Szczegóły publikacji
Opis bibliograficzny
Cochrane evaluation of (semi-)automated review methods: protocol for an adaptive platform study within reviews / Gerald Gartlehner, Susan Banda, Max Callaghan, Jo-Ana Chase, Andreea Dobrescu, Angelika Eisele-Metzger, Ella Flemyng, Sean Gardner, Ursula Griebler, Bartosz Helfer, Paweł JEMIOŁO, Biljana Macura, Jan C. Minx, Anna Noel-Storr, Noosheen Rajabzadeh Tahmasebi, Amin Sharifan, Joerg J. Meerpohl, James Thomas // Journal of Clinical Epidemiology ; ISSN 0895-4356 . — 2026 — vol. 198 art. no. 112390, s. 1-10. — Bibliogr. s. 9-10, Abstr. — Publikacja dostępna online od: 2026-06-19. — P. Jemioło - dod. afiliacja: Faculty of Medicine, Department of Hygiene and Dietetics, Jagiellonian University Medical College, Krakow, Poland ; Cochrane Poland, Krakow, Poland
Autorzy (18)
- Gartlehner Gerald
- Banda Susan
- Callaghan Max
- Chase Jo-Ana
- Dobrescu Andreea
- Eisele-Metzger Angelika
- Flemyng Ella
- Gardner Sean
- Griebler Ursula
- Helfer Bartosz
- AGHJemioło Paweł
- Macura Biljana
- Minx Jan
- Noel-Storr Anna
- Tahmasebi Noosheen Rajabzadeh
- Sharifan Amin
- Meerpohl Joerg J.
- Thomas James
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 169327 |
|---|---|
| Data dodania do BaDAP | 2026-09-12 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.jclinepi.2026.112390 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Clinical Epidemiology |
Abstract
Background and Objectives: Artificial intelligence (AI) has the potential to improve the efficiency of evidence synthesis and reduce human error. However, robust methods for evaluating rapidly evolving AI tools within the practical workflows of evidence synthesis remain underdeveloped. This protocol describes a study design for assessing the effectiveness, efficiency, and usability of AI tools in comparison to traditional human-only workflows in the context of Cochrane systematic reviews. Methods: Members of the Cochrane Evaluation of (Semi-)Automated Review Methods (CESAR) project developed an adaptive platform study-within-a-review design, modeled after clinical platform trials. This design employs a master protocol to concurrently evaluate multiple AI tools (interventions) against a standard human-only process (control) across 3 key review tasks: title and abstract screening, full-text screening, and data extraction. The adaptive framework allows for the addition or removal of AI tools based on interim performance analyses without necessitating a restart of the study. Performance will be assessed using metrics such as accuracy (sensitivity, specificity, precision), efficiency (time on task), response stability, impact of errors, and usability, in alignment with Responsible use of AI in evidence SynthEsis principles. Results: The study will generate comparative data about the performance and usability of specific AI tools used in a semiautomated or fully automated manner relative to standard human effort. The protocol provides a flexible framework for the assessment of AI tools in evidence synthesis, addressing the limitations of static, one-time evaluations. Conclusion: This study protocol presents a novel methodological approach to addressing the challenges of evaluating AI tools for evidence syntheses. By validating entire workflows rather than individual technologies, the findings will establish an evidence base for determining the viability of integrating AI into evidence synthesis workflows. The adaptive design of this study is flexible and can be adopted by other investigators, ensuring that the evaluation framework remains relevant as new tools emerge.
