Key takeaways from Stanford’s symposium on AI for Data Science
Numerous symposia and conferences have been held to discuss the promise of Artificial Intelligence (AI). Many center on its potential to transform fields like health and medicine, law, education, business, and more. Further, while many AI-focused events include those data scientists involved in developing foundational models, to our knowledge, there has been little attention on AI's role for data science and the data scientist. In a new symposium series with its inaugural debut in December 2024 titled <i>AI for Data Science</i>, thought leaders convened to discuss both the promises and challenges of integrating AI into the workflows of data scientists. A keynote address by Michael Pencina from Duke University together with contributions from three panels covered a wide range of topics including rigor, reproducibility, the training of current and future data scientists, and the potential of AI's integration in public health.
Authors
- James Zou (ORCID: https://orcid.org/0000-0001-8880-4764)
- Mark A. Musen (ORCID: https://orcid.org/0000-0003-3325-793X)
- Melissa L. Bondy (ORCID: https://orcid.org/0000-0002-0278-414X)
- Ivor B. Horn (ORCID: https://orcid.org/0000-0002-5571-4671)
- Manisha Desai (ORCID: https://orcid.org/0000-0002-6949-2651)
- Sara J. Singer (ORCID: https://orcid.org/0000-0002-3374-1177)
- Bryan Bunning (ORCID: https://orcid.org/0000-0003-3092-0038)
- Nigam H. Shah (ORCID: https://orcid.org/0000-0001-9385-7158)
- Mary Boulos (ORCID: https://orcid.org/0000-0001-5046-598X)
- Eleni Linos
- Steven N. Goodman (ORCID: https://orcid.org/0000-0002-3872-5723)
- John Auerbach
- Michael Pencina (ORCID: https://orcid.org/0000-0002-1968-2641)
- Michelle D. Williams (ORCID: https://orcid.org/0000-0002-9133-7332)
- Jade Benjamin‐Chung (ORCID: https://orcid.org/0000-0003-3631-3132)
- Laurence Baker
- Lee Sanders (ORCID: https://orcid.org/0000-0003-3231-6918)
- Ni Deng
Institutions
- Boston Children's Hospital (US)
- Duke University (US)
- Stanford Health Care (US)
- Stanford Medicine (US)
- Boston Children's Museum (US)
- ICF International (United Kingdom) (GB)
- ICF International (United States) (US)
- Stanford University (US)
Publication Details
- Journal
- Journal of Clinical and Translational Science
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1017/cts.2025.10154
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- University of Cambridge
- National Institutes of Health
- Centers for Disease Control and Prevention
- Stanford Bio-X
- Stanford Institute for Economic Policy Research
- National Cancer Institute
- Stanford Cancer Institute