BACKGROUND: Advancements in cancer treatment and survivorship rely on participation in research and access to health records.
METHODS: This study explored preferences for data access and sharing in 14 workshops with 42 community members, most of whom were a cancer survivor or carer. Various scenarios for data access and sharing were presented and discussed, with participants\' preferences summarized using descriptive statistics. Reasons underlying these preferences were identified through a thematic analysis of workshop transcripts.
RESULTS: Most participants indicated a willingness for researchers to use their self-report data and current health records for a specific research project (86%). Many were also willing for their self-report data and current (62%) or all future (44%) health records to be shared with other researchers for use in other studies if made aware of this. Willingness to consent to data access and sharing data in cancer research was influenced by: (i) the potential for data sharing to advance medical discoveries and benefit people impacted by cancer in the future, (ii) transparency around researchers\' credibility and their intentions for data sharing, (iii) level of ownership and control over data sharing, and (iv) protocols for privacy and confidentiality in data sharing.
CONCLUSIONS: Based on these themes, we present practical strategies for optimizing data access and sharing in cancer research.

Standard endovascular aortic repair (EVAR) has become the standard of care for treating infrarenal abdominal aortic aneurysms (AAAs) in patients with favorable anatomies, while patients with challenging AAA anatomies, and those with suprarenal or thoraco-abdominal aneurysms, still need alternative, more complex, solutions, including custom-made branched or fenestrated grafts, which are constrained by production delay and costs. To address urgent needs and complex cases, physicians have proposed modifying standard endografts by manually creating graft fenestrations. This allows for effective aneurysm exclusion and satisfactory patency of visceral vessels. Although physician-modified grafts (PMEGs) have demonstrated high technical success, standardized creation processes and long-term safety data are still lacking, necessitating further study to validate their clinical and legal standing. The aim of this article is to illustrate the state of the art with regard to this surgical technique, summarizing its origin, evolution, and the main clinical evidence supporting its effectiveness. The paper also aims to discuss the main medico-legal issues related to the use of PMEGs, with particular reference to the issue of safety related to the standardization of the surgical technique, medical liability profiles, and informed consent.

UNASSIGNED: Voice as a health biomarker using artificial intelligence (AI) is gaining momentum in research. The noninvasiveness of voice data collection through accessible technology (such as smartphones, telehealth, and ambient recordings) or within clinical contexts means voice AI may help address health disparities and promote the inclusion of marginalized communities. However, the development of AI-ready voice datasets free from bias and discrimination is a complex task. The objective of this study is to better understand the perspectives of engaged and interested stakeholders regarding ethical and trustworthy voice AI, to inform both further ethical inquiry and technology innovation.
UNASSIGNED: A questionnaire was administered to voice AI experts, clinicians, scholars, patients, trainees, and policy-makers who participated at the 2023 Voice AI Symposium organized by the Bridge2AI-Voice AI Consortium. The survey used a mix of Likert scale, ranking and open-ended questions. A total of 27 stakeholders participated in the study.
UNASSIGNED: The main results of the study are the identification of priorities in terms of ethical issues, an initial definition of ethically sourced data for voice AI, insights into the use of synthetic voice data, and proposals for acting on the trustworthiness of voice AI. The study shows a diversity of perspectives and adds nuance to the planning and development of ethical and trustworthy voice AI.
UNASSIGNED: This study represents the first stakeholder survey related to voice as a biomarker of health published to date. This study sheds light on the critical importance of ethics and trustworthiness in the development of voice AI technologies for health applications.

