Machine Learning-Assisted Health Economics and Policy Reviews: A Comparative Assessment
This study highlights the potential of ML tools to enhance the efficiency of title and abstract screening in health economics and policy literature reviews. To fully realise this potential, it is essential for regulatory bodies to establish comprehensive guidelines that ensure ML-assisted reviews uphold rigorous evidence quality standards, thereby enhancing their integrity and reliability.
Authors
- Oriana Ciani (ORCID: https://orcid.org/0000-0002-3607-0508)
- Vittoria Ardito (ORCID: https://orcid.org/0000-0003-2034-716X)
- Michael Drummond (ORCID: https://orcid.org/0000-0002-6126-0944)
- Ludovico Cavallaro (ORCID: https://orcid.org/0009-0002-3426-2570)
Institutions
- University of York (GB)
- Bocconi University (IT)
Publication Details
- Journal
- Applied Health Economics and Health Policy
- Published
- 2025-03-28
- DOI
- https://doi.org/10.1007/s40258-025-00963-y
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.31
Funders
- HORIZON EUROPE Framework Programme