ArticleJournal of biomedical informatics2024
Rare disease diagnosis using knowledge guided retrieval augmentation for ChatGPT.
Article in Journal of biomedical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Improving large language model applications in biomedicine with retrieval-augmented generation: a systematic review, meta-analysis, and clinical development guidelines.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Article
- Rare-Disease Diagnosis on the ZebraMap Multimodal Case Report Dataset: A Hybrid Pipeline with Grounded Explainability.Sensors (Basel, Switzerland) · 2026Article
- A data-driven method for research trend analysis in a scientific discipline: Application to the journal of biomedical informatics.Journal of biomedical informatics · 2026Article
- Research on ChatGPT generated text detection model based on phonetic feature extraction and semantic features.Scientific reports · 2026Article
- Multidisciplinary blinded randomized expert evaluation of large language models for clinical diagnosis and management.Communications medicine · 2026Article
- ChatGPT in precision medicine.APL bioengineering · 2026Review
- SHREC: A framework for advancing next-generation computational phenotyping with large language models.PLOS digital health · 2026Article
- Hide and Seek: Privacy-Preserving Artificial Intelligence with a Feasibility Study in Rare Disease Diagnosis.medRxiv : the preprint server for health sciences · 2026Article
- TCM-DiffRAG: personalized syndrome differentiation reasoning method for traditional Chinese medicine based on knowledge graph and chain of thought.Frontiers in medicine · 2026Article
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- ZebraMap: A Multimodal Rare Disease Knowledge Map with Automated Data Aggregation & LLM-Enriched Information Extraction Pipeline.Diagnostics (Basel, Switzerland) · 2025Article
- Scalable scientific interest profiling using large language models.Journal of biomedical informatics · 2025Article
- Cracking the code: a head-to-head comparison of expert clinicians and artificial intelligence in diagnosing rare diseases.Orphanet journal of rare diseases · 2025Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Leveraging Open-Source Large Language Models to Identify Undiagnosed Patients with Rare Genetic Aortopathies.medRxiv : the preprint server for health sciences · 2025Article
- SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models.ArXiv · 2025Article
- Leveraging long context in retrieval augmented language models for medical question answering.NPJ digital medicine · 2025Article
- Preliminary evaluation of ChatGPT model iterations in emergency department diagnostics.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Although rare diseases individually have a low prevalence, they collectively affect nearly 400 million individuals around the world. On average, it takes five years for an accurate rare disease diagnosis, but many patients remain undiagnosed or misdiagnosed. As machine learning technologies have been used to aid diagnostics in the past, this study aims to test ChatGPT's suitability for rare disease diagnostic support with the enhancement provided by Retrieval Augmented Generation (RAG). RareDxGPT, our enhanced ChatGPT model, supplies ChatGPT with information about 717 rare diseases from an external knowledge resource, the RareDis Corpus, through RAG. In RareDxGPT, when a query is entered, the three documents most relevant to the query in the RareDis Corpus are retrieved. Along with the query, they are returned to ChatGPT to provide a diagnosis. Additionally, phenotypes for thirty different diseases were extracted from free text from PubMed's Case Reports. They were each entered with three different prompt types: "prompt", "prompt + explanation" and "prompt + role play." The accuracy of ChatGPT and RareDxGPT with each prompt was then measured. With "Prompt", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 37 % of the cases correct. With "Prompt + Explanation", RareDxGPT had a 43 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. With "Prompt + Role Play", RareDxGPT had a 40 % accuracy, while ChatGPT 3.5 got 23 % of the cases correct. To conclude, ChatGPT, especially when supplying extra domain specific knowledge, demonstrates early potential for rare disease diagnosis with adjustments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.