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

PURPOSE: This study examines the use of Google's advanced large language models and the LangChain library for developing a chat counseling system for cataract disease.

METHODS

The proposed system integrates a manually constructed cataract disease information repository with Google Generative artificial intelligence (AI) models, while LangChain is used to simplify the development and deployment of the conversational pipeline. The effectiveness of the system is evaluated using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score, a standard metric for assessing the quality of generated responses.

RESULTS

Preliminary evaluation demonstrates an encouraging trend, indicating that the proposed system is suitable for building innovative and responsive counseling tools for cataract education.

CONCLUSION

The findings suggest that combining Google Generative AI models with LangChain and a curated cataract information repository can support the development of practical conversational systems in the domain of cataract disease.