Development of Application-Specific Large Language Models to Facilitate Research Ethics Review
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arXiv
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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908557374390272 |
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| author | Mann, Sebastian Porsdam Jiehao, Joel Seah Latham, Stephen R. Savulescu, Julian Aboy, Mateo Earp, Brian D. |
| author_facet | Mann, Sebastian Porsdam Jiehao, Joel Seah Latham, Stephen R. Savulescu, Julian Aboy, Mateo Earp, Brian D. |
| contents | Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processes. These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets, and equipped with retrieval capabilities to access up-to-date, context-relevant information. We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support. While addressing concerns about accuracy, context sensitivity, and human oversight, we acknowledge remaining challenges such as over-reliance on AI and the need for transparency. By enhancing the efficiency and quality of ethical review while maintaining human judgment in critical decisions, IRB-specific LLMs offer a promising tool to improve research oversight. We call for pilot studies to evaluate the feasibility and impact of this approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10741 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Development of Application-Specific Large Language Models to Facilitate Research Ethics Review Mann, Sebastian Porsdam Jiehao, Joel Seah Latham, Stephen R. Savulescu, Julian Aboy, Mateo Earp, Brian D. Computation and Language Computers and Society Institutional review boards (IRBs) play a crucial role in ensuring the ethical conduct of human subjects research, but face challenges including inconsistency, delays, and inefficiencies. We propose the development and implementation of application-specific large language models (LLMs) to facilitate IRB review processes. These IRB-specific LLMs would be fine-tuned on IRB-specific literature and institutional datasets, and equipped with retrieval capabilities to access up-to-date, context-relevant information. We outline potential applications, including pre-review screening, preliminary analysis, consistency checking, and decision support. While addressing concerns about accuracy, context sensitivity, and human oversight, we acknowledge remaining challenges such as over-reliance on AI and the need for transparency. By enhancing the efficiency and quality of ethical review while maintaining human judgment in critical decisions, IRB-specific LLMs offer a promising tool to improve research oversight. We call for pilot studies to evaluate the feasibility and impact of this approach. |
| title | Development of Application-Specific Large Language Models to Facilitate Research Ethics Review |
| topic | Computation and Language Computers and Society |
| url | https://arxiv.org/abs/2501.10741 |