Optimizing the research ethics committee’s assessment of biomedical research proposals

Vázquez Masana, Ruth

Optimizing the research ethics committee’s assessment of biomedical research proposals

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Citation

Vázquez Masana, R. Optimizing the research ethics committee’s assessment of biomedical research proposals. 2025. handle: http://hdl.handle.net/10230/71348

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Abstract

Research Ethics Committees (REC) are under significant pressure as the volume of Biomedical Research Proposals (BRP) grows, the escalating complexity of scientific knowledge due to the advent of Artificial Intelligence, and the proliferation of immature proposals submitted for evaluation. This study introduces Pre-Val-AI-THICS, an artificial intelligence-based prevalidation tool and an ontology, designed to optimize the review of BRP by REC at Hospital Clínic de Barcelona (HCB). The work employed a sequential methodology comprising an initial analysis of REC evaluation workflows followed by the design and feasibility assessment of the tool during a hackathon event. It culminated in the development of a proof of concept integrating both automated extraction of contents and AI-assisted review capabilities. The Pre-Val-AI-THICS system consists of two primary components: a Python-based module utilizing regular expressions for BRP content extraction and verifying compliance with the HCB’s standardized template, and an AI module leveraging Chain-of-Thought prompt engineering with Llama 3.2 3B, to detect and suggest corrections related to data management and study design inconsistencies. Complementing these components, a preliminary BRP ontology was constructed using Protégé to formalize key concepts and relationships inherent in BRP evaluation. It was demonstrated that Pre-Val-AI-THICS effectively identified recurrent data management and structural errors, providing actionable recommendations to enhance protocol quality prior to REC submission. While the ontology remains in a nascent stage, it is envisioned as a foundation for the future development of a knowledge graph supporting standardized representation and evaluation of BRP across diverse committees. Overall, the findings underscore the potential of AI-driven prevalidation tools to streamline REC workflows, reduce administrative burden, and improve the consistency and rigor of ethical assessments. Further research will focus on advancing ontology-driven navigation and interoperability to enable comprehensive, semantically enriched access to BRP content and evaluation criteria.