A LangChain-facilitated conversational approach to cataract disease: A pilot study with large language models

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Natural Language Processing

Background:

• Cataract disease management requires accessible patient education.
• Developing effective digital health tools is crucial for patient support.
• Large language models offer new avenues for health information delivery.

Purpose of the Study:

• To develop and evaluate a chat counseling system for cataract disease using advanced AI.
• To explore the integration of Google Generative AI models and the LangChain library.
• To assess the system's suitability for cataract patient education.

Main Methods:

• Developed a conversational pipeline using Google Generative AI models and the LangChain library.
• Integrated a curated information repository specific to cataract disease.
• Evaluated system performance using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score.

Main Results:

• Preliminary evaluations indicate the system's potential for cataract education.
• The developed system demonstrated an encouraging trend in response quality.
• The integration of AI and LangChain proved effective for building the conversational system.

Conclusions:

• Google Generative AI models and LangChain can facilitate the creation of practical conversational systems for cataract disease.
• The proposed system is suitable for developing innovative counseling tools for cataract education.
• Combining AI with curated medical information offers a promising approach for patient support.