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Large language models like ChatGPT and Gemini show high coherence in designing pharmacoepidemiological studies but require expert review due to variable relevance and coding accuracy.

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Area of Science:

  • Pharmacoepidemiology
  • Artificial Intelligence in Healthcare
  • Health Informatics

Background:

  • Pharmacoepidemiological studies are crucial for understanding drug safety and effectiveness.
  • Large language models (LLMs) offer potential tools for research design support.
  • Evaluating LLM capabilities in specialized scientific fields is essential.

Purpose of the Study:

  • To assess the utility of ChatGPT and Gemini in supporting pharmacoepidemiological study design.
  • To evaluate the coherence, relevance, and coding accuracy of LLM-generated responses for study protocols.
  • To identify strengths and limitations of LLMs in this domain.

Main Methods:

  • Analysis of 48 pharmacoepidemiological study protocols (2018-2024).
  • Human expert evaluation of LLM responses across seven key study design components.
  • Assessment of coding accuracy using ATC, CPT, and ICD systems.

Main Results:

  • Both LLMs demonstrated high coherence (>90%) in most study design components.
  • ChatGPT excelled in 'Index date' and 'Study design' coherence.
  • Gemini led in 'Study outcome' and 'Study exposure' coherence.
  • Relevance varied, with lower agreement on covariates and follow-up.
  • Coding accuracy showed low agreement, highest with ATC (50%).

Conclusions:

  • LLMs show promise for supporting pharmacoepidemiological study design, particularly in coherence.
  • Limitations in relevance and coding accuracy necessitate critical expert oversight.
  • Further development is needed to enhance LLM utility in complex research design.