Applications of Digital Intelligence Technologies in Rare Disease Etiology Research

Yixuan GUO; Yi GAO; Hongrui ZHANG; Anqi HUANG; Yujie LI; Xudong ZHANG; Yaofang ZHANG; Jian GUO; Shuyang ZHANG

Return

Applications of Digital Intelligence Technologies in Rare Disease Etiology Research

VernacularTitle:数智化技术在罕见病病因研究中的应用
Author: Yixuan GUO ¹ ; Yi GAO ² ; Hongrui ZHANG ² ; Anqi HUANG ² ; Yujie LI ² ; Xudong ZHANG ² ; Yaofang ZHANG ² ; Jian GUO ³ ; Shuyang ZHANG ⁴
Author Information

1. State Key Laboratory of Complex, Severe, and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China;School of Public Health, Shanxi Medical University, Taiyuan 030001, China;.
2. School of Public Health, Shanxi Medical University, Taiyuan 030001, China;.
3. State Key Laboratory of Complex, Severe, and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China;.
4. State Key Laboratory of Complex, Severe, and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China;Department of Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China;Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China;.
Publication Type:Review
Keywords: digital intelligence technologies; rare diseases; etiology research
From: JOURNAL OF RARE DISEASES 2026;5(3):390-398
CountryChina
Language:Chinese
Abstract:
Digital intelligence technology is a comprehensive technical system with data as the key element and artificial intelligence as the core driving force. It provides innovative methodological support for addressing core bottlenecks in rare disease etiology research, including sample scarcity, high-dimensional datacomplexity, intricate pathogenic mechanisms, and challenges in causal validation. This paper systematically reviews the application pathways and current implementation of digital intelligence across four major stages of rare disease research, including etiological clue mining, causal inference, mechanism analysis, and clinical translation. Furthermore, it summarizes the technical strengths, existing limitations, and developmental challenges, aiming to inform optimization of the research paradigm and promote the sustainable advancement of rare disease research.