Academic Journal
AI-assisted protocol information extraction for improved accuracy and efficiency in clinical trial workflows.
| Τίτλος: | AI-assisted protocol information extraction for improved accuracy and efficiency in clinical trial workflows. |
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| Συγγραφείς: | Babaeipour R; Banting Health AI(1), 357 Bay St., Toronto, ON, M5H 4A6, Canada., Charest F; Banting Health AI(1), 357 Bay St., Toronto, ON, M5H 4A6, Canada. Electronic address: francois@bantinghealth.ai., Wright M; Banting Health AI(1), 357 Bay St., Toronto, ON, M5H 4A6, Canada. |
| Πηγή: | Journal of biomedical informatics [J Biomed Inform] 2026 Jul; Vol. 179, pp. 105036. Date of Electronic Publication: 2026 Apr 10. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1532-0480 (Electronic) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE |
| Imprint Name(s): | Publication: Orlando : Elsevier Original Publication: San Diego, CA : Academic Press, c2001- |
| Ιατρικοί όροι (MeSH): | Information Storage and Retrieval*/methods , Artificial Intelligence* , Workflow* , Clinical Trials as Topic* , Clinical Protocols*, Humans ; Generative Artificial Intelligence ; Intelligent Systems |
| Περίληψη: | Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial teams. Structuring protocol content into standard formats has the potential to improve efficiency, support documentation quality, and strengthen compliance. We evaluate an Artificial Intelligence (AI) system using generative LLMs with Retrieval-Augmented Generation (RAG) for automated clinical trial protocol information extraction. We compare the extraction accuracy of our clinical-trial-specific RAG process against that of publicly available (standalone) LLMs. We also assess the operational impact of AI-assistance on simulated extraction Clinical Research Coordinator (CRC) workflows. Our RAG process shows higher extraction accuracy (89.0%) than standalone LLMs with fine-tuned prompts (62.6%) against expert-supported reference annotations. In simulated extraction workflows, AI-assisted tasks are completed ≥40% faster, are rated as less cognitively demanding and are strongly preferred by users. While expert oversight remains essential, this suggests that AI-assisted extraction can enable protocol intelligence at scale, motivating the integration of similar methodologies into real-world clinical workflows to further validate its impact on feasibility, study start-up, and post-activation monitoring. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Francois Charest reports financial support was provided by Banting Health AI Inc. Madison Wright reports financial support was provided by Banting Health AI Inc. Ramtin Babaeipour reports financial support was provided by Banting Health AI Inc. Francois Charest reports a relationship with Banting Health AI Inc. that includes: employment and equity or stocks. Madison Wright reports a relationship with Banting Health AI Inc. that includes: employment and equity or stocks. Ramtin Babaeipour reports a relationship with Banting Health AI Inc. that includes: employment and equity or stocks. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: CRC workflows; Clinical trials; Information extraction; LLM; Protocols; RAG; Schedule of events |
| Entry Date(s): | Date Created: 20260412 Date Completed: 20260611 Latest Revision: 20260620 |
| Update Code: | 20260621 |
| DOI: | 10.1016/j.jbi.2026.105036 |
| PMID: | 41967790 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1532-0480 |
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| DOI: | 10.1016/j.jbi.2026.105036 |