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.

Gordon ER ，Trager MH ，Kontos D ，Weng C ，Geskin LJ ，Dugdale LS ，Samie FH ... -  《-》

Ethical implications of artificial intelligence in skin cancer diagnostics: use-case analyses.

Skin cancer is the most common cancer worldwide. Early diagnosis is crucial to improving patient survival and morbidity. Artificial intelligence (AI)-assisted smartphone applications (apps) for skin cancer potentially offer accessible, early risk assessment of suspicious skin lesions. However, the integration of novel technologies into dermatology pathways raises ethical concerns. Although ethical principles for AI governance are well known, how these principles should be applied to real-life AI apps readily available for public use is less well understood. To conduct an ethical use-case analysis of commercially available skin cancer apps, to better understand the ethical issues arising from their development and use in a real-world context. Established methods for the ethical analysis of clinical AI applications were applied to two popular skin cancer apps in the UK: SkinVision and Scanoma. Systematic searches of published literature, regulatory documents and websites were conducted to review the evidence regarding app development, effectiveness and use. Screening for inclusion was undertaken by two researchers independently. Ethical concerns were identified with reference to previously described ethical concerns and principles for AI-assisted healthcare. By conceptualizing ethical principles within the use-context of skin cancer apps, we identified specific ethical issues arising throughout the AI lifecycle of both apps. One company provided extensive detail regarding algorithm development and decision-making; this information was insufficiently reported for the other app. Other concerns identified were related to number, quality and consistency of studies assessing algorithm efficacy. Limited efforts to address potential skin tone biases and the exclusion of individuals with darker skin tones as target users by one app risks perpetuating existing inequalities. Inadequate regulatory oversight was identified. Findings from our ethical use-case analysis of two patient-facing AI-assisted skin cancer apps suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation should increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists and the public is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.

Shah SFH ，Arecco D ，Draper H ，Tiribelli S ，Harriss E ，Matin RN ... -  《-》

Ethics of Procuring and Using Organs or Tissue from Infants and Newborns for Transplantation, Research, or Commercial Purposes: Protocol for a Bioethics Scoping Review.

Since the inception of transplantation, it has been crucial to ensure that organ or tissue donations are made with valid informed consent to avoid concerns about coercion or exploitation. This issue is particularly challenging when it comes to infants and younger children, insofar as they are unable to provide consent. Despite their vulnerability, infants' organs and tissues are considered valuable for biomedical purposes due to their size and unique properties. This raises questions about the conditions under which it is permissible to remove and use these body parts for transplantation, research, or commercial purposes. The aim of this protocol is to establish a foundation for a scoping review that will identify, clarify, and categorise the main ethical arguments regarding the permissibility of removing and using organs or tissues from infants. The scoping review will follow the methodology outlined by the Joanna Briggs Institute (JBI), consisting of five stages: (1) identifying the research question, (2) developing the search strategy, (3) setting inclusion criteria, (4) extracting data, and (5) presenting and analysing the results. We will include both published and unpublished materials that explicitly discuss the ethical arguments related to the procurement and use of infant organs or tissues in the biomedical context. The search will cover various databases, including the National Library of Medicine, Web of Science, EBSCO, and others, as well as grey literature sources. Two raters will independently assess the eligibility of articles, and data from eligible studies will be extracted using a standardised form. The extracted data will then be analysed descriptively through qualitative content analysis. There has been debate about how to respect the rights and interests of organ and tissue donors since the beginning of transplantation practice, given the moral risks involved in procuring parts of their bodies and using them for transplantation or research. A major concern has been to ensure that, at a minimum, donation of organs or other bodily tissues for transplantation or research is done under conditions of valid informed consent, so as to avoid coercion or exploitation among other moral harms. In the case of infants and younger children, however, this concern poses special difficulties insofar as infants and younger children are deemed incapable of providing valid consent. Due to their diminutive size and other distinctive properties, infants' organs and tissues are seen as valuable for biomedical purposes. Yet, the heightened vulnerability of infants raises questions about when and whether it is ever permissible to remove these body parts or use them in research or for other purposes. The aim of this protocol is to form the basis of a systematic scoping review to identify, clarify, and systematise the main ethical arguments for and against the permissibility of removing and using infant or newborn (hereafter, "infant") organs or tissues in the biomedical context (i.e. for transplantation, research, or commercial purposes). Our scoping review will broadly follow the well-established methodology outlined by the Joanna Briggs Institute ( Peters et al., 2020). We will follow a five-stage review process: (1) identification of the research question, (2) development of the search strategy, (3) inclusion criteria, (4) data extraction, and (5) presentation and analysis of the results. Published and unpublished bibliographic material (including reports, dissertations, book chapters, etc.) will be considered based on the following inclusion criteria: the presence of explicit (bio)ethical arguments or reasons (concept) for or against the procurement and use of organs or tissues from infants, defined as a child from birth until 1 year old (population), in the biomedical domain, including transplantation, research, and commercial development (context). We will search for relevant studies in the National Library of Medicine (including PubMed and MEDLINE), Virtual Health Library, Web of Science, Google Scholar, EBSCO, Google Scholar, PhilPapers, The Bioethics Literature Database (BELIT), EthxWeb as well as grey literature sources (e.g., Google, BASE, OpenGrey, and WorldCat) and the reference lists of key studies to identify studies suitable for inclusion. A three-stage search strategy will be used to determine the eligibility of articles, as recommended by the JBI methodological guidelines. We will exclude sources if (a) the full text is not accessible, (b) the main text is in a language other than English, or (c) the focus is exclusively on scientific, legal, or religious/theological arguments. All articles will be independently assessed for eligibility between two raters (MB & XL); data from eligible articles will be extracted and charted using a standardised data extraction form. The extracted data will be analysed descriptively using basic qualitative content analysis. Ethical review is not required as scoping reviews are a form of secondary data analysis that synthesise data from publicly available sources. Our dissemination strategy includes peer review publication, presentation at conferences, and outreach to relevant stakeholders. The results will be reported according to the PRISMA-ScR guidelines. An overview of the general data from the included studies will be presented in the form of graphs or tables showing the distribution of studies by year or period of publication, country of origin, and key ethical arguments. These results will be accompanied by a narrative summary describing how each included study or article relates to the aims of this review. Research gaps will be identified and limitations of the review will also be highlighted. A paper summarising the findings from this review will be published in a peer-reviewed journal. In addition, a synthesis of the key findings will be disseminated to biomedical settings (e.g., conferences or workshops, potentially including ones linked to university hospitals) in the UK, USA, Türkiye, and Singapore. They will also be shared with the academic community and policy makers involved in the organ procurement organisations (OPO), which will potentially consider our recommendations in their decision-making processes regarding infant tissue/organ donation practice in these countries. The use of a rigorous, well-established methodological framework will ensure the production of a high-quality scoping review that will contribute to the bioethics literature.A comprehensive search of disciplinary and cross-disciplinary databases will be undertaken to ensure coverage of all possible sources that meet the inclusion criteria for the review.This review will focus exclusively on infant tissue/organ procurement/use in biomedical contexts, providing a comprehensive and reliable source of ethical arguments for future debates on this sensitive topic.The review will be limited to articles published in English, which increases the risk of missing relevant sources published in other languages.The review will be limited to articles for which the full text is available, which increases the risk of missing relevant sources that otherwise may have been included in the scoping review had the full text been accessible.

