Technology & Management Review

Peer Reviewed Article

Vol. 6 (2021)

AI-Powered Diagnostics: Revolutionizing Medical Research and Patient Care

Submitted
2024 October 27
Published
2021-03-03

Abstract

This paper examines how AI-powered diagnostics have transformed medical research and patient care, focusing on diagnostic accuracy, clinical procedures, and individualized therapy. The main goal is to evaluate how AI technologies are incorporated into healthcare, uncover ethical and regulatory concerns, and provide policy solutions. The secondary data evaluation examines AI's diagnostic advantages and drawbacks using contemporary research and case examples. Significant results show that AI enhances diagnostic accuracy and workflow efficiency but also shows algorithmic bias, lack of transparency, standardization, and regulatory problems. These constraints highlight the need for inclusive datasets, transparent AI models, and rigorous validation. The study's policy implications underline the need for comprehensive governance structures to promote fair, safe, and successful AI implementation in healthcare. Policymakers should optimize AI-powered diagnostics' potential to enhance patient outcomes and advance precision medicine by tackling these obstacles.

References

  1. Addimulam, S., Mohammed, M. A., Karanam, R. K., Ying, D., Pydipalli, R., Patel, B., Shajahan, M. A., Dhameliya, N., & Natakam, V. M. (2020). Deep Learning-Enhanced Image Segmentation for Medical Diagnostics. Malaysian Journal of Medical and Biological Research, 7(2), 145-152. https://mjmbr.my/index.php/mjmbr/article/view/687
  2. Chang, Y., Park, H., Hyun-Jin, Y., Lee, S., Kwee-Yum, L. (2018). Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature. Scientific Reports (Nature Publisher Group), 8, 1-11. https://doi.org/10.1038/s41598-018-27214-6
  3. Guo, J., Li, B. (2018). The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity, 2(1), 174-181. https://doi.org/10.1089/heq.2018.0037
  4. He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X. (2019). The Practical Implementation of Artificial Intelligence Technologies in Medicine. Nature Medicine, 25(1), 30-36. https://doi.org/10.1038/s41591-018-0307-0
  5. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., Aerts, H. J. W. L. (2018). Artificial Intelligence in Radiology. Nature Reviews. Cancer, 18(8), 500-510. https://doi.org/10.1038/s41568-018-0016-5
  6. Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95
  7. Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663
  8. Lin, S. Y., Mahoney, M. R., Sinsky, C. A. (2019). Ten Ways Artificial Intelligence Will Transform Primary Care. Journal of General Internal Medicine, 34(8), 1626-1630. https://doi.org/10.1007/s11606-019-05035-1
  9. Minh, D., Carpenter, A. E. (2019). Leveraging Machine Vision in Cell-based Diagnostics to Do More With Less. Nature Materials, 18(5), 414-418. https://doi.org/10.1038/s41563-019-0339-y
  10. Mohammed, M. A., Mohammed, R., Pasam, P., & Addimulam, S. (2018). Robot-Assisted Quality Control in the United States Rubber Industry: Challenges and Opportunities. ABC Journal of Advanced Research, 7(2), 151-162. https://doi.org/10.18034/abcjar.v7i2.755
  11. Mohammed, R. & Pasam, P. (2020). Autonomous Drones for Advanced Surveillance and Security Applications in the USA. NEXG AI Review of America, 1(1), 32-53.
  12. Mohammed, R., Addimulam, S., Mohammed, M. A., Karanam, R. K., Maddula, S. S., Pasam, P., & Natakam, V. M. (2017). Optimizing Web Performance: Front End Development Strategies for the Aviation Sector. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 38-45. https://upright.pub/index.php/ijrstp/article/view/142
  13. Nizamuddin, M., Natakam, V. M., Sachani, D. K., Vennapusa, S. C. R., Addimulam, S., & Mullangi, K. (2019). The Paradox of Retail Automation: How Self-Checkout Convenience Contrasts with Loyalty to Human Cashiers. Asian Journal of Humanity, Art and Literature, 6(2), 219-232. https://doi.org/10.18034/ajhal.v6i2.751
  14. Nizamuddin, M., Natakam, V. N., Kothapalli, K. R. V., Raghunath Kashyap Karanam, R. K., Addimulam, S. (2020). AI in Marketing Analytics: Revolutionizing the Way Businesses Understand Consumers. NEXG AI Review of America, 1(1), 54-69.
  15. Rahman, K. (2017). Digital Platforms in Learning and Assessment: The Coming of Age of Artificial Intelligence in Medical Checkup. International Journal of Reciprocal Symmetry and Theoretical Physics, 4, 1-5. https://upright.pub/index.php/ijrstp/article/view/3
  16. Ranschaert, E. R., Morozov, S., Algra, P. R. (2019). Artificial Intelligence in Medical Imaging: Opportunities, Applications and Risks. Artificial Intelligence in Medical Imaging. https://doi.org/10.1007/978-3-319-94878-2
  17. Rodriguez, M., Mohammed, M. A., Mohammed, R., Pasam, P., Karanam, R. K., Vennapusa, S. C. R., & Boinapalli, N. R. (2019). Oracle EBS and Digital Transformation: Aligning Technology with Business Goals. Technology & Management Review, 4, 49-63. https://upright.pub/index.php/tmr/article/view/151
  18. Sanyal, P., Mukherjee, T., Barui, S., Das, A., Gangopadhyay, P. (2018). Artificial Intelligence in Cytopathology: A Neural Network to Identify Papillary Carcinoma on Thyroid Fine-needle Aspiration Cytology Smears. Journal of Pathology Informatics, 9(1), 43-43. https://doi.org/10.4103/jpi.jpi_43_18
  19. Shah, P., Kendall, F., Sean, K., Goosen, R., Hu, J. (2019). Artificial Intelligence and Machine Learning in Clinical Development: A Translational Perspective. NPJ Digital Medicine, 2(1). https://doi.org/10.1038/s41746-019-0148-3
  20. Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M. (2019). Translating Cancer Genomics into Precision Medicine with Artificial Intelligence: Applications, Challenges and Future Perspectives. Human Genetics, 138(2), 109-124. https://doi.org/10.1007/s00439-019-01970-5
  21. Ying, D., Kothapalli, K. R. V., Mohammed, M. A., Mohammed, R., & Pasam, P. (2018). Building Secure and Scalable Applications on Azure Cloud: Design Principles and Architectures. Technology & Management Review, 3, 63-76. https://upright.pub/index.php/tmr/article/view/149

Most read articles by the same author(s)