THE ROLE OF ARTIFICIAL INTELLIGENCE IN PREDICTIVE HEALTHCARE: TRANSFORMING EARLY DIAGNOSIS AND PREVENTIVE MEDICINE
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Keywords
Artificial Intelligence, Predictive Healthcare, Early Diagnosis, Convolutional Neural Networks, Preventive Medicine
Abstract
AI technology is a complex and developing area that has become a promising platform for predicting and preventing diseases. The purpose of this study was the improvement achieved through AI models like Random Forest and CNNs (Convolutional Neural Network) when compared with traditional diagnostic techniques in terms of accuracy, sensitivity, and specificity. The study showed that AI models had superior results to the classical ones, and CNNs had the best metrics; these models can analyze big data and study early signs of diseases. The results of this research have highlighted the capability of artificial intelligence to drive shocking changes in this essential sector of our lives through timely interferences and personalized treatment plans that will in one way or another enhance the quality of a patient’s life or a family’s budget towards treating diseases. The data quality, algorithm interpretability, and ethical issues are the issues that have not been solved completely yet. They are important to fix and address so that the use of AI to be fair and effectively applied to clinical practice. The study especially stresses on multi-disciplinary approach with technologists, clinicians, and policymakers to provide ethical and efficient solutions to integrating AI innovations in healthcare. It should be directed towards the creation of post-hoc interpretable models, as well as the construction of appropriate legal instruments to encourage the practice of AI in medical practice. The revolutionary application of AI in the prognostication of health and its capability to completely reframe early identification and preventive measures.
References
2. Bos, D., Wolters, F. J., Darweesh, S. K., Vernooij, M. W., de Wolf, F., Ikram, M. A., & Hofman, A. (2018). Cerebral small vessel disease and the risk of dementia: a systematic review and meta-analysis of population-based evidence. Alzheimer's & Dementia, 14(11), 1482-1492.
3. Breiman, L. (2001). Random forests. Machine learning, 45, 5-32.
4. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015, August). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21st ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1721-1730).
5. Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. New England journal of medicine, 372(9), 793-795.
6. Demirer, R. M., & Demirer, O. (2019, April). Early prediction of sepsis from clinical data using artificial intelligence. In 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT) (pp. 1-4). IEEE.
7. Driessen, A. H., Berger, W. R., Chan Pin Yin, D. R., Piersma, F. R., Neefs, J., van den Berg, N. W., ... & de Groot, J. R. (2017). Electrophysiologically guided thoracoscopic surgery for advanced atrial fibrillation: 5-year follow-up. Journal of the American College of Cardiology, 69(13), 1753-1754.
8. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
9. 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.
10. Gatti, D. M., Weber, S. N., Goodwin, N. C., Lammert, F., & Churchill, G. A. (2018). Genetic background influences susceptibility to chemotherapy-induced hematotoxicity. The pharmacogenomics journal, 18(2), 319-330.
11. Goh, K. H., Wang, L., Yeow, A. Y. K., Poh, H., Li, K., Yeow, J. J. L., & Tan, G. Y. H. (2021). Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nature communications, 12(1), 711.
12. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
13. Karimian, G., Petelos, E., & Evers, S. M. (2022). The ethical issues of the application of artificial intelligence in healthcare: a systematic scoping review. AI and Ethics, 2(4), 539-551.
14. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.
15. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: review, opportunities, and challenges. Briefings in Bioinformatics, 19(6), 1236-1246.
16. McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., ... & Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89-94.
17. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866-872.
18. Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018). Ensuring fairness in machine learning to advance health equity. Annals of Internal Medicine, 169(12), 866-872.
19. Reece, A. G., & Danforth, C. M. (2017). Instagram photos reveal predictive markers of depression. EPJ Data Science, 6(1), 15.
20. Shen, J., Zhang, C. J., Jiang, B., Chen, J., Song, J., Liu, Z., ... & Ming, W. K. (2019). Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR medical informatics, 7(3), e10010.
21. Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589-1604.
22. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
23. Taber, D. J., McGillicuddy, J. W., Bratton, C. F., Rohan, V. S., Nadig, S., Dubay, D., & Baliga, P. K. (2017). Cytolytic induction therapy improves clinical outcomes in African-American kidney transplant recipients. Annals of Surgery, 266(3), 450-456.
24. Vaid, A., Somani, S., Russak, A. J., De Freitas, J. K., Chaudhry, F. F., Paranjpe, I., ... & Glicksberg, B. S. (2020). Machine learning to predict mortality and critical events in a cohort of patients with COVID-19 in New York City: model development and validation. Journal of medical Internet research, 22(11), e24018.
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Dr Ashwini L H
Ex-Assistant Professor, JJM Medical College and rc Davanagere, Karnataka
Dr Vinaykumar L H
Administrative Medical Officer Phc Aladageri Hirekerur tq, Haveri District
Dr Hanumanaik L
Chief Medical Officer GH Harihara, Harihara tq, Davanagere District