Barış M ，Lim X ，T Almonte M ，Shaw D ，Brierley J ，Porsdam Mann S ，Nguyen T ，Menikoff J ，Wilkinson D ，Savulescu J ，Earp BD ... -  《-》

Data stewardship and curation practices in AI-based genomics and automated microscopy image analysis for high-throughput screening studies: promoting robust and ethical AI applications.

Researchers have increasingly adopted AI and next-generation sequencing (NGS), revolutionizing genomics and high-throughput screening (HTS), and transforming our understanding of cellular processes and disease mechanisms. However, these advancements generate vast datasets requiring effective data stewardship and curation practices to maintain data integrity, privacy, and accessibility. This review consolidates existing knowledge on key aspects, including data governance, quality management, privacy measures, ownership, access control, accountability, traceability, curation frameworks, and storage systems. We conducted a systematic literature search up to January 10, 2024, across PubMed, MEDLINE, EMBASE, Scopus, and additional scholarly platforms to examine recent advances and challenges in managing the vast and complex datasets generated by these technologies. Our search strategy employed structured keyword queries focused on four key thematic areas: data governance and management, curation frameworks, algorithmic bias and fairness, and data storage, all within the context of AI applications in genomics and microscopy. Using a realist synthesis methodology, we integrated insights from diverse frameworks to explore the multifaceted challenges associated with data stewardship in these domains. Three independent reviewers, who systematically categorized the information across critical themes, including data governance, quality management, security, privacy, ownership, and access control conducted data extraction and analysis. The study also examined specific AI considerations, such as algorithmic bias, model explainability, and the application of advanced cryptographic techniques. The review process included six stages, starting with an extensive search across multiple research databases, resulting in 273 documents. Screening based on broad criteria, titles, abstracts, and full texts followed this, narrowing the pool to 38 highly relevant citations. Our findings indicated that significant research was conducted in 2023 by highlighting the increasing recognition of robust data governance frameworks in AI-driven genomics and microscopy. While 36 articles extensively discussed data interoperability and sharing, AI-model explain ability and data augmentation remained underexplored, indicating significant gaps. The integration of diverse data types-ranging from sequencing and clinical data to proteomic and imaging data-highlighted the complexity and expansive scope of AI applications in these fields. The current challenges identified in AI-based data stewardship and curation practices are lack of infrastructure and cost optimization, ethical and privacy considerations, access control and sharing mechanisms, large scale data handling and analysis and transparent data-sharing policies and practice. Proposed solutions to address issues related to data quality, privacy, and bias management include advanced cryptographic techniques, federated learning, and blockchain technology. Robust data governance measures, such as GA4GH standards, DUO versioning, and attribute-based access control, are essential for ensuring data integrity, security, and ethical use. The study also emphasized the critical role of Data Management Plans (DMPs), meticulous metadata curation, and advanced cryptographic techniques in mitigating risks related to data security and identifiability. Despite advancements, significant challenges persisted in balancing data ownership with research accessibility, integrating heterogeneous data sources, ensuring platform interoperability, and maintaining data quality. Ongoing risks of unauthorized access and data breaches underscored the need for continuous innovation in data management practices and stricter adherence to legal and ethical standards. These findings explored the current practices and challenges in data stewardship, offering a roadmap for strengthening the governance, security, and ethical use of AI in genomics and microscopy. While robust governance frameworks and ethical practices have established a foundation for data integrity and transparency, there remains an urgent need for collaborative efforts to develop interoperable platforms and transparent data-sharing policies. Additionally, evolving legal and ethical frameworks will be crucial to addressing emerging challenges posed by AI technologies. Fostering transparency, accountability, and ethical responsibility within the research community will be key to ensuring trust and driving ethically sound scientific advancements.

