Evolving Landscape of Ethics in Oncology: A Journey Through the Past, Present, and Future.

Providing a brief overview of past, present, and future ethics issues in oncology, this article begins with historical contexts, including the paternalistic approach to cancer care. It delves into present-day challenges such as navigating cancer treatment during pregnancy and addressing health care disparities faced by LGBTQ+ individuals. It also explores the ethical implications of emerging technologies, notably artificial intelligence and Big Data, in clinical decision making and medical education.

The selective deployment of AI in healthcare: An ethical algorithm for algorithms.

Machine-learning algorithms have the potential to revolutionise diagnostic and prognostic tasks in health care, yet algorithmic performance levels can be materially worse for subgroups that have been underrepresented in algorithmic training data. Given this epistemic deficit, the inclusion of underrepresented groups in algorithmic processes can result in harm. Yet delaying the deployment of algorithmic systems until more equitable results can be achieved would avoidably and foreseeably lead to a significant number of unnecessary deaths in well-represented populations. Faced with this dilemma between equity and utility, we draw on two case studies involving breast cancer and melanoma to argue for the selective deployment of diagnostic and prognostic tools for some well-represented groups, even if this results in the temporary exclusion of underrepresented patients from algorithmic approaches. We argue that this approach is justifiable when the inclusion of underrepresented patients would cause them to be harmed. While the context of historic injustice poses a considerable challenge for the ethical acceptability of selective algorithmic deployment strategies, we argue that, at least for the case studies addressed in this article, the issue of historic injustice is better addressed through nonalgorithmic measures, including being transparent with patients about the nature of the current epistemic deficits, providing additional services to algorithmically excluded populations, and through urgent commitments to gather additional algorithmic training data from excluded populations, paving the way for universal algorithmic deployment that is accurate for all patient groups. These commitments should be supported by regulation and, where necessary, government funding to ensure that any delays for excluded groups are kept to the minimum. We offer an ethical algorithm for algorithms-showing when to ethically delay, expedite, or selectively deploy algorithmic systems in healthcare settings.

[Ethics and artificial intelligence]. / Ethik und künstliche Intelligenz.

The introduction of artificial intelligence (AI) into radiology promises to enhance efficiency and improve diagnostic accuracy, yet it also raises manifold ethical questions. These include data protection issues, the future role of radiologists, liability when using AI systems, and the avoidance of bias. To prevent data bias, the datasets need to be compiled carefully and to be representative of the target population. Accordingly, the upcoming European Union AI act sets particularly high requirements for the datasets used in training medical AI systems. Cognitive bias occurs when radiologists place too much trust in the results provided by AI systems (overreliance). So far, diagnostic AI systems are used almost exclusively as "second look" systems. If diagnostic AI systems are to be used in the future as "first look" systems or even as autonomous AI systems in order to enhance efficiency in radiology, the question of liability needs to be addressed, comparable to liability for autonomous driving. Such use of AI would also significantly change the role of radiologists.

Ethics for AI in Plastic Surgery: Guidelines and Review.

INTRODUCTION: Artificial intelligence (AI) holds the potential to revolutionize medicine, offering vast improvements for plastic surgery. While human physicians are limited to one lifetime of experience, AI is poised to soon surpass human capabilities, as it draws on limitless information and continuous learning abilities. Nevertheless, as AI becomes increasingly prevalent in this domain, it gives rise to critical ethical considerations that must be addressed by professionals. MATERIALS AND METHODS: This work reviews the literature referring to the ethical challenges brought on by the ever-expanding use of AI in plastic surgery and offers guidelines for its application. RESULTS: Ethical challenges include the disclosure of use of AI by caregivers, validation of decision-making, data privacy, informed consent and autonomy, potential biases in AI systems, the opaque nature of AI models, questions of liability, and the need for regulations. CONCLUSIONS: There is a lack of consensus for the ethical use of AI in plastic surgery. Guidelines, such as those presented in this work, are needed within each discipline of medicine to respond to important ethical considerations for the safe use of AI. LEVEL OF EVIDENCE V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

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.

Ethics of artificial intelligence in dermatology.

The integration of artificial intelligence (AI) in dermatology holds promise for enhancing clinical accuracy, enabling earlier detection of skin malignancies, suggesting potential management of skin lesions and eruptions, and promoting improved continuity of care. AI implementation in dermatology, however, raises several ethical concerns. This review explores the current benefits and challenges associated with AI integration, underscoring ethical considerations related to autonomy, informed consent, and privacy. We also examine the ways in which beneficence, nonmaleficence, and distributive justice may be impacted. Clarifying the role of AI, striking a balance between security and transparency, fostering open dialogue with our patients, collaborating with developers of AI, implementing educational initiatives for dermatologists and their patients, and participating in the establishment of regulatory guidelines are essential to navigating ethical and responsible AI incorporation into dermatology.

Behind the mask: a critical perspective on the ethical, moral, and legal implications of AI in ophthalmology.

