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Large Language Models Use in Dry Eye Disease: Perplexity AI versus ChatGPT4.

Sowmya V Kothandan1, Stephanie L Watson2, Sayan Basu3

  • 1Brien Holden Eye Research Centre (BHERC), LV Prasad Eye Institute, Hyderabad, India.

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Summary
This summary is machine-generated.

Large language models (LLMs) like ChatGPT and Perplexity AI show similar performance for patient queries in dry eye disease (DED) and can aid in patient education. However, they are not yet suitable for generating research ideas or conducting literature searches in DED.

Keywords:
Artificial intelligenceChatGPTLLMPerplexity AIdry eye disease

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Dry eye disease (DED) is a prevalent condition affecting millions globally.
  • The integration of artificial intelligence (AI) in healthcare, specifically large language models (LLMs), offers potential for clinical and research applications.
  • Evaluating the efficacy and limitations of LLMs in specialized medical fields like DED is crucial.

Purpose of the Study:

  • To compare the utility of two prominent LLMs, ChatGPT4 and Perplexity AI, in addressing queries relevant to dry eye disease (DED) within clinical and research contexts.
  • To assess the performance of LLMs in generating patient-centered information and research ideas for DED.

Main Methods:

  • Ocular surface experts developed 12 prompts, comprising 10 patient-focused questions and 2 research ideas for DED.
  • Responses from ChatGPT4 and Perplexity AI were evaluated by experts using a standardized grading system (1-4) assessing accuracy, compassion, comprehensiveness, professionalism, humanness, and overall quality.
  • Mean scores for each response characteristic were compared between the two LLMs.

Main Results:

  • Both ChatGPT4 and Perplexity AI achieved comparable overall quality scores for patient-related DED queries (mean scores 2.6 and 2.7, respectively).
  • ChatGPT4 demonstrated superior performance in generating DED-related research ideas compared to Perplexity AI (mean score 3.4 vs. 2.6).
  • Perplexity AI's citations were web-based and not evidence-based, and inter-rater reliability for response characteristics was low for both models.

Conclusions:

  • LLMs like ChatGPT and Perplexity AI show comparable utility for patient-related queries in DED, suggesting a potential role in patient education and counseling under supervision.
  • These LLMs are currently not sufficiently advanced for generating novel research ideas or conducting comprehensive literature reviews in the field of DED.
  • Further development and validation are needed to enhance the reliability and evidence-based nature of LLM responses in specialized medical domains.