Alaa Talaat Youssef

All Publications

• Ethical Considerations in the Design and Conduct of Clinical Trials of Artificial Intelligence. JAMA network open Youssef, A., Nichol, A. A., Martinez-Martin, N., Larson, D. B., Abramoff, M., Wolf, R. M., Char, D. 2024; 7 (9): e2432482

  Abstract

  Importance: Safe integration of artificial intelligence (AI) into clinical settings often requires randomized clinical trials (RCT) to compare AI efficacy with conventional care. Diabetic retinopathy (DR) screening is at the forefront of clinical AI applications, marked by the first US Food and Drug Administration (FDA) De Novo authorization for an autonomous AI for such use.Objective: To determine the generalizability of the 7 ethical research principles for clinical trials endorsed by the National Institute of Health (NIH), and identify ethical concerns unique to clinical trials of AI.Design, Setting, and Participants: This qualitative study included semistructured interviews conducted with 11 investigators engaged in the design and implementation of clinical trials of AI for DR screening from November 11, 2022, to February 20, 2023. The study was a collaboration with the ACCESS (AI for Children's Diabetic Eye Exams) trial, the first clinical trial of autonomous AI in pediatrics. Participant recruitment initially utilized purposeful sampling, and later expanded with snowball sampling. Study methodology for analysis combined a deductive approach to explore investigators' perspectives of the 7 ethical principles for clinical research endorsed by the NIH and an inductive approach to uncover the broader ethical considerations implementing clinical trials of AI within care delivery.Results: A total of 11 participants (mean [SD] age, 47.5 [12.0] years; 7 male [64%], 4 female [36%]; 3 Asian [27%], 8 White [73%]) were included, with diverse expertise in ethics, ophthalmology, translational medicine, biostatistics, and AI development. Key themes revealed several ethical challenges unique to clinical trials of AI. These themes included difficulties in measuring social value, establishing scientific validity, ensuring fair participant selection, evaluating risk-benefit ratios across various patient subgroups, and addressing the complexities inherent in the data use terms of informed consent.Conclusions and Relevance: This qualitative study identified practical ethical challenges that investigators need to consider and negotiate when conducting AI clinical trials, exemplified by the DR screening use-case. These considerations call for further guidance on where to focus empirical and normative ethical efforts to best support conduct clinical trials of AI and minimize unintended harm to trial participants.

  View details for DOI 10.1001/jamanetworkopen.2024.32482

  View details for PubMedID 39240560

• Applied artificial intelligence for global child health: Addressing biases and barriers. PLOS digital health Muralidharan, V., Schamroth, J., Youssef, A., Celi, L. A., Daneshjou, R. 2024; 3 (8): e0000583

  Abstract

  Given the potential benefits of artificial intelligence and machine learning (AI/ML) within healthcare, it is critical to consider how these technologies can be deployed in pediatric research and practice. Currently, healthcare AI/ML has not yet adapted to the specific technical considerations related to pediatric data nor adequately addressed the specific vulnerabilities of children and young people (CYP) in relation to AI. While the greatest burden of disease in CYP is firmly concentrated in lower and middle-income countries (LMICs), existing applied pediatric AI/ML efforts are concentrated in a small number of high-income countries (HICs). In LMICs, use-cases remain primarily in the proof-of-concept stage. This narrative review identifies a number of intersecting challenges that pose barriers to effective AI/ML for CYP globally and explores the shifts needed to make progress across multiple domains. Child-specific technical considerations throughout the AI/ML lifecycle have been largely overlooked thus far, yet these can be critical to model effectiveness. Governance concerns are paramount, with suitable national and international frameworks and guidance required to enable the safe and responsible deployment of advanced technologies impacting the care of CYP and using their data. An ambitious vision for child health demands that the potential benefits of AI/Ml are realized universally through greater international collaboration, capacity building, strong oversight, and ultimately diffusing the AI/ML locus of power to empower researchers and clinicians globally. In order that AI/ML systems that do not exacerbate inequalities in pediatric care, teams researching and developing these technologies in LMICs must ensure that AI/ML research is inclusive of the needs and concerns of CYP and their caregivers. A broad, interdisciplinary, and human-centered approach to AI/ML is essential for developing tools for healthcare workers delivering care, such that the creation and deployment of ML is grounded in local systems, cultures, and clinical practice. Decisions to invest in developing and testing pediatric AI/ML in resource-constrained settings must always be part of a broader evaluation of the overall needs of a healthcare system, considering the critical building blocks underpinning effective, sustainable, and cost-efficient healthcare delivery for CYP.

