Bio

Dr.Youssef is a postdoctoral fellow at the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI), in the Department of Radiology. She received her Doctor of Philosophy (PhD) degree in Population Health and Medical Education from the Institute of Medical Science, University of Toronto in 2021, Canada. Her research interests lies at the intersection of artificial intelligence (AI) implementation and clinical evaluation. She works with multi-disciplinary research teams to assess, design, develop, and implement person-centered AI solutions that address a clinical need. Her research addresses the ethical, organizational, and workflow barriers that impede clinical adoption of AI in healthcare. Dr. Youssef co-leads the development of several AI educational programs that centers on building capacity for AI research by training diverse group of learners to facilitate safe and responsible use of AI in healthcare for public good.

Honors & Awards

  • 2022 Stanford Postdoc JEDI Champion Award, Office of Postdoctoral Affairs - Stanford University (09/20/22)

Boards, Advisory Committees, Professional Organizations

  • Post-doctoral Representative, Committee for Research - Faculty Senate, Stanford University (2021 - 2022)

Stanford Advisors

Contact

  • Academic
    ayoussef@stanford.edu
    University - Scholar Department: Radiology Position: Postdoctoral Scholar

Additional Info

Links

Lab Affiliations

All Publications

  • 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

  • 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

https://orcid.org/0000-0001-6505-8236