of clinical image analysis, teledermatology, natural language processing models, privacy, skin of colour representation, and patient and provider attitudes toward AI. We identified nine ethical principles to facilitate the safe use of AI in dermatology. These ethical principles include fairness, inclusivity, transparency, accountability, security, privacy, reliability, informed consent and conflict of interest. Although there are many benefits of integrating AI into clinical practice, our findings highlight how safeguards must be put in place to reduce rising ethical concerns.

The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations.

There is clear evidence to suggest that diabetes does not affect all populations equally. Among adults living with diabetes, those from ethnoracial minority communities-foreign-born, immigrant, refugee, and culturally marginalized-are at increased risk of poor health outcomes. Artificial intelligence (AI) is actively being researched as a means of improving diabetes management and care; however, several factors may predispose AI to ethnoracial bias. To better understand whether diabetes AI interventions are being designed in an ethnoracially equitable manner, we conducted a secondary analysis of 141 articles included in a 2018 review by Contreras and Vehi entitled "Artificial Intelligence for Diabetes Management and Decision Support: Literature Review." Two members of our research team independently reviewed each article and selected those reporting ethnoracial data for further analysis. Only 10 articles (7.1%) were ultimately selected for secondary analysis in our case study. Of the 131 excluded articles, 118 (90.1%) failed to mention participants' ethnic or racial backgrounds. The included articles reported ethnoracial data under various categories, including race (n=6), ethnicity (n=2), race/ethnicity (n=3), and percentage of Caucasian participants (n=1). Among articles specifically reporting race, the average distribution was 69.5% White, 17.1% Black, and 3.7% Asian. Only 2 articles reported inclusion of Native American participants. Given the clear ethnic and racial differences in diabetes biomarkers, prevalence, and outcomes, the inclusion of ethnoracial training data is likely to improve the accuracy of predictive models. Such considerations are imperative in AI-based tools, which are predisposed to negative biases due to their black-box nature and proneness to distributional shift. Based on our findings, we propose a short questionnaire to assess ethnoracial equity in research describing AI-based diabetes interventions. At this unprecedented time in history, AI can either mitigate or exacerbate disparities in health care. Future accounts of the infancy of diabetes AI must reflect our early and decisive action to confront ethnoracial inequities before they are coded into our systems and perpetuate the very biases we aim to eliminate. If we take deliberate and meaningful steps now toward training our algorithms to be ethnoracially inclusive, we can architect innovations in diabetes care that are bound by the diverse fabric of our society.

Patient safety and quality improvement: Ethical principles for a regulatory approach to bias in healthcare machine learning.

Accumulating evidence demonstrates the impact of bias that reflects social inequality on the performance of machine learning (ML) models in health care. Given their intended placement within healthcare decision making more broadly, ML tools require attention to adequately quantify the impact of bias and reduce its potential to exacerbate inequalities. We suggest that taking a patient safety and quality improvement approach to bias can support the quantification of bias-related effects on ML. Drawing from the ethical principles underpinning these approaches, we argue that patient safety and quality improvement lenses support the quantification of relevant performance metrics, in order to minimize harm while promoting accountability, justice, and transparency. We identify specific methods for operationalizing these principles with the goal of attending to bias to support better decision making in light of controllable and uncontrollable factors.

Ethics Implications of the Use of Artificial Intelligence in Violence Risk Assessment.

Artificial intelligence is rapidly transforming the landscape of medicine. Specifically, algorithms powered by deep learning are already gaining increasingly wide adoption in fields such as radiology, pathology, and preventive medicine. Forensic psychiatry is a complex and intricate specialty that seeks to balance the disparate approaches of psychiatric science, which strives to explain human behavior deterministically, and the law, which emphasizes free choice and moral responsibility. This balancing, a central task of the forensic psychiatrist, is necessarily fraught with ambiguity. Such a complex task may intuitively seem impenetrable to artificial intelligence. This article first aims to challenge this assumption and then seeks to address the unique concerns posed by the adoption of artificial intelligence in violence risk assessment and prediction. The relevant ethics concerns are analyzed within the framework of traditional bioethics principles. Finally, recommendations for practitioners, ethicists, and others are offered as a starting point for further discussion.

Intelligent, Autonomous Machines in Surgery.