PURPOSE: This narrative review aims to provide an overview of the dangers, controversial aspects, and implications of artificial intelligence (AI) use in ophthalmology and other medical-related fields. METHODS: We conducted a decade-long comprehensive search (January 2013-May 2023) of both academic and grey literature, focusing on the application of AI in ophthalmology and healthcare. This search included key web-based academic databases, non-traditional sources, and targeted searches of specific organizations and institutions. We reviewed and selected documents for relevance to AI, healthcare, ethics, and guidelines, aiming for a critical analysis of ethical, moral, and legal implications of AI in healthcare. RESULTS: Six main issues were identified, analyzed, and discussed. These include bias and clinical safety, cybersecurity, health data and AI algorithm ownership, the "black-box" problem, medical liability, and the risk of widening inequality in healthcare. CONCLUSION: Solutions to address these issues include collecting high-quality data of the target population, incorporating stronger security measures, using explainable AI algorithms and ensemble methods, and making AI-based solutions accessible to everyone. With careful oversight and regulation, AI-based systems can be used to supplement physician decision-making and improve patient care and outcomes.

The diagnostic, therapeutic, and ethical impact of artificial intelligence in modern medicine.

In the evolution of modern medicine, artificial intelligence (AI) has been proven to provide an integral aspect of revolutionizing clinical diagnosis, drug discovery, and patient care. With the potential to scrutinize colossal amounts of medical data, radiological and histological images, and genomic data in healthcare institutions, AI-powered systems can recognize, determine, and associate patterns and provide impactful insights that would be strenuous and challenging for clinicians to detect during their daily clinical practice. The outcome of AI-mediated search offers more accurate, personalized patient diagnoses, guides in research for new drug therapies, and provides a more effective multidisciplinary treatment plan that can be implemented for patients with chronic diseases. Among the many promising applications of AI in modern medicine, medical imaging stands out distinctly as an area with tremendous potential. AI-powered algorithms can now accurately and sensitively identify cancer cells and other lesions in medical images with greater accuracy and sensitivity. This allows for earlier diagnosis and treatment, which can significantly impact patient outcomes. This review provides a comprehensive insight into diagnostic, therapeutic, and ethical issues with the advent of AI in modern medicine.

Tan lejos de la investigación científica, tan cerca de la inteligencia artificial / So far from scientific research, so close to artificial intelligence

En este editorial se exploran los posibles riesgos que representa el uso de la inteligencia artificial para la elaboración de trabajos académicos y científicos. Además, se presenta una lista de riesgos para la investigación científica elaborada por la plataforma ChatGPT con el objetivo de explorar su precisión en la generación de textos.

This editorial explores the possible risks posed by the use of artificial intelligence for the preparation of academic and scientific work. Additionally, a list of risks for scientific research is presented by the ChatGPT platform with the aim of exploring its accuracy in generating texts.

La Inteligencia artificial generativa en la comunicación científica: retos y oportunidades / Generative Artificial Intelligence in Scientific Communication: Challenges and Opportunities

Se ofrece la visión panorámica sobre la inteligencia artificial generativa en el contexto de la comunicación científica desde dos puntos de vista: el de los investigadores y el de los editores de las revistas académicas. También se describen alguno de los retos y oportunidades más significativos de la aplicación de la IA en la comunicación académica y se concluye con una serie de recomendaciones de buenas prácticas

An overview of generative artificial intelligence in the context of scientific communication is offered from two points of view: that of researchers and that of the editors of academic journals. Some of the most significant challenges and opportunities of the application of AI in academic communication are also described and it concludes with a series of good practice recommendations

[Moving towards a personalized oncology: The contribution of genomic techniques and artificial intelligence in the use of circulating tumor biomarkers]. / En marche vers une oncologie personnalisée : l'apport des techniques génomiques et de l'intelligence artificielle dans l'usage des biomarqueurs tumoraux circulants.

Technological advances, in particular the development of high-throughput sequencing, have led to the emergence of a new generation of molecular biomarkers for tumors. These new tools have profoundly changed therapeutic management in oncology, with increasingly precise molecular characterization of tumors leading to increasingly personalized therapeutic targeting. Detection of circulating tumor cells and/or circulating tumor DNA in blood samples -so-called 'liquid biopsies'- can now provide a genetic snapshot of the patient's tumor through an alternative and less invasive procedure than biopsy of the tumor tissue itself. This procedure for characterizing and monitoring the disease in real time facilitates the search for possible relapses, the emergence of resistance, or emergence of a new therapeutic target. In the long term, it might also provide a means of early detection of cancer. These new approaches require the treatment of ever-increasing amounts of clinical data, notably, with the goal of calculating composite clinical-biological predictive scores. The use of artificial intelligence will be unavoidable in this domain, but it raises ethical questions and implications for the health-care system that will have to be addressed.

Legal and ethical considerations of artificial intelligence in skin cancer diagnosis.

Artificial intelligence (AI) technology is becoming increasingly accurate and prevalent for the diagnosis of skin cancers. Commercially available AI diagnostic software is entering markets across the world posing new legal and ethical challenges for both clinicians and software companies. Australia has the highest rates of skin cancer in the world and is poised to be a significant benefactor and pioneer of the technology. This review describes the legal and ethical considerations raised by the emergence of artificial intelligence in skin cancer diagnosis and proposes recommendations for best practice.

