An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866911596585943040 |
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| author | Zhan, Zaifu Hou, Yu Yu, Kai Zeng, Min Burgun, Anita Chen, Xiaoyi Zhang, Rui |
| author_facet | Zhan, Zaifu Hou, Yu Yu, Kai Zeng, Min Burgun, Anita Chen, Xiaoyi Zhang, Rui |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_14179 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review Zhan, Zaifu Hou, Yu Yu, Kai Zeng, Min Burgun, Anita Chen, Xiaoyi Zhang, Rui Computation and Language Artificial Intelligence 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. |
| title | An Underexplored Frontier: Large Language Models for Rare Disease Patient Education and Communication -- A scoping review |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2604.14179 |