Abstract
AI and ML have flooded the healthcare industry with new technological approaches to affect patient experiences through smart approaches towards predictability, treatment, and diagnosis. The following paper focuses on exploring the effects caused by the implementation of artificial intelligence technologies in the sphere of healthcare. This research explores different case studies to prove that early diagnosis, treatment customization, and organizational effectiveness are all driven by AI. The paper is concerned with the approaches used in the implementation of artificial intelligence in the health sector, together with the consequent enhancement of patients’ experiences. This paper also looks at some of the issues of ethics and the future of artificial intelligence in the health sector while focusing on proactively securing the patient and the progress of the technology.
Keywords: AI in Healthcare, Machine Learning, Patient Outcomes, Predictive Analytics, Healthcare.
References
- Artificial intelligence enhances MRI scans, National Institutes of Health. Online. https://www.nih.gov/news-events/nih-research-matters/artificial-intelligence-enhances-mri-scans
- AI-based Patient Monitoring System, Online. https://www.researchgate.net/figure/AI-based-Patient-Monitoring-System_fig2_369299264
- AI in Medical Imaging, Analyticalscience Wiley, Online. https://analyticalscience.wiley.com/content/article-do/ai-medical-imaging
- Shortliffe, E. H., & Buchanan, B. G. (1975). A Model of Inexact Reasoning in Medicine. Mathematical Biosciences, 23(3-4), 351-379.
- Miller, R. A., Pople, H. E., & Myers, J. D. (1985). INTERNIST-I, an experimental computer-based diagnostic consultant for general internal medicine. New England Journal of Medicine, 307(8), 468-476.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning, Nature, 521(7553), 436-444. https://doi.org/10.1038/nature14539
- 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. https://doi.org/10.1093/bib/bbx044
- De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., ... & Keane, P. A. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350.
- Ferrucci, D. (2012). Introduction to “this is Watson”. IBM Journal of Research and Development, 56(3.4), 1-15. https://doi.org/10.1147/JRD.2012.2184356
- 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.
- 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.
- Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
- US Food and Drug Administration. (2020). Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. Online. https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-device
Corresponding Author
Mahesh Kambala
Elevance Health
DOI: https://dx.doi.org/10.18535/jmscr/v12i08.07
