The use of artificial intelligence in the treatment of rare diseases: A scoping review.

IF 1.1 Q2 MEDICINE, GENERAL & INTERNAL
Da He, Ru Wang, Zhilin Xu, Jiangna Wang, Peipei Song, Haiyin Wang, Jinying Su
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Abstract

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 (n = 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 (n = 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.

人工智能在罕见病治疗中的应用:范围综述。
随着人工智能(AI)在医学和医疗保健领域的应用日益广泛,AI 技术有望改善罕见病的诊断、治疗和预后。目前,现有的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。本综述旨在系统分析人工智能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了相关研究,并制定了检索文章的筛选标准。从三个数据库中找到的 407 篇文章中,我们选择了来自 8 个国家的 13 篇文章,这些文章研究了 10 种不同的罕见病。最常研究的罕见疾病是罕见神经系统疾病(5/13,38.46%)。在已确定的四个治疗领域中,7 篇文章(53.85%)侧重于药物研究,其中 5 篇特别侧重于药物发现(药物再利用、药物靶点和小分子抑制剂的发现),1 篇侧重于临床前研究(药物相互作用),1 篇侧重于临床研究(临床参数的信息强度评估)。在所选的 13 篇文章中,我们共发现了 32 种不同的算法,其中随机森林(RF)是最常用的算法(n = 4/32,12.50%)。在这些文章中,人工智能治疗罕见病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 项研究涉及药物研究领域,使用的是 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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intractable & rare diseases research
Intractable & rare diseases research MEDICINE, GENERAL & INTERNAL-
CiteScore
2.10
自引率
0.00%
发文量
29
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