THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE DECISION-MAKING
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Keywords
Artificial intelligence, Healthcare decision-making, Machine learning, Diagnosis, Personalized medicine, Ethical considerations.
Abstract
The use of artificial intelligence (AI) in healthcare decision-making has gained significant attention in recent years due to its potential to revolutionize the way medical decisions are made. This review article aims to provide a comprehensive overview of the current state of AI applications in healthcare decision-making, including its benefits, challenges, and future directions. We discuss various AI techniques such as machine learning, natural language processing, and deep learning, and how they are being used to improve diagnosis, treatment planning, personalized medicine, and patient outcomes. Additionally, we explore the ethical and regulatory considerations surrounding the use of AI in healthcare decision-making, as well as the potential impact on healthcare professionals and patients. By synthesizing the existing literature, this review highlights the opportunities and challenges associated with the integration of AI into clinical practice and provides insights into how AI can be effectively utilized to enhance healthcare decision-making processes.
References
2. Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing machine learning in health care—addressing ethical challenges. New England Journal of Medicine, 378(11), 981-983.
3. Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13), 1317-1318.
4. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.
5. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. W., Feng, M., Ghassemi, M., ... & Celi, L. A. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
6. Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA, 316(22), 2402-2410.
7. Chen, J. H., Asch, S. M., & Machine Learning and Prediction in Medicine Team. (2017). Machine learning and prediction in medicine—beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507-2509.
8. Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Langlotz, C. (2017). CheXNet: Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.
9. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
10. Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
11. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
12. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
13. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Navigating the landscape of model interpretability. arXiv preprint arXiv:1706.07979.
14. Choi, E., Bahadori, M. T., Sun, J., Kulas, J. A., & Schuetz, A. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512).
15. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
16. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
17. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Navigating the landscape of model interpretability. arXiv preprint arXiv:1706.07979.
18. Choi, E., Bahadori, M. T., Sun, J., Kulas, J. A., & Schuetz, A. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512).
19. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
20. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
21. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Navigating the landscape of model interpretability. arXiv preprint arXiv:1706.07979.
22. Choi, E., Bahadori, M. T., Sun, J., Kulas, J. A., & Schuetz, A. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512).
23. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
24. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
25. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Navigating the landscape of model interpretability. arXiv preprint arXiv:1706.07979.
26. Choi, E., Bahadori, M. T., Sun, J., Kulas, J. A., & Schuetz, A. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512).
27. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
28. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
29. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Navigating the landscape of model interpretability. arXiv preprint arXiv:1706.07979.
30. Choi, E., Bahadori, M. T., Sun, J., Kulas, J. A., & Schuetz, A. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512).
31. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
32. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. Journal of Machine Learning Research, 17(1), 301-318.
33. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Navigating the landscape of model interpretability. arXiv preprint arXiv:1706.07979.
34. Choi, E., Bahadori, M. T., Sun, J., Kulas, J. A., & Schuetz, A. (2016). RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism. In Advances in Neural Information Processing Systems (pp. 3504-3512).
35. Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association, 24(2), 361-370.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Alfseel Osamah Abdullh A
Biomedical engineering Eastern health cluster Al-dammam, Saudi Arabia.
Alshehri, Yehia Abdulrhman I
Dentist, Third health cluster- Nafi General hospital, Saudi Arabia.
Hamdi, Dhafer Hussain B
Radiology technologist, Samtah General Hospital, Saudi Arabia.
Aminah Abdullah H Alsultan
Nurse Maternity and children hospital, Saudi Arabia.
Sarah Naif Aldawish
Family medicine resident Al-Oraijaa Al-Qadima primary health care, Saudi Arabia.
Samar Obeaudallha Al_Beshri
Nurse specialist Alaziziyah Children Hospital, Saudi Arabia.
Abdullah Hussain D Koshak
MOH AL HAJJ STREET PHC DENTIST JEDDAH, Saudi Arabia.
Ayat Suliman Jassaim Alhassan
Nurse Omran General Hospital, Saudi Arabia.
Zamil, Abdulrhman Ahmed H
Senior registrar Family medicine Alhussainiah PHC , Saudi Arabia.
Ahmed Mohammed Alsahli
Resident physician Health insurance King salman bin Abdulaziz medical city in Madinah, Saudi Arabia.
Manal Taher S Alnofaely
Nurse Maternity and children hospital , Saudi Arabia.
Mariam Ali Almajhad
Nurse Maternity and children hospital , Saudi Arabia.
Ayat Hussain Alhamad
Nurse Maternity and children hospital , Saudi Arabia.
SHARAHILI, NAIF AHMED M
Radiology technologist, AHAD ALMSARAHA GENERAL HOPITAL, Saudi Arabia.