Editorial: Artificial Intelligence (AI) in Clinical Medicine and the 2020 CONSORT-AI Study Guidelines.

Artificial intelligence (AI) in clinical medicine includes physical robotics and devices and virtual AI and machine learning. Concerns have been raised regarding ethical issues for the use of AI in surgery, including guidance for surgical decisions, patient confidentiality, and the need for support from controlled clinical trials to use these methods so that clinical guidelines can be developed. The most common applications for virtual AI include disease diagnosis, health monitoring and digital patient consultations, clinical training, patient data management, drug development, and personalized medicine. In September 2020, the CONSORT-A1 extension was developed with 14 additional items that should be reported for AI studies that include clear descriptions of the AI intervention, skills required, study setting, inputs and outputs of the AI intervention, analysis of errors, and the human and AI interactions. This Editorial aims to present current applications and challenges of AI in clinical medicine and the importance of the new 2020 CONSORT-AI study guidelines.

Artificial intelligence in medicine: A matter of joy or concern?

Artificial Intelligence (AI), a concept which dates back to the 1950s, is increasingly being developed by many medical specialties, especially those based on imaging or surgery. While the cognitive component of AI is far superior to that of human intelligence, it lacks consciousness, feelings, intuition and adaptation to unexpected situations. Furthermore, fundamental questions arise with regard to data security, the impact on healthcare professions, and the distribution of roles between physicians and AI especially concerning consent to medical care and liability in the event of a therapeutic accident.

La inteligencia artificial en la salud pública / Artificial intelligence in public health

The use of artificial intelligence in public health is growing and getting more presence. This knowledge capsule has the objective of increasing aware on this discipline necessary guiding principles, its components and sub-fields and the uses it has. Although this tool presents a linkage between the artificial intelligence and the eight principles for the digital transformation of public health and propose the main considerations to be taken for its implementation

The use of artificial intelligence in public health is growing and getting more presence. This knowledge capsule has the objective of increasing aware on this discipline necessary guiding principles, its components and sub-fields and the uses it has. Although this tool presents a linkage between the artificial intelligence and the eight principles for the digital transformation of public health and propose the main considerations to be taken for its implementation

Consideraciones bioéticas en relación con el uso de la inteligencia artificial en mastología / Bioethical considerations in the use of artificial intelligence in mastology / Considerações bioéticas em relação ao uso de inteligência artificial em mastologia

Resumen: El screening mamográfico ha ayudado a identificar el cáncer de mama en sus estadíos más tempranos, cuando los tratamientos son más efectivos. El empleo de la inteligencia artificial (IA) en el análisis de los mamogramas ha demostrado ser capaz de superar la habilidad del ojo humano para detectar lesiones en la mama sospechosas de cáncer. El objetivo del presente trabajo es realizar un aporte reflexivo sobre el avance de la tecnología digital y en particular de la IA en los screening mamográficos, desde el punto de vista técnico y bioético. Se analizan ventajas y limitaciones de la IA, explicando cómo se produce el aprendizaje de los sistemas computacionales. Se propone un debate bioético sobre cuestiones tales como la privacidad, la credibilidad, la responsabilidad y la educación permanente. Se resalta la importancia de establecer canales de diálogo entre todas las partes involucradas en la incorporación de las nuevas tecnologías en medicina.

Abstract: Mammographic screening has helped to identify breast cancer in its earliest stages, when treatment is most effective. The use of Artificial Intelligence in the analysis of mammograms has proved to be able to excel the human eye in detecting lesions in the breast that may be suspicious for cancer. The objective of this study is to make a reflective contribution on the advancement of digital technology and in particular, Artificial Intelligence in mammographic screening, from the technical and bioethical points of view. Advantages and limitations of Artificial Intelligence are analyzed explaining how machine learning occurs. A bioethical debate is proposed on issues such as privacy, credibility, accountability and continuous education. The importance of establishing channels of dialogue between all stakeholders in the incorporation of new technologies in medicine is highlighted.

Resumo: O rastreamento mamográfico ajuda a identificar o câncer de mama em seus estágios iniciais, quando os tratamentos são mais eficazes. O uso da Inteligência Artificial (AI) na análise de mamografias tem se mostrado capaz de superar a capacidade do olho humano em detectar lesões na mama suspeitas de câncer. O objetivo deste trabalho é fazer uma contribuição reflexiva sobre o avanço da tecnologia digital e, em particular, a AI em mamografia, do ponto de vista técnico e bioético. As vantagens e limitações da AI são analisadas explicando como o aprendizado de sistemas computacionais é feito. Propõe-se um debate bioético sobre questões como privacidade, credibilidade, responsabilidade e educação ao longo da vida. Destaca-se a importância do estabelecimento de canais de diálogo entre todas as partes envolvidas na incorporação de novas tecnologias na medicina.

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.