Automated Diagnostics in Medicine and Their Role in Reducing Human Error

Authors

  • Maxsudov Valijon Gafurjonovich
  • Arzikulov Fazliddin Faxriddin o‘g‘li Tashkent State Medical University

Keywords:

automated diagnostics, artificial intelligence, machine learning

Abstract

Automated diagnostic systems have become integral to modern medicine, offering significant improvements in the accuracy and efficiency of disease detection. By utilizing artificial intelligence, machine learning, and advanced data analysis techniques, these systems reduce the dependency on human judgment, which can be prone to errors caused by fatigue, bias, or varying levels of expertise. This paper explores the role of automated diagnostics in minimizing human error, enhancing diagnostic consistency, and supporting clinicians in making informed decisions. The benefits of automated tools, such as improved detection of subtle medical conditions and standardized evaluation protocols, are discussed alongside challenges related to integration, ethical considerations, and the necessity of maintaining human oversight. The study highlights the potential of automated diagnostics to improve patient safety and healthcare quality while emphasizing their complementary role in clinical practice.

References

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

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

Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., ... & Ng, A. Y. (2017). CheXNet:

Radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv preprint arXiv:1711.05225.

https://arxiv.org/abs/1711.05225

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

Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019). Key challenges

for delivering clinical impact with artificial intelligence. BMC Medicine, 17(1), 195.

https://doi.org/10.1186/s12916-019-1426-2

Beam, A. L., & Kohane, I. S. (2018). Big data and machine learning in health care. JAMA, 319(13),

–1318. https://doi.org/10.1001/jama.2017.18391

Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial

intelligence in healthcare: a multidisciplinary perspective. BMC Medical Informatics and Decision Making,

(1), 310. https://doi.org/10.1186/s12911-020-01334-2

Downloads

Published

2025-05-17

Issue

Section

Articles

How to Cite

Automated Diagnostics in Medicine and Their Role in Reducing Human Error. (2025). Texas Journal of Medical Science, 44, 9-12. https://zienjournals.com/index.php/tjms/article/view/6186