Surgeons perform two primary tasks: operating and engaging patients and caregivers in shared decision-making. Human dexterity and decision-making are biologically limited. Intelligent, autonomous machines have the potential to augment or replace surgeons. Rather than regarding this possibility with denial, ire, or indifference, surgeons should understand and steer these technologies. Closer examination of surgical innovations and lessons learned from the automotive industry can inform this process. Innovations in minimally invasive surgery and surgical decision-making follow classic S-shaped curves with three phases: (1) introduction of a new technology, (2) achievement of a performance advantage relative to existing standards, and (3) arrival at a performance plateau, followed by replacement with an innovation featuring greater machine autonomy and less human influence. There is currently no level I evidence demonstrating improved patient outcomes using intelligent, autonomous machines for performing operations or surgical decision-making tasks. History suggests that if such evidence emerges and if the machines are cost effective, then they will augment or replace humans, initially for simple, common, rote tasks under close human supervision and later for complex tasks with minimal human supervision. This process poses ethical challenges in assigning liability for errors, matching decisions to patient values, and displacing human workers, but may allow surgeons to spend less time gathering and analyzing data and more time interacting with patients and tending to urgent, critical-and potentially more valuable-aspects of patient care. Surgeons should steer these technologies toward optimal patient care and net social benefit using the uniquely human traits of creativity, altruism, and moral deliberation.

Ethics in Health Informatics.

Contemporary bioethics was fledged and is sustained by challenges posed by new technologies. These technologies have affected many lives. Yet health informatics affects more lives than any of them. The challenges include the development and the appropriate uses and users of machine learning software, the balancing of privacy rights against the needs of public health and clinical practice in a time of Big Data analytics, whether and how to use this technology, and the role of ethics and standards in health policy. Historical antecedents in statistics and evidence-based practice foreshadow some of the difficulties now faced, but the scope and scale of these challenges requires that ethics, too, be brought to scale in parallel, especially given the size of contemporary data sets and the processing power of new computers. Fortunately, applied ethics affords a variety of tools to help identify and rank applicable values, support best practices, and contribute to standards. The bioethics community can in partnership with the informatics community arrive at policies that promote the health sciences while reaffirming the many and varied rights that patients expect will be honored.

Defining ethical standards for the application of digital tools to population health research.

There is growing interest in population health research, which uses methods based on artificial intelligence. Such research draws on a range of clinical and non-clinical data to make predictions about health risks, such as identifying epidemics and monitoring disease spread. Much of this research uses data from social media in the public domain or anonymous secondary health data and is therefore exempt from ethics committee scrutiny. While the ethical use and regulation of digital-based research has been discussed, little attention has been given to the ethics governance of such research in higher education institutions in the field of population health. Such governance is essential to how scholars make ethical decisions and provides assurance to the public that researchers are acting ethically. We propose a process of ethics governance for population health research in higher education institutions. The approach takes the form of review after the research has been completed, with particular focus on the role artificial intelligence algorithms play in augmenting decision-making. The first layer of review could be national, open-science repositories for open-source algorithms and affiliated data or information which are developed during research. The second layer would be a sector-specific validation of the research processes and algorithms by a committee of academics and stakeholders with a wide range of expertise across disciplines. The committee could be created as an off-shoot of an already functioning national oversight body or health technology assessment organization. We use case studies of good practice to explore how this process might operate.

La recherche sur la santé de la population à l'aide de méthodes fondées sur l'intelligence artificielle suscite un intérêt croissant. Ce type de recherche s'appuie sur une série de données cliniques et non cliniques pour prédire les risques sanitaires, par exemple en détectant les épidémies et en surveillant la propagation des maladies. Une part importante de cette recherche emploie des données issues des réseaux sociaux, appartenant au domaine public, ou des données secondaires anonymes relatives à la santé. Par conséquent, elles ne sont soumises à aucun contrôle de la part d'un comité d'éthique. L'usage et les règles déontologiques ont certes fait l'objet de discussions, mais l'attention portée à la gouvernance de l'éthique dans le cadre des recherches que des établissements d'enseignement supérieur ont menées sur la santé des populations reste minime. Pourtant, une telle gouvernance est essentielle pour que les spécialistes puissent prendre des décisions éthiques et garantir au public que les chercheurs agissent dans le respect de la déontologie. Nous proposons un processus de gouvernance éthique pour la recherche sur la santé de la population dans les établissements d'enseignement supérieur. Notre approche consiste à établir un rapport à la fin de la recherche, qui se concentre sur le rôle joué par les algorithmes d'intelligence artificielle dans l'accroissement de la prise de décisions. Le premier niveau de rapport pourrait comporter des registres nationaux accessibles selon le principe de science ouverte pour les algorithmes open-source ainsi que les données ou informations connexes développés durant la recherche. Le second niveau serait composé d'une validation sectorielle des algorithmes et processus de recherche par un comité d'universitaires et d'intervenants possédant une large gamme de compétences dans diverses disciplines. Ce comité pourrait être créé en tant que ramification d'un organisme national de surveillance déjà à l'œuvre, ou d'un organisme d'évaluation des technologies de la santé. Nous utilisons des études de cas pour identifier les bonnes pratiques et découvrir comment ce processus pourrait être appliqué.

