Keywords
Clinical Outcomes
How to Cite
Abstract
In the era of precision medicine, there is a growing need for clinical decision support systems (CDSS) that leverage artificial intelligence (AI) to provide personalized recommendations to healthcare providers. AI-powered CDSS can analyze large datasets, including patient medical records, genetic information, and relevant research, to generate insights that aid clinicians in making more informed decisions. This paper explores the development and implementation of AI-powered CDSS for precision medicine, focusing on its potential to enhance clinical outcomes, reduce healthcare costs, and improve patient satisfaction. We discuss key technologies, challenges, and future directions in this rapidly evolving field.
References
Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
N. Pushadapu, “AI-Driven Solutions for Seamless Integration of FHIR in Healthcare Systems: Techniques, Tools, and Best Practices ”, Journal of AI in Healthcare and Medicine, vol. 3, no. 1, pp. 234–277, Jun. 2023
Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.