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Exploring variation in research priorities generated by AI tools.

John Garry1, Mark Tomlinson2,3, Maria Lohan3,4

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This summary is machine-generated.

Large language models (LLMs) can aid health research priority-setting. Gemini outputs align with other AI tools, while DeepSeek and Perplexity offer distinct technical and public health perspectives, respectively.

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Area of Science:

  • Health research
  • Artificial intelligence
  • Medical informatics

Background:

  • Large language models (LLMs) are increasingly utilized in research.
  • The role of AI in health-related research priority-setting exercises (RPSEs) is emerging.
  • Differences in research priorities generated by various AI tools are not well understood.

Purpose of the Study:

  • To compare research priorities generated by five AI tools for four diseases (cancer, COVID-19, HIV, Alzheimer's).
  • To assess the stability of differences between AI tool outputs using identical and modified prompts.

Main Methods:

  • Comparative analysis of AI tool outputs using SBERT-BioBERT embeddings and cosine similarity.
  • Evaluation of five AI tools: DeepSeek, ChatGPT, Claude, Perplexity, and Gemini.
  • Stability assessment through prompt re-runs and modifications.

Main Results:

  • Gemini's outputs were highly similar to other AI tools.
  • DeepSeek and Perplexity produced the most distinct outputs.
  • DeepSeek focused on technical medical issues, while Perplexity emphasized public health concerns, a stable distinction.

Conclusions:

  • Gemini is suitable for researchers preferring a single AI model for health research priorities.
  • DeepSeek and Perplexity provide complementary insights when multiple AI models are used.