Existe un interés creciente en la investigación sanitaria poblacional, que utiliza métodos basados en la inteligencia artificial. Dicha investigación se basa en una serie de datos clínicos y no clínicos para hacer predicciones sobre los riesgos sanitarios, como la identificación de epidemias y el seguimiento de la propagación de enfermedades. Gran parte de esta investigación utiliza los datos de las redes sociales de dominio público o los datos sanitarios secundarios anónimos y, por lo tanto, está exenta del escrutinio del comité de ética. Si bien se ha debatido sobre el uso y la regulación éticos de la investigación basada en tecnología digital, se ha prestado poca atención a la gobernanza ética de dicha investigación en las instituciones de enseñanza superior relacionadas con la salud poblacional. Esa gobernanza es esencial sobre cómo los académicos toman decisiones éticas y ofrece garantías al público de que los investigadores actúan de manera ética. Se propone un proceso de gobernanza ética para la investigación sanitaria poblacional en las instituciones de educación superior. El enfoque adopta la forma de una revisión una vez que la investigación ha sido completada, con especial atención a la función que los algoritmos de inteligencia artificial desempeñan en el aumento de la toma de decisiones. La primera fase de revisión podría consistir en la creación de repositorios nacionales de ciencia abierta para los algoritmos de código abierto y los datos o información afiliados que se desarrollen durante la investigación. La segunda fase consistiría en la validación de los procesos y algoritmos de investigación en un sector específico por parte de un comité de académicos y partes interesadas con una amplia gama de conocimientos especializados en todas las disciplinas. El comité podría crearse como una rama de un organismo nacional de supervisión ya en funcionamiento o de una organización de evaluación sobre tecnologías de la salud. Se utilizan estudios de casos de buenas prácticas para explorar cómo podría funcionar este proceso.

Ensuring trustworthy use of artificial intelligence and big data analytics in health insurance.

Technological advances in big data (large amounts of highly varied data from many different sources that may be processed rapidly), data sciences and artificial intelligence can improve health-system functions and promote personalized care and public good. However, these technologies will not replace the fundamental components of the health system, such as ethical leadership and governance, or avoid the need for a robust ethical and regulatory environment. In this paper, we discuss what a robust ethical and regulatory environment might look like for big data analytics in health insurance, and describe examples of safeguards and participatory mechanisms that should be established. First, a clear and effective data governance framework is critical. Legal standards need to be enacted and insurers should be encouraged and given incentives to adopt a human-centred approach in the design and use of big data analytics and artificial intelligence. Second, a clear and accountable process is necessary to explain what information can be used and how it can be used. Third, people whose data may be used should be empowered through their active involvement in determining how their personal data may be managed and governed. Fourth, insurers and governance bodies, including regulators and policy-makers, need to work together to ensure that the big data analytics based on artificial intelligence that are developed are transparent and accurate. Unless an enabling ethical environment is in place, the use of such analytics will likely contribute to the proliferation of unconnected data systems, worsen existing inequalities, and erode trustworthiness and trust.

Les progrès technologiques en matière de big data (un terme qui désigne de grandes quantités de données extrêmement variées, provenant de différentes sources et pouvant être traitées rapidement), de sciences de l'information et d'intelligence artificielle peuvent améliorer le fonctionnement du système de santé, mais aussi promouvoir des soins personnalisés et servir l'intérêt public. Néanmoins, ces technologies ne permettront pas de remplacer les composantes fondamentales du système de santé, comme le leadership éthique et la bonne gouvernance, ni d'éviter la nécessité de créer un environnement déontologique et réglementaire solide. Le présent document se penche sur la définition de cet environnement déontologique et réglementaire solide pour l'analyse des big data dans le domaine de l'assurance maladie, et fournit à titre d'exemple les mécanismes de protection et de participation qu'il convient d'instaurer. En premier lieu, imposer un cadre de gouvernance précis et efficace est essentiel au traitement des données. Des normes juridiques doivent être promulguées, tandis que les assureurs doivent être encouragés et incités à adopter une approche centrée sur l'humain, tant dans leur conception que dans leur utilisation de l'analyse des big data et de l'intelligence artificielle. Deuxièmement, il faut mettre en place un processus clair et responsable afin d'expliquer quels types d'informations sont susceptibles d'être employés et à quelles fins. Troisièmement, les personnes concernées doivent avoir la possibilité de déterminer de quelle manière leurs données personnelles sont gérées et régies, en étant activement impliquées dans ce processus. Et quatrièmement, les assureurs et les organes de gouvernance, dont les régulateurs et législateurs, doivent collaborer pour faire en sorte que l'analyse des big data basée sur l'intelligence artificielle soit correcte et transparente. À moins d'établir un environnement éthique, l'usage d'une telle analyse entraînera probablement la prolifération de systèmes de données non connectés, l'aggravation des inégalités actuelles ainsi qu'une perte de confiance et de fiabilité.

