Vardit Ravitsky’s research while affiliated with The Hastings Center and other places

Objectives Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation. Materials and Methods We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle. Results, Discussion, and Conclusion Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.

Introduction The 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools. Methods Each workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience. Results Key outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes. Discussion The symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.

We hypothesized that ethical criteria that guide the use of preimplantation genetic testing (PGT) could be used to inform policies about expanded use of non-invasive prenatal screening (NIPS). We used a systematic review of reasons approach to assess ethical criteria used to justify using (or not using) PGT for genetic conditions. Out of 1135 identified documents, we retained and analyzed 216 relevant documents. Results show a clear distinction in acceptability of PGT for medical vs. non-medical conditions. Criteria to decide on use of PGT for medical conditions are largely based on their severity, but there is no clear definition of “severity”. Instead, characteristics of the condition that relate to severity are used as sub-criteria to assess severity. We found that characteristics that are used as sub-criteria for assessing severity include monogenic etiology, high penetrance, absence of treatment, early age of onset, shortened lifespan, and reduced quality of life. Consensus about the use of PGT is highest for conditions that meet most of these criteria. There is no consensus around the acceptability of using PGT to detect non-medical conditions. We propose that the same severity criteria could be used by policymakers to assess the acceptability of using other genetic tests in screening and practice, including for the use of NIPS for additional conditions as indications broaden.

Research question This scoping review investigates the Ethical, Legal, and Social Implications (ELSI) of gamete donation, a critical facet of Assisted Reproductive Technologies, by analyzing the evolving research scope, methodological approaches, and the geographical skew in the literature. Despite the increased global uptake of donor gametes, current scholarship predominantly emanates from Western contexts and focuses on majoritized groups. This bias constrains the universality of research findings and limits their applicability across varied legal, cultural, and social contexts, underscoring a need for broader inclusivity. Design We addressed 867 pivotal articles published between 1999 and 2019. Results Our analysis reveals a discernible escalation in research volume, with 62% based on empirical research. The intellectual landscape unfolds into four dominant clusters: Regulatory Frameworks, Incentives, and Access; Family Dynamics and Genetic Linkages; Identity and Privacy in Donor Conception; and Cultural and Societal Attitudes towards GD. Each cluster highlights nuanced dimensions of gamete donation, from regulatory intricacies and psychological welfare to identity ethics and cultural perceptions. Conclusion Our findings advocate for a shift towards more globally representative and methodologically inclusive research. By integrating diverse cultural narratives and expanding geographical breadth, future research can offer holistic understandings of gamete donation, fostering equitable and culturally resonant practices and policies worldwide.