  View details for DOI 10.1371/journal.pdig.0000583

  View details for PubMedID 39172772

• Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study. JAMA network open Ng, M. Y., Youssef, A., Miner, A. S., Sarellano, D., Long, J., Larson, D. B., Hernandez-Boussard, T., Langlotz, C. P. 2023; 6 (12): e2345892

  Abstract

  The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care.To discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts.This qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data.Data set experts' perceptions on what makes data sets AI ready.Participants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness.In this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.

  View details for DOI 10.1001/jamanetworkopen.2023.45892

  View details for PubMedID 38039004

• Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care. JAMA network open Youssef, A., Ng, M. Y., Long, J., Hernandez-Boussard, T., Shah, N., Miner, A., Larson, D., Langlotz, C. P. 2023; 6 (12): e2348422

  Abstract

  Limited sharing of data sets that accurately represent disease and patient diversity limits the generalizability of artificial intelligence (AI) algorithms in health care.To explore the factors associated with organizational motivation to share health data for AI development.This qualitative study investigated organizational readiness for sharing health data across the academic, governmental, nonprofit, and private sectors. Using a multiple case studies approach, 27 semistructured interviews were conducted with leaders in data-sharing roles from August 29, 2022, to January 9, 2023. The interviews were conducted in the English language using a video conferencing platform. Using a purposive and nonprobabilistic sampling strategy, 78 individuals across 52 unique organizations were identified. Of these, 35 participants were enrolled. Participant recruitment concluded after 27 interviews, as theoretical saturation was reached and no additional themes emerged.Concepts defining organizational readiness for data sharing and the association between data-sharing factors and organizational behavior were mapped through iterative qualitative analysis to establish a framework defining organizational readiness for sharing clinical data for AI development.Interviews included 27 leaders from 18 organizations (academia: 10, government: 7, nonprofit: 8, and private: 2). Organizational readiness for data sharing centered around 2 main constructs: motivation and capabilities. Motivation related to the alignment of an organization's values with data-sharing priorities and was associated with its engagement in data-sharing efforts. However, organizational motivation could be modulated by extrinsic incentives for financial or reputational gains. Organizational capabilities comprised infrastructure, people, expertise, and access to data. Cross-sector collaboration was a key strategy to mitigate barriers to access health data.This qualitative study identified sector-specific factors that may affect the data-sharing behaviors of health organizations. External incentives may bolster cross-sector collaborations by helping overcome barriers to accessing health data for AI development. The findings suggest that tailored incentives may boost organizational motivation and facilitate sustainable flow of health data for AI development.

  View details for DOI 10.1001/jamanetworkopen.2023.48422

  View details for PubMedID 38113040

• Inter-institutional data-driven education research: consensus values, principles, and recommendations to guide the ethical sharing of administrative education data in the Canadian medical education research context. Canadian medical education journal Grierson, L., Cavanagh, A., Youssef, A., Lee-Krueger, R., McNeill, K., Button, B., Kulasegaram, K. 2023; 14 (5): 113-120

  Abstract

  Background: Administrative data are generated when educating, licensing, and regulating future physicians but these data are rarely used beyond their pre-specified purposes. The capacity necessary for sensitive and responsive oversight that supports the sharing of administrative medical education data across institutions for research purposes needs to be developed.Method: A pan-Canadian consensus-building project was undertaken to develop agreement on the goals, benefits, risks, values, and principles that should underpin inter-institutional data-driven medical education research in Canada. A survey of key literature, consultations with various stakeholders and five successive knowledge synthesis workshops informed this project. Propositions were developed, driving subsequent discussions until collective agreement was distilled.Results: Consensus coalesced around six key principles: establishing clear purposes, rationale, and methodology for inter-institutional data-driven research a priori; informed consent from data generators in education systems is non-negotiable; multi-institutional data sharing requires special governance; data governance should be guided by data sovereignty; data use should be guided by an identified set of shared values; and best practices in research data-management should be applied.Conclusion: We recommend establishing a representative governance body, engaging trusted data facility, and adherence to extant data management policies when sharing administrative medical education data for research purposes in Canada.

  View details for DOI 10.36834/cmej.75874

  View details for PubMedID 38045068

• The Importance of Understanding Language in Large Language Models. The American journal of bioethics : AJOB Youssef, A., Stein, S., Clapp, J., Magnus, D. 2023; 23 (10): 6-7

  View details for DOI 10.1080/15265161.2023.2256614

  View details for PubMedID 37812091

• Is the Algorithm Good in a Bad World, or Has It Learned to be Bad? The Ethical Challenges of "Locked" Versus "Continuously Learning" and "Autonomous" Versus "Assistive" AI Tools in Healthcare. The American journal of bioethics : AJOB Youssef, A., Abramoff, M., Char, D. 2023; 23 (5): 43-45

  View details for DOI 10.1080/15265161.2023.2191052

  View details for PubMedID 37130390