Employing General-Purpose and Biomedical Large Language Models with Advanced Prompt Engineering for Pharmacoepidemiologic Study Design

Background

The potential of large language models (LLMs) to automate and support pharmacoepidemiologic study design is an emerging area of interest, yet their reliability remains insufficiently characterized. General-purpose LLMs often display inaccuracies, while the comparative performance of specialized biomedical LLMs in this domain remains unknown.

Methods

This study evaluated general-purpose LLMs (GPT-4o and DeepSeek-R1) versus biomedically fine-tuned LLMs (QuantFactory/Bio-Medical-Llama-3-8B-GGUF and Irathernotsay/qwen2-1.5B-medical_qa-Finetune) using 46 protocols (2018-2024) from the HMA-EMA Catalogue and Sentinel System. Performance was assessed across relevance, logic of justification, and ontology-code agreement across multiple coding systems using Least-to-Most (LTM) and Active Prompting strategies.

Results

GPT-4o and DeepSeek-R1 paired with LTM prompting achieved the highest relevance and logic of justification scores, with GPT-4o-LTM reaching a median relevance score of 4 in 8 of 9 questions for HMA-EMA protocols. Biomedical LLMs showed lower relevance overall and frequently generated insufficient justification. All LLMs demonstrated limited proficiency in ontology-code mapping.

Conclusion

Off-the-shelf general-purpose LLMs currently offered more reliable support for pharmacoepidemiologic study design than the smaller biomedical LLMs evaluated. In the model-controlled comparison (GPT-4o with Least-to-Most versus Active prompting), prompt strategy did not significantly affect performance (paired p = 0.93), and prompt comparisons are therefore reported descriptively. Because the two model groups also differed in scale and instruction-tuning, this contrast should be interpreted as a comparison of readily deployable options rather than of biomedical specialization alone.