He Da, Wang Ru, Xu Zhilin, Wang Jiangna, Song Peipei, Wang Haiyin, Su Jinying
Shanghai Health Development Research Center (Shanghai Medical Information Center), Shanghai, China.
EYE & ENT Hospital of Fudan University, Shanghai, China.
Intractable Rare Dis Res. 2024 Feb;13(1):12-22. doi: 10.5582/irdr.2023.01111.
With the increasing application of artificial intelligence (AI) in medicine and healthcare, AI technologies have the potential to improve the diagnosis, treatment, and prognosis of rare diseases. Presently, existing research predominantly focuses on the areas of diagnosis and prognosis, with relatively fewer studies dedicated to the domain of treatment. The purpose of this review is to systematically analyze the existing literature on the application of AI in the treatment of rare diseases. We searched three databases for related studies, and established criteria for the selection of retrieved articles. From the 407 unique articles identified across the three databases, 13 articles from 8 countries were selected, which investigated 10 different rare diseases. The most frequently studied rare disease group was rare neurologic diseases ( = 5/13, 38.46%). Among the four identified therapeutic domains, 7 articles (53.85%) focused on drug research, with 5 specifically focused on drug discovery (drug repurposing, the discovery of drug targets and small-molecule inhibitors), 1 on pre-clinical studies (drug interactions), and 1 on clinical studies (information strength assessment of clinical parameters). Across the selected 13 articles, we identified total 32 different algorithms, with random forest (RF) being the most commonly used ( = 4/32, 12.50%). The predominant purpose of AI in the treatment of rare diseases in these articles was to enhance the performance of analytical tasks (53.33%). The most common data source was database data (35.29%), with 5 of these studies being in the field of drug research, utilizing classic databases such as RCSB, PDB and NCBI. Additionally, 47.37% of the articles highlighted the existing challenge of data scarcity or small sample sizes.
随着人工智能（AI）在医学和医疗保健领域的应用日益增加，AI技术有潜力改善罕见病的诊断、治疗和预后。目前，现有研究主要集中在诊断和预后领域，致力于治疗领域的研究相对较少。本综述的目的是系统分析关于AI在罕见病治疗中应用的现有文献。我们在三个数据库中搜索相关研究，并制定了检索文章的选择标准。在三个数据库中识别出的407篇独特文章中，选取了来自8个国家的13篇文章，这些文章研究了10种不同的罕见病。研究最频繁的罕见病组是罕见神经系统疾病（=5/13，38.46%）。在确定的四个治疗领域中，7篇文章（53.85%）专注于药物研究，其中5篇专门关注药物发现（药物再利用、药物靶点和小分子抑制剂的发现），1篇关注临床前研究（药物相互作用），1篇关注临床研究（临床参数的信息强度评估）。在所选的13篇文章中，我们总共识别出32种不同的算法，随机森林（RF）是最常用的（=4/32，12.50%）。这些文章中AI在罕见病治疗中的主要目的是提高分析任务的性能（53.33%）。最常见的数据来源是数据库数据（35.29%），其中5项研究在药物研究领域，利用了RCSB、PDB和NCBI等经典数据库。此外，47.37%的文章强调了数据稀缺或样本量小的现有挑战。
