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Evolving Consultation: Enhancing Ophthalmic Diagnostic Performance Using Large Language Model.

Taiga Inooka1, Hikaru Ota1, Yosuke Taki1

  • 1Department of Ophthalmology, Nagoya University Graduate School of Medicine, Nagoya, Japan.

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|February 23, 2026
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Summary
This summary is machine-generated.

Large language models like ChatGPT-4o enhance ophthalmologists' diagnostic reasoning, especially for residents. However, careful management is needed due to increased variability in factuality and safety when using AI tools.

Keywords:
Artificial intelligenceClinical decision support systemsLarge language modelsMedical educationProblem solving

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Education

Background:

  • Large language models (LLMs) are increasingly used in healthcare.
  • Studies assessing LLM effectiveness in ophthalmology for complex differential diagnoses are lacking.
  • This study evaluates ChatGPT-4o's impact on ophthalmologists' clinical reasoning.

Purpose of the Study:

  • To assess the effectiveness of ChatGPT-4o in improving ophthalmologists' diagnostic reasoning.
  • To determine which experience levels benefit most from LLM assistance.
  • To analyze changes in response quality, factuality, and safety.

Main Methods:

  • Prospective study involving 20 ophthalmologists (10 residents, 10 board-certified).
  • Ten original ophthalmic clinical scenarios were used.
  • Responses were collected before and after ChatGPT-4o assistance and evaluated on coherency, factuality, comprehensiveness, and safety.

Main Results:

  • ChatGPT-4o significantly improved coherency, comprehensiveness, and safety scores for both groups (P < 0.001).
  • Factuality scores did not significantly improve (P = 0.114 and 0.839).
  • Increased citation frequency was observed, but 44% were inaccurate; variability in factuality and safety increased.

Conclusions:

  • ChatGPT-4o enhances diagnostic reasoning and response quality, particularly for residents.
  • Integration requires managing increased variability in factuality and safety.
  • Retrieval-augmented generation systems may ensure accurate and safe AI-assisted clinical information.