Artificial Intelligence in Modern Biology: Transforming Research and Healthcare
DOI:
https://doi.org/10.59436/jsiane.v6i3.2.2583-2093Keywords:
Artificial Intelligence, Machine Learning, Biology, Healthcare, Precision Medicine, Bioinformatics, Drug Discovery, Generative AI, Deep LearningAbstract
Artificial Intelligence (AI) has emerged as a transformative force in biological sciences and healthcare, fundamentally reshaping research methodologies, data analysis, disease diagnosis, drug discovery, and personalized medicine. The rapid advancement of high-throughput biological technologies has generated unprecedented volumes of genomic, proteomic, metabolomic, and clinical data, necessitating sophisticated computational approaches for effective interpretation. AI technologies, including machine learning, deep learning, natural language processing, computer vision, and generative artificial intelligence, provide powerful tools for extracting meaningful insights from complex biological datasets. In biological research, AI facilitates genome annotation, protein structure prediction, systems biology modeling, ecological monitoring, and synthetic biology applications. In healthcare, AI-driven systems have improved diagnostic accuracy, accelerated drug development, enhanced medical imaging interpretation, and enabled precision medicine approaches tailored to individual patients. Recent advances in generative AI and foundation models have further expanded opportunities for scientific discovery by supporting literature analysis, molecular design, and hypothesis generation. Despite these benefits, challenges related to data quality, algorithmic bias, explainability, ethical governance, privacy protection, and regulatory oversight remain significant barriers to widespread implementation. This review examines the evolution of AI in biological sciences, discusses its major applications in research and healthcare, evaluates current limitations, and highlights future directions. The review concludes that responsible integration of AI into biological and medical sciences has the potential to accelerate scientific innovation, improve healthcare delivery, and contribute significantly to global health and sustainable development.
References
Aebersold, R., & Mann, M. (2016). Mass-spectrometric exploration of proteome structure and function. Nature, 537(7620), 347–355.
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016). Deep learning for computational biology. Molecular Systems Biology, 12(7), 878.
Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522.
Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. New England Journal of Medicine, 372(9), 793–795.
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019). Deep learning: New computational modelling techniques for genomics. Nature Reviews Genetics, 20(7), 389–403.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583–589.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Libbrecht, M. W., & Noble, W. S. (2015). Machine learning applications in genetics and genomics. Nature Reviews Genetics, 16(6), 321–332.
Litjens, G., et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88.
McKinney, S. M., et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94.
Nature Editorial. (2023). Tools such as ChatGPT threaten transparent science; here are our ground rules for their use. Nature, 613, 612.
Stokes, J. M., et al. (2020). A deep learning approach to antibiotic discovery. Cell, 180(4), 688–702.
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
Vamathevan, J., et al. (2019). Applications of machine learning in drug discovery and development. Nature Reviews Drug Discovery, 18(6), 463–477.
World Health Organization. (2021). Ethics and Governance of Artificial Intelligence for Health.
Zhavoronkov, A., et al. (2019). Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nature Biotechnology, 37(9), 1038–1040.
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