Research article
● Open access
Artificial Intelligence in Biomedical Engineering: A Comprehensive Review of Technological Transformation and Healthcare Innovation
Abstract
Background: The convergence of biomedical engineering (BME) with artificial intelligence (AI) and advanced computational methodologies is fundamentally reshaping modern healthcare. This interdisciplinary synergy enables unprecedented capabilities in medical diagnostics, therapeutic interventions, patient monitoring, and healthcare delivery systems. As AI technologies continue to evolve rapidly, understanding their integration with biomedical engineering becomes essential for researchers, clinicians, and policymakers.
Objective: This comprehensive review systematically examines the transformative impact of artificial intelligence on biomedical engineering, exploring key application domains including medical imaging, diagnostics, functional genomics, healthcare informatics, and therapeutic systems. The study aims to synthesize current knowledge, identify emerging trends, and propose future research directions at the intersection of AI and BME.
Methods: A systematic literature review was conducted across major scientific databases including PubMed, IEEE Xplore, Scopus, and Web of Science for publications between 2015 and 2025. The search strategy combined terms related to artificial intelligence, machine learning, deep learning, biomedical engineering, medical imaging, genomics, and healthcare informatics. Studies were included if they addressed AI applications in biomedical contexts with empirical validation or comprehensive theoretical frameworks. Thematic analysis was employed to synthesize findings across multiple domains.
Results: The review reveals that AI technologies, particularly machine learning and deep learning, have achieved remarkable success across diverse biomedical applications. In medical imaging, AI algorithms demonstrate diagnostic accuracy comparable to or exceeding human experts in detecting pathologies from X-ray, CT, MRI, and ultrasound images. In functional genomics, machine learning enables analysis of high-throughput sequencing data, identification of genetic variants, and prediction of gene function and regulation. Healthcare informatics has been transformed through natural language processing for electronic health record analysis, predictive modeling for patient outcomes, and clinical decision support systems. Therapeutic applications include AI-assisted robotic surgery, personalized drug delivery systems, and brain-computer interfaces for neural rehabilitation. The integration of AI with wearable sensors and Internet of Medical Things (IoMT) enables continuous patient monitoring and proactive healthcare interventions.
Conclusion: Artificial intelligence is revolutionizing biomedical engineering by enabling data-driven insights, personalized medicine, and intelligent healthcare systems. The synergy between AI algorithms and biomedical technologies enhances diagnostic accuracy, treatment precision, and patient outcomes. However, challenges remain regarding data privacy, algorithmic bias, regulatory frameworks, and ethical considerations. Future research should focus on explainable AI, federated learning for privacy-preserving analytics, multimodal data integration, and robust validation in clinical settings. The continued collaboration between engineers, data scientists, clinicians, and policymakers will be essential for realizing the full potential of AI-driven biomedical innovation.
Keywords
Artificial intelligence; biomedical engineering; machine learning; deep learning; medical imaging; functional genomics; healthcare informatics; precision medicine; brain-computer interfaces; Internet of Medical Things
References
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Akhtar, Z. B. (2025b). Exploring AI for pain research management: A deep dive investigative exploration. Journal of Pain Research and Management, 1(1), 28-42.
Akhtar, Z. B., & Rawol, A. T. (2025). Advanced methods towards functional genomics and medical informatics: The artificial intelligence (AI) and integrated computing perspectives. Trends in Telemedicine and E-Health, 5(5).
Akhtar, Z. B., & Rozario, V. S. (2025). AI perspectives within computational neuroscience: EEG integrations and the human brain. In Artificial Intelligence and Applications (Vol. 3, No. 2, pp. 145-160).
Akhtar, Z. B., Rawol, A. T., & Rozario, V. S. (2025). Voices in the night: Sleep paralysis and the intersection of brain, trauma, traditions. Journal of Pain Research and Management, 1(1), 62-74.
Amponsah, O., Nopuo, P. S. A., Manga, F. A., Catli, N. B., & Labus, K. (2025). Future-oriented biomaterials based on natural polymer resources: Characteristics, application innovations, and development trends. International Journal of Molecular Sciences, 26(12), 5518.
Bagherpour, R., Bagherpour, G., & Mohammadi, P. (2025). Application of artificial intelligence in tissue engineering. Tissue Engineering Part B: Reviews, 31(1), 31-43.
Chan, S., Reddy, V., Myers, B., Thibodeaux, Q., Brownstone, N., et al. (2020). Machine learning in dermatology: Current applications, opportunities, and limitations. Dermatology and Therapy, 10(3), 365-386.
Chen, W., Sun, Q., Chen, X., Xie, G., Wu, H., et al. (2021). Deep learning methods for heart sounds classification: A systematic review. Entropy, 23(6), 667.
Davis, R., Jr., Duggal, I., Peppas, N. A., & Gaharwar, A. K. (2025). Designing the next generation of biomaterials through nanoengineering. Advanced Materials, 37(39), 2501761.
Ebrahim, N. A., Soliman, S. M. (2026). Advanced biomaterials and biomedical devices for studying tumor-associated fibroblasts: Current trends, innovations, and future prospects. Biomedical Materials & Devices, 4(1), 287-301.
Felix, R. B., Shabazz, A., Holeman, W. P., Han, S., Wyble, M., et al. (2025). From promise to practice: Recent growth in 30 years of tissue engineering commercialization. Tissue Engineering Part A, 31(7-8), 285-302.
