Rasha Abu-El-Ruz, Ali Hasan, Dima Hijazi, Ovelia Masoud, Atiyeh M Abdallah, Susu M Zughaier, Maha Al-Asmakh
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引用次数: 0
Abstract
Background: Artificial intelligence (AI) is increasingly playing important roles in healthcare diagnosis, treatment, monitoring, and prevention of diseases. Despite this widespread implementation of AI in biomedical sciences, it has yet to be characterized.
Aim: The aim of this scoping review is to explore AI in biomedical sciences. Specific objectives are to synthesize six scopes addressing the characteristics of AI in biomedical sciences and to provide in-depth understanding of its relevance to education.
Methods: This scoping review has been developed according to Arksey and O'Malley frameworks. PubMed, Embase, and Web of Science databases were searched using broad search terms without restrictions. Citations were imported into EndNote for screening and extraction. Data were categorized and synthesized to define six scopes discussing AI in biomedical sciences.
Results: A total of 2,249 articles were retrieved for screening and extraction, and 192 articles were included in this review. Six scopes were synthesized from the extracted data: Scope (1): AI in biomedical sciences by decade, highlighting the increasing number of publications on AI in biomedical sciences. Scope (2): AI in biomedical sciences by region, showing that publications on AI in biomedical sciences mainly originate from high-income countries, particularly the USA. Scope (3): AI in biomedical sciences by model, identifying machine learning as the most frequently reported model. Scope (4): AI in biomedical sciences by discipline, with microbiology the discipline most commonly associated with AI in biomedical sciences. Scope (5): AI in biomedical sciences education, which was limited to only six studies, indicating a gap in research on the educational application of AI in biomedical sciences. Scope (6): Opportunities and limitations of AI in biomedical sciences, where major reported opportunities include efficiency, accuracy, universal applicability, and real-world application. Limitations include; model complexity, limited applicability, and algorithm robustness.
Conclusion: AI has generally been under characterized in the biomedical sciences due to variability in AI models, disciplines, and perspectives of applicability.
背景：人工智能（AI）在医疗保健诊断、治疗、监测和预防疾病方面发挥着越来越重要的作用。尽管人工智能在生物医学科学领域得到了广泛应用，但它还没有被描述出来。目的：本综述的目的是探讨人工智能在生物医学科学中的应用。具体目标是综合六个范围，解决生物医学科学中人工智能的特点，并深入了解其与教育的相关性。方法：根据Arksey和O'Malley框架进行范围审查。PubMed， Embase和Web of Science数据库使用无限制的广泛搜索词进行搜索。将引文导入EndNote进行筛选和提取。对数据进行分类和综合，以定义讨论生物医学科学中的人工智能的六个范围。结果：共检索到筛选提取文献2249篇，纳入文献192篇。从提取的数据中合成了六个范围：范围(1)：按十年划分的生物医学科学中的人工智能，突出了生物医学科学中人工智能的出版物数量不断增加。范围(2)：按地区划分的生物医学领域的人工智能，表明生物医学领域的人工智能出版物主要来自高收入国家，尤其是美国。范围(3)：通过模型分析生物医学科学中的人工智能，确定机器学习是最常报道的模型。范围(4)：按学科划分的生物医学科学中的人工智能，微生物学是与生物医学科学中的人工智能最常相关的学科。范围(5)：人工智能在生物医学教育中的应用，仅限6项研究，表明人工智能在生物医学教育中的应用研究存在空白。范围(6)：人工智能在生物医学科学中的机遇与局限，其中报告的主要机遇包括效率、准确性、普遍适用性和现实应用。限制包括:模型复杂性，有限的适用性和算法鲁棒性。结论：由于人工智能模型、学科和应用角度的差异，人工智能在生物医学科学中的特征普遍不足。
期刊介绍：
The British Journal of Biomedical Science is committed to publishing high quality original research that represents a clear advance in the practice of biomedical science, and reviews that summarise recent advances in the field of biomedical science. The overall aim of the Journal is to provide a platform for the dissemination of new and innovative information on the diagnosis and management of disease that is valuable to the practicing laboratory scientist.
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