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Updated: Jun 21, 2025
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Development of AI-generated medical responses using the ChatGPT for cancer patients
Jae-Woo Lee1, In-Sang Yoo2, Ji-Hye Kim3
1Department of Family Medicine, Chungbuk National University Hospital, Cheongju, Republic of Korea; Department of Family Medicine, Chungbuk National University College of Medicine, Cheongju, Republic of Korea.
Background And Objective:
To develop a healthcare chatbot service (AI-guided bot) that conducts real-time conversations using large language models to provide accurate health information to patients.
Methods:
To provide accurate and specialized medical responses, we integrated several cancer practice guidelines. The size of the integrated meta-dataset was 1.17 million tokens. The integrated and classified metadata were extracted, transformed into text, segmented to specific character lengths, and vectorized using the embedding model. The AI-guide bot was implemented using Python 3.9. To enhance the scalability and incorporate the integrated dataset, we combined the AI-guide bot with OpenAI and the LangChain framework. To generate user-friendly conversations, a language model was developed based on Chat-Generative Pretrained Transformer (ChatGPT), an interactive conversational chatbot powered by GPT-3.5. The AI-guide bot was implemented using ChatGPT3.5 from Sep. 2023 to Jan. 2024.
Results:
The AI-guide bot allowed users to select their desired cancer type and language for conversational interactions. The AI-guided bot was designed to expand its capabilities to encompass multiple major cancer types. The performance of the AI-guide bot responses was 90.98 ± 4.02 (obtained by summing up the Likert scores).
Conclusions:
The AI-guide bot can provide medical information quickly and accurately to patients with cancer who are concerned about their health.
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