An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review

arXiv cs.CL / 4/17/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

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Key Points

Rare diseases affect more than 300 million people globally, and the article highlights ongoing gaps in patient education and communication despite the complexity of care and limited expertise.
A scoping review (Jan 2022–Mar 2026) found 12 studies using large language models for rare-disease patient education and communication, mainly leveraging general-purpose models such as ChatGPT.
Research so far is concentrated on question-answering with curated prompt/question sets, with few studies using real-world data or modeling longitudinal communication over time.
Evaluation practices largely emphasize accuracy, while patient-centered metrics (e.g., readability, empathy, and communication quality) and multilingual communication are comparatively underexplored.
The review concludes the area is still early and recommends future work focused on patient-centered design, domain-adapted approaches, and real-world, safe and adaptive deployment.

Abstract

Rare diseases affect over 300 million people worldwide and are characterized by complex care pathways, limited clinical expertise, and substantial unmet communication needs throughout the long patient journey. Recent advances in large language models (LLMs) offer new opportunities to support patient education and communication, yet their application in rare diseases remains unclear. We conducted a scoping review of studies published between January 2022 and March 2026 across major databases, identifying 12 studies on LLM-based rare disease patient education and communication. Data were extracted on study characteristics, application scenarios, model usage, and evaluation methods, and synthesized using descriptive and qualitative analyses. The literature is highly recent and dominated by general-purpose models, particularly ChatGPT. Most studies focus on patient question answering using curated question sets, with limited use of real-world data or longitudinal communication scenarios. Evaluations are primarily centered on accuracy, with limited attention to patient-centered dimensions such as readability, empathy, and communication quality. Multilingual communication is rarely addressed. Overall, the field remains at an early stage. Future research should prioritize patient-centered design, domain-adapted methods, and real-world deployment to support safe, adaptive, and effective communication in rare diseases.