Principal Investigator
Bio
Publications
-
The AI life cycle: a holistic approach to creating ethical AI for health decisions.
Nature medicine
2022
More
View details for DOI 10.1038/s41591-022-01993-y
View details for PubMedID 36163298
-
Picture a data scientist: a call to action for increasing diversity, equity, and inclusion in the age of AI.
Journal of the American Medical Informatics Association : JAMIA
2022
More
Abstract
The lack of diversity, equity, and inclusion continues to hamper the artificial intelligence (AI) field and is especially problematic for healthcare applications. In this article, we expand on the need for diversity, equity, and inclusion, specifically focusing on the composition of AI teams. We call to action leaders at all levels to make team inclusivity and diversity the centerpieces of AI development, not the afterthought. These recommendations take into consideration mitigation at several levels, including outreach programs at the local level, diversity statements at the academic level, and regulatory steps at the federal level.
View details for DOI 10.1093/jamia/ocac156
View details for PubMedID 36048021
-
Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model
SCIENTIFIC DATA
2022; 9 (1): 24
More
Abstract
As artificial intelligence (AI) makes continuous progress to improve quality of care for some patients by leveraging ever increasing amounts of digital health data, others are left behind. Empirical evaluation studies are required to keep biased AI models from reinforcing systemic health disparities faced by minority populations through dangerous feedback loops. The aim of this study is to raise broad awareness of the pervasive challenges around bias and fairness in risk prediction models. We performed a case study on a MIMIC-trained benchmarking model using a broadly applicable fairness and generalizability assessment framework. While open-science benchmarks are crucial to overcome many study limitations today, this case study revealed a strong class imbalance problem as well as fairness concerns for Black and publicly insured ICU patients. Therefore, we advocate for the widespread use of comprehensive fairness and performance assessment frameworks to effectively monitor and validate benchmark pipelines built on open data resources.
View details for DOI 10.1038/s41597-021-01110-7
View details for Web of Science ID 000746595100001
View details for PubMedID 35075160
-
Digital twins for predictive oncology will be a paradigm shift for precision cancer care.
Nature medicine
2021
More
View details for DOI 10.1038/s41591-021-01558-5
View details for PubMedID 34824458
Google Scholar and PubMed.
Academic Appointments
Associate Professor, Medicine - Biomedical Informatics Research
Associate Professor, Biomedical Data Science
Associate Professor, Surgery - General Surgery
Member, Stanford Cancer Institute
Professional Education
M.S., Stanford University, Health Services Research (2013)
Ph.D., University Claude Bernard, Lyon 1, Computational Biology (1999)
M.P.H., Yale University, Epidemiology (1993)
B.A., University California, Irvine, Psychology (1991)
B.S., University of California, Irvine, Biology (1991)