The “Artificial Intelligence Statistician”: Utilizing Generative Artificial Intelligence to Select an Appropriate Model and Execute Network Meta-Analyses
This exploratory study demonstrates the feasibility of LLMs to automate key components of NMAs, determining the requisite NMA framework based only on input data. Further exploring these capabilities could clarify their role in streamlining NMA workflows.
Authors
- Kasper Johannesen (ORCID: https://orcid.org/0000-0001-8075-8003)
- Tim Reason
- Cheryl Jones (ORCID: https://orcid.org/0000-0001-5363-3694)
- Bill Malcolm (ORCID: https://orcid.org/0000-0003-1664-3927)
- Yunchou Wu
- Emma Benbow
Institutions
- Home Office (GB)
- Cambridge Scientific (United States) (US)
- Pima County Health Department (US)
- Digital Scientific (United Kingdom) (GB)
- Bristol-Myers Squibb (United Kingdom) (GB)
- Bristol-Myers Squibb (Sweden) (SE)
- Bristol-Myers Squibb (Italy) (IT)
Publication Details
- Journal
- Value in Health
- Published
- 2025-08-12
- DOI
- https://doi.org/10.1016/j.jval.2025.08.001
- Citations
- 1
- Primary Topic
- Health Systems, Economic Evaluations, Quality of Life
- Type
- article
- Field-Weighted Citation Impact
- 3.13
Funders
- Bristol-Myers Squibb