Artificial Intelligence in US Healthcare System: Current Applications and Ethical Challenges

Ajay Ashok Jadhav

doi:10.69968/ijisem.2026v5i2160-168

Authors

Ajay Ashok Jadhav Department of Computer Science, Lamar University, Beaumont, Texas, USA

DOI:

https://doi.org/10.69968/ijisem.2026v5i2160-168

Keywords:

Artificial Intelligence, US Healthcare, Ethical Challenges, AI Applications, Explainable AI, AI Regulation

Abstract

Artificial intelligence (AI) is currently evolving the American healthcare system by way of its potential to improve diagnostic precision, expedite administrative procedures, and predictive analytics that can inform preventative measures for illness that could occur in the future. It has been seen that applications of AI in the form of applications and systems such as AI robotics surgery, deep learning algorithms, and precision medicine are making the patients outcomes better and also hospital operations more streamlined. This trajectory leads to data-driven, patient-focused care due to their use in the computation of the risk assessment, early illness disclosure, and personalized treatment plans, which are increasingly starting to fall under the purview of the use of machine learning models. Yet, health complexification remains extreme, and embedding AI in health care still has a long way to go, a multidimensional problematic divergence due to the combination of ethical and legal obstacles, including algorithmic bias, data privacy issues, and opaque AI-driven decision-making. The difficulty that adoption is becoming on the state level, due to worries about the impact of AI on the workers who provide healthcare and worries about job displacement and doubt, and the need for explainable AI, makes adoption more difficult. The issue of safe and responsible use of AI technology is guaranteed by the regulatory environment, where FDA clearance of AI medical devices and changing ethical standards play the central role. For this, they require the kind of continuous regulatory improvements, multidisciplinary cooperation, and the steps in place in AI governance frameworks. There will be future research in explainable AI, AI bias reduction, and AI-driven interventions to help the public’s health benefit at an optimal level while respecting equity and guaranteeing accountability. This research investigates the current state of AI in U.S. healthcare, its ethics and regulatory implications, and its future potential in disruptive AI.

References

[1] Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731. https://doi.org/10.1038/s41551-018-0305-z

[2] 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

[3] Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

[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. https://doi.org/10.1056/NEJMp1606181

[5] Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., ... & Fuchs, T. J. (2019). Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nature Medicine, 25(8), 1301-1309. https://doi.org/10.1038/s41591-019-0508-1

[6] 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. https://doi.org/10.1038/nature21056

[7] Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. https://doi.org/10.1126/science.aax2342

[8] Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., & Summers, R. M. (2020). ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. IEEE Transactions on Medical Imaging, 36(2), 310-316. https://doi.org/10.1109/TMI.2017.2699723

[9] Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature Biomedical Engineering, 2(10), 719-731. https://doi.org/10.1038/s41551-018-0305-z

[10] Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., & Ng, A. Y. (2018). Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Medicine, 15(11), e1002686. https://doi.org/10.1371/journal.pmed.1002686 Link

[11] Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., ... & Tsai, S. (2019). End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 25(6), 954-961. https://doi.org/10.1038/s41591-019-0447-x

[12] Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. https://doi.org/10.1371/journal.pone.0174944

[13] Sheikhalishahi, S., Balaraman, V., Osmani, V., & Lavesson, N. (2020). Benchmarking machine learning models on multi-centre eICU critical care dataset. PLOS ONE, 15(7), e0235424. https://doi.org/10.1371/journal.pone.0235424

[14] Henry, K. E., Hager, D. N., Pronovost, P. J., & Saria, S. (2015). A targeted real-time early warning score (TREWS) for septic shock. Science Translational Medicine, 7(299), 299ra122. https://doi.org/10.1126/scitranslmed.aab3719

[15] 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. https://doi.org/10.1001/jama.2016.17216