THE ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN MEDICAL DIAGNOSTICS
Keywords:
Artificial Intelligence, Medical Diagnostics, Machine Learning, Healthcare Technology, Clinical Decision Support, Medical Imaging, Predictive Analytics, Digital Health, Diagnostic Accuracy, Precision MedicineAbstract
Artificial Intelligence (AI) is transforming the landscape of modern medical diagnostics by enhancing accuracy, speed, and accessibility of healthcare services. This article explores how AI technologies—such as machine learning, deep learning, and natural language processing—are being integrated into diagnostic systems to identify diseases ranging from cancer to cardiovascular conditions. The paper highlights real-world applications including AI-assisted imaging, pathology, and predictive analytics. Moreover, it discusses the benefits, such as reduced diagnostic errors and improved clinical workflows, while also addressing challenges related to data privacy, ethical considerations, and the need for robust validation. Ultimately, AI holds significant promise for augmenting physician capabilities and advancing precision medicine.
References
1. Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books.
2. Jiang, F. et al. (2017). Artificial intelligence in healthcare: past, present and future. BMJ, 2(4), e000101. https://doi.org/10.1136/bmjhci-2017-000101
3. IBM Watson Health. (2020). AI for Oncology. https://www.ibm.com/watson-health
4. Esteva, A. et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118. https://doi.org/10.1038/nature21056
5. Attia, Z. et al. (2019). An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. The Lancet, 394(10201), 861–867.
6. Obermeyer, Z., & Emanuel, E. (2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine, 375, 1216–1219. https://doi.org/10.1056/NEJMp1606181
7. Litjens, G. et al. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
8. McKinney, S. M. et al. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://doi.org/10.1038/s41586-019-1799-6
9. Siemens Healthineers. (2021). AI-Rad Companion – Intelligent support in radiology. https://www.siemens-healthineers.com
10. Wang, Y. et al. (2018). Clinical information extraction applications: a literature review. Journal of Biomedical Informatics, 77, 34–49. https://doi.org/10.1016/j.jbi.2017.11.011
11. Kelly, C. J. et al. (2019). Key challenges for delivering clinical impact with artificial intelligence. BMC Medicine, 17, Article 195. https://doi.org/10.1186/s12916-019-1426-2
12. Tjoa, E., & Guan, C. (2020). A survey on explainable artificial intelligence (XAI): Towards medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314
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