BACKGROUND: Innovative tools leveraging artificial intelligence (AI) and machine learning (ML) are rapidly being developed for medicine, with new applications emerging in prediction, diagnosis, and treatment across a range of illnesses, patient populations, and clinical procedures. One barrier for successful innovation is the scarcity of research in the current literature seeking and analyzing the views of AI or ML researchers and physicians to support ethical guidance.
OBJECTIVE: This study aims to describe, using a qualitative approach, the landscape of ethical issues that AI or ML researchers and physicians with professional exposure to AI or ML tools observe or anticipate in the development and use of AI and ML in medicine.
METHODS: Semistructured interviews were used to facilitate in-depth, open-ended discussion, and a purposeful sampling technique was used to identify and recruit participants. We conducted 21 semistructured interviews with a purposeful sample of AI and ML researchers (n=10) and physicians (n=11). We asked interviewees about their views regarding ethical considerations related to the adoption of AI and ML in medicine. Interviews were transcribed and deidentified by members of our research team. Data analysis was guided by the principles of qualitative content analysis. This approach, in which transcribed data is broken down into descriptive units that are named and sorted based on their content, allows for the inductive emergence of codes directly from the data set.
RESULTS: Notably, both researchers and physicians articulated concerns regarding how AI and ML innovations are shaped in their early development (ie, the problem formulation stage). Considerations encompassed the assessment of research priorities and motivations, clarity and centeredness of clinical needs, professional and demographic diversity of research teams, and interdisciplinary knowledge generation and collaboration. Phase-1 ethical issues identified by interviewees were notably interdisciplinary in nature and invited questions regarding how to align priorities and values across disciplines and ensure clinical value throughout the development and implementation of medical AI and ML. Relatedly, interviewees suggested interdisciplinary solutions to these issues, for example, more resources to support knowledge generation and collaboration between developers and physicians, engagement with a broader range of stakeholders, and efforts to increase diversity in research broadly and within individual teams.
CONCLUSIONS: These qualitative findings help elucidate several ethical challenges anticipated or encountered in AI and ML for health care. Our study is unique in that its use of open-ended questions allowed interviewees to explore their sentiments and perspectives without overreliance on implicit assumptions about what AI and ML currently are or are not. This analysis, however, does not include the perspectives of other relevant stakeholder groups, such as patients, ethicists, industry researchers or representatives, or other health care professionals beyond physicians. Additional qualitative and quantitative research is needed to reproduce and build on these findings.

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The integration of large language models (LLMs) like ChatGPT into medical education presents potential benefits and challenges. These technologies, aligned with constructivist learning theories, could potentially enhance critical thinking and problem-solving through inquiry-based learning environments. However, the actual impact on educational outcomes and the effectiveness of these tools in fostering learning require further empirical study. This technological shift necessitates a reevaluation of curriculum design and the development of new assessment methodologies to measure its effects accurately. Additionally, the use of LLMs introduces significant ethical concerns, particularly in addressing inherent AI biases to ensure equitable educational access. LLMs may also help reduce global disparities in medical education by providing broader access to contemporary medical knowledge and practices, though their deployment must be managed carefully to truly support the training of competent, ethical medical professionals.

BACKGROUND: The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing.
OBJECTIVE: This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom.
METHODS: We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms \'polygenic embryo screening\', \'polygenic preimplantation\', and \'PGT-P\'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant.
RESULTS: The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for \'designer babies\', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling.
CONCLUSIONS: The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.

UNASSIGNED: Li-Fraumeni syndrome (LFS) is an inherited cancer predisposition syndrome with an estimated prevalence of 1 in 3,000-5,000 individuals. LFS poses a significant cancer risk throughout the lifespan, with notable cancer susceptibility in childhood. Despite being predominantly inherited, up to 20% of cases arise de novo. Surveillance protocols facilitate the reduction of mortality and morbidity through early cancer detection. While newborn screening (NBS) has proven effective in identifying newborns with rare genetic conditions, even those occurring as rarely as 1 in 185,000, its potential for detecting inherited cancer predispositions remains largely unexplored.
UNASSIGNED: This survey-based study investigates perspectives toward NBS for LFS among individuals with and parents of children with LFS receiving care at single comprehensive cancer center in the U.S.
UNASSIGNED: All participants unanimously supported NBS for LFS (n = 24). Reasons included empowerment (83.3%), control (66.7%), and peace of mind (54.2%), albeit with concerns about anxiety (62.5%) and devastation (50%) related to receiving positive results. Participants endorsed NBS as beneficial for cancer detection and prevention (91.7%), research efforts (87.5%), and family planning (79.2%) but voiced apprehensions about the financial cost of cancer surveillance (62.5%), emotional burdens (62.5%), and insurance coverage and discrimination (54.2%). Approximately 83% of respondents believed that parental consent should be required to screen newborns for LFS.
UNASSIGNED: This study revealed strong support for NBS for LFS despite the recognition of various perceived benefits and risks. These findings underscore the complex interplay between clinical, psychosocial, and ethical factors in considering NBS for LFS from the perspective of the LFS community.

BACKGROUND: Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges.
METHODS: A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests.
RESULTS: Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI.
CONCLUSIONS: The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.

This editorial delves into the integration of artificial intelligence (AI) into nursing documentation, emphasizing its potential to streamline workflows, reduce human error, and enhance patient care. AI technologies, notably natural language processing and decision support systems, present opportunities to automate tedious documentation tasks and enhance record accuracy. However, their adoption raises ethical considerations, such as privacy, bias, and accountability. Striking a balance between technological advancements and ethical imperatives is pivotal to harnessing the benefits of AI while safeguarding patient safety and upholding professional integrity in nursing practice. Advocating for ongoing evaluation, regulation, and education is crucial to ensure the responsible integration of AI into nursing documentation. This approach aims to improve patient outcomes and maintain the high standards of the nursing profession.