Los avances tecnológicos relativos a los macrodatos (es decir, grandes cantidades de datos muy variados de muchas fuentes diversas que pueden procesarse rápidamente), las ciencias de los datos y la inteligencia artificial pueden mejorar las funciones del sistema sanitario y promover la atención personalizada y el bien público. No obstante, estas tecnologías no sustituirán los componentes fundamentales del sistema sanitario, como el liderazgo ético y la gobernanza, ni evitarán la necesidad de un entorno ético y normativo sólido. En el presente documento se examina cómo podría ser un entorno ético y normativo sólido para el análisis de macrodatos en el ámbito de los seguros médicos, y se describen ejemplos de mecanismos de protección y participación que deberían establecerse. En primer lugar, es fundamental contar con un marco claro y eficaz de gestión de datos. Es necesario promulgar normas jurídicas y alentar e incentivar a las aseguradoras para que adopten un enfoque centrado en el ser humano en el diseño y la aplicación de análisis de macrodatos e inteligencia artificial. En segundo lugar, es necesario un proceso claro y responsable para explicar cómo y qué información se puede utilizar. En tercer lugar, se debe facultar a las personas cuyos datos puedan ser utilizados mediante su participación activa en la determinación de cómo se pueden gestionar y regular sus datos personales. En cuarto lugar, las aseguradoras y los órganos de gobierno, incluidos los reguladores y los responsables de formular políticas, deben colaborar para garantizar que los análisis de macrodatos basados en la inteligencia artificial que se elaboren sean transparentes y precisos. A menos que exista un entorno ético adecuado, el uso de esos análisis probablemente contribuirá a la proliferación de sistemas de datos sin conexión, empeorará las desigualdades existentes y reducirá la fiabilidad y la confianza.

The ethical, legal and social implications of using artificial intelligence systems in breast cancer care.

Breast cancer care is a leading area for development of artificial intelligence (AI), with applications including screening and diagnosis, risk calculation, prognostication and clinical decision-support, management planning, and precision medicine. We review the ethical, legal and social implications of these developments. We consider the values encoded in algorithms, the need to evaluate outcomes, and issues of bias and transferability, data ownership, confidentiality and consent, and legal, moral and professional responsibility. We consider potential effects for patients, including on trust in healthcare, and provide some social science explanations for the apparent rush to implement AI solutions. We conclude by anticipating future directions for AI in breast cancer care. Stakeholders in healthcare AI should acknowledge that their enterprise is an ethical, legal and social challenge, not just a technical challenge. Taking these challenges seriously will require broad engagement, imposition of conditions on implementation, and pre-emptive systems of oversight to ensure that development does not run ahead of evaluation and deliberation. Once artificial intelligence becomes institutionalised, it may be difficult to reverse: a proactive role for government, regulators and professional groups will help ensure introduction in robust research contexts, and the development of a sound evidence base regarding real-world effectiveness. Detailed public discussion is required to consider what kind of AI is acceptable rather than simply accepting what is offered, thus optimising outcomes for health systems, professionals, society and those receiving care.

Translational Research-For the Individual and the Community.

The potential for translational research to improve human health is unprecedented, as the integration of genetic health risks with other data influencing health provides substantial opportunities for improvement. However, how integrating these data sources in a fair, unbiased and appropriate way without reinforcing pre-existing assumptions requires thoughtful implementation. Furthermore, integration of new technologies requires assessment of needs and benefits for the individual balanced with community needs and goals. Thus, examination of values, goals and implicit assumptions through transparent, authentic engagement of individuals and communities is essential.