NIH PMC: RAG for Cancer Information

paper

PubMed Central(peer-reviewed)·pmc.ncbi.nlm.nih.gov/articles/PMC12425422/

Credibility Rating

4/5

High(4)

High quality. Established institution or organization with editorial oversight and accountability.

Rating inherited from publication venue: PubMed Central

Relevant to AI safety practitioners interested in practical mitigation of hallucinations in high-stakes deployment contexts; illustrates how RAG serves as a grounding mechanism for factual reliability in medical AI, a domain where errors carry direct human harm potential.

Metadata

Importance: 32/100journal articleprimary source

Summary

This study develops and evaluates a Retrieval-Augmented Generation (RAG) system to reduce hallucinations in AI chatbots providing cancer information. By grounding responses in authoritative medical sources, the system improves factual accuracy while maintaining response quality and utility. The findings demonstrate RAG as a practical technical safety intervention for high-stakes medical AI applications.

Key Points

•Hallucinations in medical AI chatbots pose serious risks; RAG grounds responses in authoritative cancer information sources to reduce fabricated content.
•The study empirically evaluates the tradeoff between accuracy and response quality/utility in a RAG-based medical chatbot system.
•RAG represents a deployment-stage technical safety measure applicable to high-stakes domains where misinformation has real patient consequences.
•Findings suggest RAG can improve reliability of AI health information without significantly degrading user experience or response usefulness.
•Demonstrates a domain-specific safety approach relevant to broader questions about deploying AI in sensitive, high-stakes information contexts.

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Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study - PMC
 

 
 
 
 
 
 
 
 
 
 
 
 

 
 
 

 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
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 JMIR Cancer . 2025 Sep 11;11:e70176. doi: 10.2196/70176 
 
 
 Reducing Hallucinations and Trade-Offs in Responses in Generative AI Chatbots for Cancer Information: Development and Evaluation Study

 
 Sota Nishisako 
 
 Sota Nishisako , MSc 

 
 1 Institute for Cancer Control, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan, 81 3 3547 5201, 81 3 3547 8577 
 Find articles by Sota Nishisako 
 
 
 1, ✉ , Takahiro Higashi 
 
 Takahiro Higashi , MD, PhD 

 
 1 Institute for Cancer Control, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan, 81 3 3547 5201, 81 3 3547 8577 
 
 2 Department of Public Health and Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan 
 Find articles by Takahiro Higashi 
 
 
 1, 2 , Fumihiko Wakao 
 
 Fumihiko Wakao , MD 

 
 3 Headquarter for Cancer Information Services, National Cancer Center, Tokyo, Japan 
 Find articles by Fumihiko Wakao 
 
 
 3 
 
 Editor: Naomi Cahill 
 
 
 Author information 

 Article notes 

 Copyright and License information 

 
 
 
 
 1 Institute for Cancer Control, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan, 81 3 3547 5201, 81 3 3547 8577 
 
 2 Department of Public Health and Health Policy, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan 
 
 3 Headquarter for Cancer Information Services, National Cancer Center, Tokyo, Japan 
 
 ✉ Sota Nishisako, MSc, Institute for Cancer Control, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan, 81 3 3547 5201, 81 3 3547 8577; sotanishisako@gmail.com 

 
 
 Received 2024 Dec 17; Revised 2025 Jul 4; Accepted 2025 Jul 7; Collection date 2025.

 
 
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Resource ID: d9670d76a4fa9cde | Stable ID: YzU2YjhkNz