Ethical Incorporation of Artificial Intelligence into Neurosurgery: A Generative Pretrained Transformer Chatbot-Based, Human-Modified Approach.

INTRODUCTION: Artificial intelligence (AI) has become increasingly used in neurosurgery. Generative pretrained transformers (GPTs) have been of particular interest. However, ethical concerns regarding the incorporation of AI into the field remain underexplored. We delineate key ethical considerations using a novel GPT-based, human-modified approach, synthesize the most common considerations, and present an ethical framework for the involvement of AI in neurosurgery. METHODS: GPT-4, ChatGPT, Bing Chat/Copilot, You, Perplexity.ai, and Google Bard were queried with the prompt "How can artificial intelligence be ethically incorporated into neurosurgery?". Then, a layered GPT-based thematic analysis was performed. The authors synthesized the results into considerations for the ethical incorporation of AI into neurosurgery. Separate Pareto analyses with 20% threshold and 10% threshold were conducted to determine salient themes. The authors refined these salient themes. RESULTS: Twelve key ethical considerations focusing on stakeholders, clinical implementation, and governance were identified. Refinement of the Pareto analysis of the top 20% most salient themes in the aggregated GPT outputs yielded 10 key considerations. Additionally, from the top 10% most salient themes, 5 considerations were retrieved. An ethical framework for the use of AI in neurosurgery was developed. CONCLUSIONS: It is critical to address the ethical considerations associated with the use of AI in neurosurgery. The framework described in this manuscript may facilitate the integration of AI into neurosurgery, benefitting both patients and neurosurgeons alike. We urge neurosurgeons to use AI only for validated purposes and caution against automatic adoption of its outputs without neurosurgeon interpretation.

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.

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 .

[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.

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 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.

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.

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.

Ética de las nuevas inteligencias: Memorias de las ponencias 2do Congreso de Ética en Investigación / Ethics of new intelligences: Reports of the presentations 2nd Congress of Research Ethics

Memoria de las ponencias del 2do Congreso de Ética en Investigación, realizado en la Ciudad de Buenos Aires en septiembre de 2023, y organizado por el Comité Central de Ética en Investigación del Ministerio de Salud de esta ciudad. Se presentan las ponencias del congreso, organizadas en los capítulos: Inteligencia Artificial y Datos en la investigación; Conducta responsable en investigación e integridad científica; y Mecanismos alternativos para la instrumentación del consentimiento informado en investigaciones en salud.

¿Cómo impacta la inteligencia artificial en la Investigación clínica?: Beneficios, riesgos y desafíos éticos / How does artificial intelligence impact clinical research?: Benefits, Risks and Ethical Challenges

Introducción: La explosión tecnológica de la última década, como la inteligencia artificial (IA) y big data, afectaron positivamente la investigación clínica. Sin embargo, surgen preocupaciones éticas que no se pueden ignorar. Objetivo: Identificar beneficios y riesgos de la IA, destacando desafíos éticos relevantes, para su uso responsable en investigación clínica. Método: Estudio cualitativo y descriptivo, con un enfoque de revisión narrativa de la literatura sobre el tema de interés. Resultado: Beneficios de IA: acelera procesos, optimiza ensayos clínicos, analiza grandes volúmenes de datos, etc. Riegos y desafíos éticos de IA: sesgo algorítmico, heterogeneidad y baja calidad de datos, inseguridad y violación de privacidad, falta de transparencia e interpretabilidad de modelos, etc. Conclusiones: Es esencial contar con políticas para abordar los riesgos y desafíos éticos, garantizando que la IA se utilice de manera equitativa y segura. Estas tecnologías deben actuar como herramientas de apoyo y nunca reemplazar el juicio humano. (AU)

Introducción al Capítulo I: La ética de las nuevas inteligencias: Desafíos y principios en la era de la Inteligencia Artificial en salud / Introduction to chapter I: Ethics of the new intelligences: Challenges and principles in the era of AI in health

La era de las nuevas inteligencias ha emergido con una promesa seductora pero también con un peso ético abrumador. La Inteligencia Artificial (IA), con su capacidad para procesar datos a una escala sin precedentes y tomar decisiones autónomas, ha trascendido los límites de la imaginación humana, pero también ha abierto un abismo de incertidumbre moral que debemos abordar con urgencia y creatividad. Lo que deberíamos llamar la "ética de las nuevas inteligencias" trasciende las convenciones tradicionales de la ética aplicada. No se trata simplemente de apelar a principios preexistentes a nuevas tecnologías, sino de forjar un nuevo marco ético que sea ágil, adaptable y relevante en un mundo impulsado por la innovación tecnológica. Es un llamado a la reflexión profunda y la acción concertada, donde debemos explorar las implicancias de la IA en todas sus dimensiones, desde la toma de decisiones algorítmicas hasta la responsabilidad social de las empresas tecnológicas. En el centro de esta idea aparece la necesidad de establecer un diálogo interdisciplinario que abarque no sólo a científicos y tecnólogos, sino también a filósofos, éticos, sociólogos, y a la sociedad en su conjunto. (AU)

Implementación de la IA en la investigación: el desafío de revalorizar la inteligencia humana / Implementing AI in research: the challenge of revaluing human intelligence

La inteligencia no puede reducirse a una serie de algoritmos, ni un investigador puede equivaler a una máquina que procesa datos. En una investigación se toman decisiones con inteligencia y libre voluntad -siendo ésta la fuente de eticidad-, se utiliza la prudencia, y se introducen valores como la justicia y la equidad. Investigar es una tarea propiamente humana, antecedida por un compromiso con la búsqueda de la verdad y el bien. Las inteligencias artificiales se caracterizan por falta de veracidad, creatividad, y neutralidad, siendo su sesgo muy elevado. Revalorizar la inteligencia humana es valorar al hombre, en cuanto ser racional. (AU)

Tecnologías de inteligencia artificial en investigación en salud con seres humanos: un reto a la responsabilidad en la toma de decisiones / Artificial intelligence technologies in health research with human beings: a challenge to responsibility in decision making

Existe una responsabilidad ética en el desarrollo del conocimiento científico. La investigación biomédica representa un beneficio para la humanidad resultando ser el pilar sobre el que se asientan los avances diagnósticos y terapéuticos. Además del respeto a los estándares exigidos, existe la trascendente responsabilidad de formarse. Se considera a la educación y al control adecuado como los garantes de una investigación respetuosa de las buenas prácticas. Planteamos la enseñanza de la integridad científica desde una etapa temprana en las carreras de grado para incorporar la importancia de la credibilidad, siendo este el acervo más valioso que tiene el investigador y la institución en la cual estos desarrollan su labor. Una educación sólida en bioética es la mejor manera de adoptar una conducta íntegra, y por ende responsable, al realizar proyectos de investigación. La formación ética debe ser una construcción constante resultando necesario plantear estrategias multidimensionales para evitar prácticas inadecuadas.