Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 Aug 5:82:14362.
doi: 10.3389/bjbs.2025.14362. eCollection 2025.

Artificial Intelligence in Biomedical Sciences: A Scoping Review

Rasha Abu-El-Ruz  1 , Ali Hasan  2 , Dima Hijazi  3 , Ovelia Masoud  1 , Atiyeh M Abdallah  1 , Susu M Zughaier  4 , Maha Al-Asmakh  1   5
Affiliations
Review

Artificial Intelligence in Biomedical Sciences: A Scoping Review

Rasha Abu-El-Ruz et al. Br J Biomed Sci. .

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.

Keywords: NAACLS; artificial intelligence; biomedical sciences; clinical; scoping review.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
PRISMA flow chart.
FIGURE 2
FIGURE 2
The number of articles published over time*. *Sharp increase in relevant publications over the last decade, highlighting increased interest in AI use in biomedical sciences.
FIGURE 3
FIGURE 3
Bar chart showing the frequency of AI models in the reported articles*. *Others are automated AI-based diagnosis, generative adversarial networks, knowledge discovery and semantic analysis, and simulation and visualization.
FIGURE 4
FIGURE 4
Bar chart showing the number of publications by discipline in biomedical sciences*. *Publications from clinical perspective dominated across all biomedical science disciplines, with microbiology, general medical laboratory and haematology leading overall output. Publications from educational perspective were minimal, highlighting a gap in academic-focused literature.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial Intelligence in Healthcare: Past, Present and Future. Stroke Vasc Neurol (2017) 2(4):230–43. 10.1136/svn-2017-000101 - DOI - PMC - PubMed
    1. Kueper JK, Terry AL, Zwarenstein M, Lizotte DJ. Artificial Intelligence and Primary Care Research: A Scoping Review. Ann Fam Med (2020) 18(3):250–8. 10.1370/afm.2518 - DOI - PMC - PubMed
    1. Hamet P, Tremblay J. Artificial Intelligence in Medicine. Metabolism (2017) 69s:S36–s40. 10.1016/j.metabol.2017.01.011 - DOI - PubMed
    1. Felder RA. Laboratory Systems Integration: Robotics and Automation. Ann Biol Clin (Paris) (1991) 49(5):298–300. - PubMed
    1. Goto T, Hongo K, Yako T, Hara Y, Okamoto J, Toyoda K, et al. The Concept and Feasibility of EXPERT: Intelligent Armrest Using Robotics Technology. Neurosurgery (2013) 72:A39–A42. 10.1227/NEU.0b013e318271ee66 - DOI - PubMed

LinkOut - more resources