Infante, T., Cavaliere, C., Punzo, B., Grimaldi, V., Salvatore, M., et al. (2021). Radiogenomics and artificial intelligence approaches applied to cardiac computed tomography angiography and cardiac magnetic resonance for precision medicine in coronary heart disease: A systematic review. Circulation: Cardiovascular Imaging, 14(12), 1133-1146.
Jurczak, K. M., van Der Boon, T. A., Devia-Rodriguez, R., Schuurmann, R. C., Sjollema, J., et al. (2025). Recent regulatory developments in EU Medical Device Regulation and their impact on biomaterials translation. Bioengineering & Translational Medicine, 10(2), e10721.
Kim, S. J., Jeong, B., Park, K. D., Hahn, S. K., & Noh, I. (2025). Smart biomaterials for delivery of drugs and cells. Biomaterials Research, 29, 0227.
Kobayashi, K., Horii, T., Wada, A., Yamanaka, K., Hagiwara, A., et al. (2025). Current progress of tissue engineering with stem cells in urology: Updated review in 2025. International Journal of Urology, 32(11), 1535-1543.
Kocek, K. S., Trossmann, V. T., & Scheibel, T. (2025). 3D-printed and recombinant spider silk particle reinforced collagen composite scaffolds for soft tissue engineering. Advanced Functional Materials, 35(15), 2407760.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Liu, J., Song, Q., Yin, W., Li, C., An, N., et al. (2025). Bioactive scaffolds for tissue engineering: A review of decellularized extracellular matrix applications and innovations. Exploration, 5(1), 20230078.
Liu, S., Manshahi, F., Chen, J., Wang, X., Wang, S., et al. (2025). Unleashing the potential of electroactive hybrid biomaterials and self-powered systems for bone therapeutics. Nano-Micro Letters, 17(1), 44.
Liu, Y., Wu, F., Lyu, C., Li, S., Ye, J., & Qu, X. (2022). Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform. Transportation Research Part E: Logistics and Transportation Review, 161, 102694.
Manning, C. D., & Schütze, H. (1999). Foundations of statistical natural language processing. MIT Press.
Mitchell, T. M. (1997). Machine learning. McGraw-Hill.
Mozafari, M. (2025). How artificial intelligence shapes the future of biomaterials? Next Materials, 7, 100381.
Panahi, O. (2025). Innovative biomaterials for sustainable medical implants: A circular economy approach. European Journal of Innovative Studies and Sustainability, 1(2), 20-29.
Patel, K. (2025). Cardiac regeneration: A promising future for tissue engineering and cardiac repair. Columbia Undergraduate Science Journal, 19(1).
Qiu, S., Chen, G. Y., Qin, Y. D., Li, T. T., & Yang, F. Q. (2025). Recent advance in electrochemical chiral recognition based on biomaterials (2019-2024). Molecules, 30(16), 3386.
Rahaman, M. T., & Hossain Khan, M. S. (2025). Biomaterials for manufacturing environmentally sustainable textiles and apparel: Sources, applications, challenges, enablers and future directions. International Journal of Environmental Science and Technology, 1-56.
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Ng, A. Y. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.
Sadraei, A., & Naghib, S. M. (2025). 4D printing of physical stimuli-responsive hydrogels for localized drug delivery and tissue engineering. Polymer Reviews, 65(1), 104-168.
Senyange, B., Wesana, J., Van Huylenbroeck, G., Gellynck, X., & De Steur, H. (2025). Tissue engineering in the agri-food industry: Current status, socio-economic overview and regulatory compliance. Current Opinion in Biotechnology, 91, 103228.
Sotirakos, S., Fouda, B., Mohamed Razif, N. A., Cribben, N., Mulhall, C., et al. (2022). Harnessing artificial intelligence in cardiac rehabilitation: A systematic review. Future Cardiology, 18(2), 154-164.
Vinchurkar, K., Bukke, S. P. N., Jain, P., Bhadoria, J., Likhariya, M., et al. (2025). Advances in sustainable biomaterials: Characterizations, and applications in medicine. Discover Polymers, 2(1), 2.
Wang, H., Zu, Q., Chen, J., Yang, Z., & Ahmed, M. A. (2021). Application of artificial intelligence in acute coronary syndrome: A brief literature review. Advances in Therapy, 38(10), 5078-5086.
Wang, W., Wang, J., Hu, Z., Yan, X., Gao, Q., et al. (2025). Advancing aggregation-induced emission-derived biomaterials in viral, tuberculosis, and fungal infectious diseases. Aggregate, 6(3), e715.
Zhang, R., Lin, H., Wang, Z., Cheng, S., Li, C., et al. (2025). Research progress on antimicrobial biomaterials. Macromolecular Bioscience, 25(9), e00239.
Zhu, Y., Guo, S., Ravichandran, D., Ramanathan, A., Sobczak, M. T., et al. (2025). 3D-printed polymeric biomaterials for health applications. Advanced Healthcare Materials, 14(1), 2402571.
Ziaran, S., Danišovič, L., & Hammer, N. (2025). Tissue engineering and regenerative medicine: Advances, controversies, and future directions. Frontiers in Bioengineering and Biotechnology, 13, 1568490.