An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
For researchers and clinicians in rare diseases, this review highlights the early stage and gaps (e.g., real-world data, multilingual support) in LLM-based patient communication, but the findings are incremental as they only catalog existing work without proposing new methods.
This scoping review of 12 studies (2022-2026) found that LLM applications for rare disease patient education are nascent, dominated by ChatGPT, focused on question-answering with curated data, and evaluated mainly on accuracy rather than patient-centered metrics like readability or empathy.
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.