Taddese AA ，Addis AC ，Tam BT 《-》

Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review.

Recent healthcare advancements highlight the potential of Artificial Intelligence (AI) - and especially, among its subfields, Machine Learning (ML) - in enhancing Breast Cancer (BC) clinical care, leading to improved patient outcomes and increased radiologists' efficiency. While medical imaging techniques have significantly contributed to BC detection and diagnosis, their synergy with AI algorithms has consistently demonstrated superior diagnostic accuracy, reduced False Positives (FPs), and enabled personalized treatment strategies. Despite the burgeoning enthusiasm for leveraging AI for early and effective BC clinical care, its widespread integration into clinical practice is yet to be realized, and the evaluation of AI-based health technologies in terms of health and economic outcomes remains an ongoing endeavor. This scoping review aims to investigate AI (and especially ML) applications that have been implemented and evaluated across diverse clinical tasks or decisions in breast imaging and to explore the current state of evidence concerning the assessment of AI-based technologies for BC clinical care within the context of Health Technology Assessment (HTA). We conducted a systematic literature search following the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P) checklist in PubMed and Scopus to identify relevant studies on AI (and particularly ML) applications in BC detection and diagnosis. We limited our search to studies published from January 2015 to October 2023. The Minimum Information about CLinical Artificial Intelligence Modeling (MI-CLAIM) checklist was used to assess the quality of AI algorithms development, evaluation, and reporting quality in the reviewed articles. The HTA Core Model® was also used to analyze the comprehensiveness, robustness, and reliability of the reported results and evidence in AI-systems' evaluations to ensure rigorous assessment of AI systems' utility and cost-effectiveness in clinical practice. Of the 1652 initially identified articles, 104 were deemed eligible for inclusion in the review. Most studies examined the clinical effectiveness of AI-based systems (78.84%, n= 82), with one study focusing on safety in clinical settings, and 13.46% (n=14) focusing on patients' benefits. Of the studies, 31.73% (n=33) were ethically approved to be carried out in clinical practice, whereas 25% (n=26) evaluated AI systems legally approved for clinical use. Notably, none of the studies addressed the organizational implications of AI systems in clinical practice. Of the 104 studies, only two of them focused on cost-effectiveness analysis, and were analyzed separately. The average percentage scores for the first 102 AI-based studies' quality assessment based on the MI-CLAIM checklist criteria were 84.12%, 83.92%, 83.98%, 74.51%, and 14.7% for study design, data and optimization, model performance, model examination, and reproducibility, respectively. Notably, 20.59% (n=21) of these studies relied on large-scale representative real-world breast screening datasets, with only 10.78% (n =11) studies demonstrating the robustness and generalizability of the evaluated AI systems. In bridging the gap between cutting-edge developments and seamless integration of AI systems into clinical workflows, persistent challenges encompass data quality and availability, ethical and legal considerations, robustness and trustworthiness, scalability, and alignment with existing radiologists' workflow. These hurdles impede the synthesis of comprehensive, robust, and reliable evidence to substantiate these systems' clinical utility, relevance, and cost-effectiveness in real-world clinical workflows. Consequently, evaluating AI-based health technologies through established HTA methodologies becomes complicated. We also highlight potential significant influences on AI systems' effectiveness of various factors, such as operational dynamics, organizational structure, the application context of AI systems, and practices in breast screening or examination reading of AI support tools in radiology. Furthermore, we emphasize substantial reciprocal influences on decision-making processes between AI systems and radiologists. Thus, we advocate for an adapted assessment framework specifically designed to address these potential influences on AI systems' effectiveness, mainly addressing system-level transformative implications for AI systems rather than focusing solely on technical performance and task-level evaluations.

Uwimana A ，Gnecco G ，Riccaboni M 《-》