Authors :
Gopalakrishnan Arjunan
Volume/Issue :
Volume 9 - 2024, Issue 11 - November
Google Scholar :
https://tinyurl.com/2s4h666w
Scribd :
https://tinyurl.com/2xsxj45r
DOI :
https://doi.org/10.5281/zenodo.14287069
Abstract :
The application of AI in healthcare
encompasses and invokes a gamut of fields and meanings,
including the following: diagnostics, to provide earlier
detection of disease and predictive analytics; and
personalized medicine, or tailor-made therapies
according to genetic and health information. AI is
analyzed deeply in relation to its role in medical imaging,
treatment planning, operational efficiency, disease
management, and drug discovery, highlighting its
potential to improve healthcare outcomes and optimize
resources. Challenges it raises include bias, data privacy,
and transparency; suggested solutions outline the
possibility of responsible deployment of AI systems. In
conclusion, the report outlines the future trends along
with AI, establishing AI as an enhancer of precision
medicine and preparedness for global health. This work
underlines a call to explainable, accountable AI that will
unleash its maximum potential in healthcare innovation.
Keywords :
Artificial Intelligence in Healthcare, AI Applications, and Medical Imaging.
References :
- Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). 'It's reducing a human being to a percentage': Perceptions of justice in algorithmic decisions. ACM CHI.
- Carrell, D., et al. (2023). Exploring EHR integration with explainable AI tools. Journal of Health Informatics.
- Caruana, R., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1721-1730. https://doi.org/10.1145/2783258.2788613
- Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine — Beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507–2509. https://doi.org/10.1056/NEJMp1702071
- Chen, M., Hao, Y., & Li, Y. (2019). Machine learning and medical healthcare: A review. IEEE Access, 7, 44374-44391. https://doi.org/10.1109/ACCESS.2019.2901315
- Esteva, A., Kuprel, B., & Novoa, R. A. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118. https://doi.org/10.1038/nature21056
- Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
- Ghassemi, M., et al. (2020). Bias and transparency in medical AI: Opportunities for explainability. Nature Medicine.
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
- Holzinger, A., Carrington, A., & Müller, H. (2020). Measuring the quality of explanations: The system causability scale (SCS). KI-Künstliche Intelligenz, 34(2), 193–198. https://doi.org/10.1007/s13218-020-00636-z
- Holzinger, A., et al. (2020). From machine learning to explainable AI: Towards transparent and interpretable systems. Information Systems Frontiers.
- Holzinger, A., Langs, G., & Denk, H. (2017). Explainable AI: The new frontier in medical applications. BMC Medical Informatics and Decision Making, 17(1), 1-13. https://doi.org/10.1186/s12911-017-0475-0
- Jiang, F., Jiang, Y., & Zhi, H. (2017). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 54, 1-11. https://doi.org/10.1016/j.semcancer.2017.07.004
- Jiang, F., Jiang, Y., & Zhi, H. (2017). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 54, 1-11. https://doi.org/10.1016/j.semcancer.2017.07.004
- 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), 230–243. https://doi.org/10.1136/svn-2017-000101
- Kim, B., et al. (2021). Interpretable machine learning and its healthcare applications. Machine Learning for Healthcare.
- Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
- Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (pp. 4765–4774).
- Marcus, G., & Davis, E. (2019). Rebooting AI: Building artificial intelligence we can trust. Pantheon.
- Miller, T. (2019). Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence Journal.
- Obermeyer, Z., et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science.
- Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347–1358. https://doi.org/10.1056/NEJMra1814259
- Rajkomar, A., Oren, E., & Chen, K. (2018). Scalable and accurate deep learning for electronic health records. npj Digital Medicine, 1(1), 18. https://doi.org/10.1038/s41746-018-0029-1
- Ratti, E., et al. (2021). Ethics of XAI in healthcare: Challenges and frameworks. Journal of Medical Ethics.
- Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). https://doi.org/10.1145/2939672.2939778
- Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
- Tiwari, R. K., & Etienne, M. (2024). Artificial intelligence and healthcare: A journey through history. Life.
- Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Lancet, 394(10198), 92-100. https://doi.org/10.1016/S0140-6736(19)31251-7
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS) (pp. 5998–6008).
- Wang, F., Casalino, L. P., & Khullar, D. (2021). Can artificial intelligence improve health care delivery? JAMA, 325(5), 417-418. https://doi.org/10.1001/jama.2020.22912
- Wang, F., et al. (2023). Applications of SHAP and LIME in healthcare explainability. IEEE Transactions on Medical Imaging.
- Wang, F., Preininger, A., & Wang, X. (2021). AI in health: Solving the explainability problem with XAI. npj Digital Medicine, 4(1), 1–9. https://doi.org/10.1038/s41746-020-00329-0
- Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it’s time to make it fair. Nature, 559(7714), 324–326. https://doi.org/10.1038/d41586-018-05707-8
The application of AI in healthcare
encompasses and invokes a gamut of fields and meanings,
including the following: diagnostics, to provide earlier
detection of disease and predictive analytics; and
personalized medicine, or tailor-made therapies
according to genetic and health information. AI is
analyzed deeply in relation to its role in medical imaging,
treatment planning, operational efficiency, disease
management, and drug discovery, highlighting its
potential to improve healthcare outcomes and optimize
resources. Challenges it raises include bias, data privacy,
and transparency; suggested solutions outline the
possibility of responsible deployment of AI systems. In
conclusion, the report outlines the future trends along
with AI, establishing AI as an enhancer of precision
medicine and preparedness for global health. This work
underlines a call to explainable, accountable AI that will
unleash its maximum potential in healthcare innovation.
Keywords :
Artificial Intelligence in Healthcare, AI Applications, and Medical Imaging.