Keywords
Abstract
The integration of data science methodologies into healthcare and medicine represents one of the most consequential technological transformations of the twenty-first century. This paper examines how predictive analytics, machine learning algorithms, natural language processing, and large-scale biomedical data integration are fundamentally reshaping clinical decision-making, disease surveillance, drug discovery, and patient care delivery.
Through a systematic review of peer-reviewed literature published between 2018 and 2025, this study identifies six major domains in which data science has demonstrated measurable clinical impact: early disease detection, personalized treatment planning, hospital resource optimization, epidemiological forecasting, genomics-driven precision medicine, and real-time patient monitoring.
The review further analyzes persistent challenges, including data privacy concerns, algorithmic bias, regulatory frameworks, and the integration gap between data science tools and frontline clinical workflows. Findings suggest that while substantial progress has been achieved, the responsible and equitable deployment of data-driven healthcare systems requires coordinated action from technologists, clinicians, ethicists, and policymakers.
References
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.
Futoma, J., Morris, J., & Lucas, J. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, 56, 229–238.
Khera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., & Kathiresan, S. (2018). Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nature Medicine, 24(9), 1219–1224.
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–1205.
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.
Office of the National Coordinator for Health Information Technology. (2015). Health IT Dashboard: Hospital EHR adoption. U.S. Department of Health and Human Services.
Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., & Wolmark, N. (2004). A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. New England Journal of Medicine, 351(27), 2817–2826.