New Report From Ithaka S+R: “Adoption of Generative AI by Academic Biomedical Researchers”

The report, Adoption of Generative AI by Academic Biomedical Researchers was published today (10/17) by Ithaka S+R.

Authors

• Dylan Ruediger
• Chelsea McCracken
• Makala Skinner

From an Introductory Blog Post:

When ChatGPT was released in November 2022, it prompted an ongoing national conversation about the role of generative AI across all sectors of intellectual labor. Within the academy, that conversation has focused primarily on generative AI’s impact on instruction, with relatively little attention being given to its role in scholarly research. The field of biomedical research in particular has provided some of the most promising use cases for generative AI, as well as being a site for potentially significant harm caused by this new technology. Institutions and policymakers need to understand how biomedical researchers are using generative AI in order to provide guidance to maximize potential benefits and minimize harms.

Today, Ithaka S+R is excited to publish findings from our international survey of academic researchers, with a particular focus on those in the biomedical sciences. The survey, conducted with the support of the Chan Zuckerberg Initiative, provides a snapshot of researchers’ opinions about generative AI, how often they use it, which research tasks they use it for, and factors that serve as barriers to its use. Universities, professional organizations, and publishers will find the report to be valuable as they develop best practices around researchers’ use of generative AI. Funders and other stakeholders will gain insight into the adoption of this new technology to inform their decision making about how best to support cutting-edge research.

The survey’s key finding is that generative AI adoption is so far very mixed. While many biomedical researchers have experimented with using generative AI, that use is limited in scope and frequency. The main barrier to adoption is serious concern about the accuracy of generative AI’s outputs, while ethical concerns also rank highly as a barrier. Given the current quality of generative AI outputs and the lack of compelling best practices and models for its use, adoption of generative AI may plateau.

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The survey findings published today are an important addition to our ongoing work on the implications of generative AI. Later this year, we will publish our first report on the generative AI cohort project, which will include close analysis of the largest qualitative dataset to-date on the use of generative AI in higher ed. Our interviews with partners from across the cohort explore the impact of generative AI on teaching, learning, and research.

Report Resources

• Introductory Blog Post/Key Findings
• Key Data Breakdown (One Page)
• Full Text Report ||| Full Text Report (37 pages; PDF)

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