Adoption of Generative AI by Academic Biomedical Researchers
Open accessAbstract
Understanding how biomedical researchers are making use of generative AI is critical to informed decision making about how to support ethical adoption of the technology and assessing the risks and opportunities it presents to the research enterprise. However, most studies of the use of generative AI by academic researchers have cast a wide net rather than focusing on adoption in specific disciplines or domains. To this end, we conducted a survey of biomedical researchers.
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Interest in using AI to support research is extremely high.
“First, interest in using AI to support research is extremely high (Fig. 1).”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [12]
The Introduction describes biomedical researchers as having a “moderate degree of interest” in using generative AI for research. The “Interest in generative AI as a research tool” section reports that about a third are very or extremely interested, rather than interest being extremely high overall. Page numbers are not available in the supplied passages. (LLM, conf=0.86)
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Experimental — accuracy not yet measuredThe same surveys reveal widespread uncertainty about how to begin, with many researchers reporting limited familiarity with existing tools, limited confidence in their ability to use them responsibly, and limited institutional guidance on best practices.
“Second, the same surveys reveal widespread uncertainty about how to begin.Many researchers report limited familiarity with existing tools, limited confidence in their own ability to use them responsibly, and limited institutional guidance on best practices.”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [12]
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Experimental — accuracy not yet measuredDifferent surveys report mixed, but generally increasing adoption of AI by the scientific community.
“Different surveys report mixed, but generally increasing adoption of AI by the scientific community.”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [12]
The Introduction and Adoption of generative AI sections describe differing survey findings and mixed levels of use: many biomedical researchers had tried generative AI, but few used it regularly. The Longitudinal data collection section calls this survey a snapshot and says future data are needed to measure adoption over time. The paper therefore does not establish a generally increasing trend across the scientific community, and its findings concern generative AI rather than AI broadly. (LLM, conf=0.84)
Experimental — accuracy not yet measuredSurveys across the scientific community consistently show three dominant themes: high interest, uncertainty about how to begin, and concerns about accuracy, bias, reproducibility, and erosion of critical thinking.
“Surveys across the scientific community consistently show three dominant themes [4][5][6][7][8][9][10][11][12] .”
From Reimagining biomedical science workflows in the age of large language models (2026) · cites this paper as [12]
The Introduction and Discussion (pages not provided) report interest in generative AI, uncertainty about best practices, and concerns about accuracy; Social and research ethics also discusses bias. This paper does not establish that surveys consistently find these themes across the scientific community, and the cited passages do not identify erosion of critical thinking or reproducibility as survey themes. (LLM, conf=0.84)
Experimental — accuracy not yet measured
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Cites
The Case for Generative AI in Scholarly Practice
Berg, C. (2023) · doi:10.2139/ssrn.4407587 · Cited by 69 · see citing works
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Cited by
Sorted by citations
Making AI Generative for Higher Education: Adoption and Challenges Among Instructors and Researchers
Baytas, C., & Ruediger, D. (2025) · Ithaka S+R · doi:10.18665/sr.322677 · Cited by 23 · see citing works
Self-citationReimagining biomedical science workflows in the age of large language models
Roberts, K. F., Koutarapu, S., Melendez, J., Landsness, E. C., Reese, J. T., Elbert, D. L., & Bateman, R. J. (2026) · npj Dementia · doi:10.1038/s44400-026-00126-3
“Surveys across the scienti fi c community consistently show three dominant themes 4 – 12 .”
- Authors
- Ruediger, D., McCracken, C., & Skinner, M.
- Publisher
- Ithaka S+R
- Published
- 2024-10-17
- OpenAlex
- W4403448294
- Cited by
- 8
- References
- 1
- Citations by year
- 2026: 5 · 2025: 3
- Type
- report
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- generative AI, artificial intelligence adoption, biomedical scientists, academic research, research ethics, technology adoption, research enterprise
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