Abu-El-Ruz Rasha, Hasan Ali, Hijazi Dima, Masoud Ovelia, Abdallah Atiyeh M, Zughaier Susu M, Al-Asmakh Maha
Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar.
School of Arts and Sciences, Lebanese American University, Beirut, Lebanon.
Br J Biomed Sci. 2025 Aug 5;82:14362. doi: 10.3389/bjbs.2025.14362. eCollection 2025.
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.
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.
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.
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.
AI has generally been under characterized in the biomedical sciences due to variability in AI models, disciplines, and perspectives of applicability.
人工智能（AI）在医疗保健诊断、治疗、监测和疾病预防中发挥着越来越重要的作用。尽管人工智能在生物医学科学中得到了广泛应用，但其特征尚未得到描述。
本范围综述的目的是探索生物医学科学中的人工智能。具体目标是综合六个方面，阐述生物医学科学中人工智能的特征，并深入了解其与教育的相关性。
本范围综述是根据阿克西和奥马利框架制定的。使用广泛的搜索词对PubMed、Embase和科学网数据库进行无限制搜索。将文献导入EndNote进行筛选和提取。对数据进行分类和综合，以定义六个方面来讨论生物医学科学中的人工智能。
共检索到2249篇文章进行筛选和提取，本综述纳入了192篇文章。从提取的数据中综合出六个方面：方面（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关于人工智能的出版物数量不断增加。方面（2）：按地区划分的生物医学科学中的人工智能，表明生物医学科学中关于人工智能的出版物主要来自高收入国家，尤其是美国。方面（3）：按模型划分的生物医学科学中的人工智能，确定机器学习是最常被报道的模型。方面（4）：按学科划分的生物医学科学中的人工智能，微生物学是生物医学科学中与人工智能最常相关的学科。方面（5）：生物医学科学教育中的人工智能，这方面仅限于六项研究，表明在生物医学科学中人工智能教育应用的研究存在差距。方面（6）：生物医学科学中人工智能的机遇和局限性，主要报道的机遇包括效率、准确性、普遍适用性和实际应用。局限性包括；模型复杂性、适用性有限和算法稳健性。
由于人工智能模型、学科和适用视角的差异，人工智能在生物医学科学中的特征总体上尚未得到充分描述。
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