AI-Driven Decision Support Systems for Precision Medicine: Examining the Development and Implementation of AI-Driven Decision Support Systems in Precision Medicine

Authors

  • Mohan Raparthi Independent Researcher
  • Swaroop Reddy Gayam Independent Researcher and Senior Software Engineer at TJMax, USA
  • Bhavani Prasad Kasaraneni Independent Researcher, USA
  • Krishna Kanth Kondapaka Independent Researcher, CA ,USA
  • Sandeep Pushyamitra Pattyam Independent Researcher and Data Engineer, USA
  • Sudharshan Putha Independent Researcher and Senior Software Developer, USA
  • Siva Sarana Kuna Independent Researcher and Software Developer, USA
  • Venkata Siva Prakash Nimmagadda Independent Researcher, USA
  • Mohit Kumar Sahu Independent Researcher and Senior Software Engineer, CA, USA
  • Praveen Thuniki Independent Research, Sr Program Analyst, Georgia, USA

Keywords:

Precision medicine, AI-driven decision support systems, clinical decision-making, machine learning, genomics, data integration, patient outcomes, ethical considerations, future directions

Abstract

Artificial intelligence (AI)-driven decision support systems have revolutionized the field of precision medicine by providing clinicians with tools to personalize patient care. These systems leverage machine learning algorithms to analyze complex data sets, including genomics, imaging, and clinical data, to generate actionable insights. This paper examines the development and implementation of AI-driven decision support systems in precision medicine, highlighting their significance in clinical decision-making. We discuss key advancements in AI technologies, challenges in data integration and interpretation, and the impact of AI on improving patient outcomes. Additionally, we explore ethical considerations and future directions for AI-driven decision support systems in precision medicine.

References

Pargaonkar, Shravan. "A Review of Software Quality Models: A Comprehensive Analysis." Journal of Science & Technology 1.1 (2020): 40-53.

Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA. 2017 Nov 14;318(6):517-518. doi: 10.1001/jama.2017.7797.

Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.

Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019 Oct;16(10):391-403. doi: 10.1038/s41585-019-0226-2.

Pargaonkar, Shravan. "Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering." Journal of Science & Technology 1.1 (2020): 67-81.

Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.

Pargaonkar, S. (2020). A Review of Software Quality Models: A Comprehensive Analysis. Journal of Science & Technology, 1(1), 40-53.

Laï MC, Brian M, Mamzer MF, Peretti-Watel P, Cornu C, Aho-Glélé LS, Auquier P. Efficacy of a computerized system for drug dosage in patients with renal failure. BMC Nephrol. 2018 Jul 3;19(1):155. doi: 10.1186/s12882-018-0943-2.

Pargaonkar, S. (2020). Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering. Journal of Science & Technology, 1(1), 61-66.

Long E, Lin H, Liu Z, Wu X, Wang L, Jiang J, An Y, Xu J, Lin Z, Li X, Chen J. An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat Biomed Eng. 2017 May;1(5):1-11. doi: 10.1038/s41551-017-0072-3.

Pargaonkar, S. (2020). Future Directions and Concluding Remarks Navigating the Horizon of Software Quality Engineering. Journal of Science & Technology, 1(1), 67-81.

Park SH, Han K. Methodologic Guide for Evaluating Clinical Performance and Effect of Artificial Intelligence Technology for Medical Diagnosis and Prediction. Radiology. 2018 Jul;286(3):800-809. doi: 10.1148/radiol.2018173052.

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Published

12-04-2021

How to Cite

[1]
M. Raparthi, “AI-Driven Decision Support Systems for Precision Medicine: Examining the Development and Implementation of AI-Driven Decision Support Systems in Precision Medicine”, J. of Art. Int. Research, vol. 1, no. 1, pp. 11–20, Apr. 2021.

Issue

Vol. 1 No. 1 (2021): Journal of Artificial Intelligence Research

Section

Articles

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