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Making Chatbots more human: deep reasoning large language models in ophthalmology.

Xuanqiao Lin1, Yizhou Yang1, Yuecheng Ren2

  • 1Department of Ophthalmology, Eye, Ear, Nose, and Throat Hospital of Fudan University, Shanghai, China.

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Summary

Deep-reasoning large language models (LLMs) show promise in ophthalmology for tasks like EHR summarization. However, clinical benefits and practical implementation challenges require further investigation before widespread adoption.

Keywords:
Chatbotsartificial intelligenceclinical decision supportdeep reasoninglarge language modelsophthalmology

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Deep-reasoning large language models (LLMs) are advancing, with applications extending to ophthalmology.
  • Current ophthalmic workflows primarily use conventional computer vision for image interpretation, while text-based LLMs support language-centric tasks.
  • Multimodal AI systems integrating visual and reasoning capabilities have been explored in research settings.

Purpose of the Study:

  • To explore the potential applications of deep-reasoning LLMs in ophthalmology.
  • To assess the current state and challenges of implementing AI in ophthalmic clinical practice.
  • To identify future research directions for AI in ophthalmology.

Main Methods:

  • Review of recent advances in deep-reasoning LLMs and their applications in ophthalmology.
  • Analysis of current ophthalmic workflows and the role of AI.
  • Discussion of challenges and limitations of AI implementation in clinical settings.

Main Results:

  • LLMs can enhance language-centric workflows like electronic health record (EHR) summarization and patient education drafting.
  • Multimodal systems show potential for personalized planning but lack established clinical benefits.
  • Significant challenges to practical implementation include computational demands, privacy, bias, transparency, and system performance.

Conclusions:

  • Deep-reasoning LLMs offer promising assistive capabilities for ophthalmic practice.
  • Addressing operational, ethical, and technical constraints is crucial for successful integration.
  • Prospective interventional studies are needed to validate clinical benefits and patient outcomes.