ChatCM‐RAG: A deep learning‐based natural language processing pipeline for analysing ChatGPT applications in medicine using BERTopic and transformer‐based retrieval‐augmented generation
Abstract
The rapid adoption of ChatGPT in healthcare has generated extensive literature. However, systematic analysis of this emerging field using artificial intelligence (AI)‐powered tools remains challenging due to the volume and diversity of publications and the risk of AI‐generated hallucinations that compromise factual accuracy.
We developed ChatCM‐RAG, a deep learning pipeline integrating BERTopic with transformer‐based retrieval‐augmented generation to analyse ChatGPT applications in the Medicine literature.
We processed 904 peer‐reviewed articles (2022–2025) using a multi‐stage pipeline: BERTopic for topic modelling with UMAP dimensionality reduction and HDBSCAN clustering, Facebook AI Similarity Search for semantic retrieval, and transformer models (T5/GPT) for answer generation. The system was evaluated using representative medical queries across retrieval accuracy, generation quality, and system efficiency metrics.
ChatCM‐RAG identified four distinct topic clusters: general medical AI applications (46.2%), performance evaluation (13.9%), clinical applications and patient care (12.6%), and chatbot implementations (6.0%), with 21.2% unclustered documents. To reduce hallucination and ensure citation authenticity, the generation module is constrained to a curated corpus of 904 PubMed‐indexed documents and only permits PMID citations that exist in the retrieval set. In our pilot evaluation on eight representative medical queries, we observed 0 fabricated PMIDs and 0…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.