Integrating artificial intelligence tools in health research
Disciplines conducting health research rely on discipline-specific workflows that are distinct from data science, the discipline which develops most artificial intelligence (AI)-enabled research tools. Here we reflect on the impact of such differences in workflows and provide practical advice for leveraging AI-enabled research tools in health research. Further, we provide recommendations for how to prepare the next generation of health researchers for the inevitable influx of research integrating AI-enabled tools.
Authors
- Stefano Tancredi (ORCID: https://orcid.org/0009-0002-9110-7921)
- Viktor von Wyl (ORCID: https://orcid.org/0000-0002-8754-9797)
- Arnaud Chioléro (ORCID: https://orcid.org/0000-0002-5544-8510)
- Jana Sedláková (ORCID: https://orcid.org/0000-0002-6887-5941)
- Jürgen Bernard (ORCID: https://orcid.org/0000-0001-8741-9709)
- Frerik Smit (ORCID: https://orcid.org/0009-0007-6034-2300)
- Andreas M. Baumer
- Laura C. Rosella
- Gemma Postill
- Sintieh Ekongefeyin
Institutions
- University of Fribourg (CH)
- University of Toronto (CA)
- ZHAW Zurich University of Applied Sciences (CH)
- University of Zurich (CH)
- Trillium Health Centre (CA)
- Prevention Institute (US)
- Swiss School of Public Health (CH)
- Zurich University of Teacher Education (CH)
- Public Health Ontario (CA)
- McGill University (CA)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2026-05-16
- DOI
- https://doi.org/10.1038/s41746-026-02739-7
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Universität Zürich