Protocol for evaluating ChatGPT in biomedical association generation and verification using a RAG-enabled, cross-model majority voting workflow
arXiv:2605.30400v1 Announce Type: new Abstract: We present a protocol to evaluate ChatGPT's ability to generate disease-centric biomedical associations. It outlines how we generate the associations, validate the biological entities using biomedical ontologies, and verify associations using literature. The protocol includes a self-consistency strategy to assess generative reliability across ChatGPT models. To address ontology exact-match limitations, we provide a use case performing semantic verification through a workflow enabled by Retrieval-Augmented Generation (RAG) powered by open-source l
The proliferation of advanced AI models like ChatGPT necessitates rigorous, standardized evaluation protocols to ensure their reliability and safety in critical domains such as biomedicine.
This protocol introduces a robust, multi-faceted approach to evaluating generative AI, which is crucial for building trust and enabling safe deployment of AI in high-stakes fields like drug discovery and medical research.
The development of a RAG-enabled, cross-model majority voting workflow raises the standard for AI model evaluation, moving beyond simple outputs to address reliability and semantic verification.
- · AI model evaluators
- · Biomedical researchers
- · Healthcare sector
- · Open-source AI
- · Undeveloped AI evaluation methods
- · Companies relying on unvalidated AI
- · Generative AI models with poor reliability
Improved reliability and trustworthiness of generative AI applications in biomedical research.
Accelerated adoption of AI in drug discovery and personalized medicine due to enhanced confidence in model outputs.
New regulatory frameworks and industry standards emerging to mandate such rigorous AI evaluation protocols, impacting AI development cycles globally.
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