Vol. 4 No. 1 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

AI-Enabled Clinical Decision Support Systems for Evidence-Based Medicine

Emma Johnson
Professor of AI and Healthcare, Midtown University, New York, USA
Cover

Published 16-04-2024

Keywords

  • AI,
  • clinical decision support systems,
  • evidence-based medicine,
  • healthcare,
  • artificial intelligence

How to Cite

[1]
Emma Johnson, “AI-Enabled Clinical Decision Support Systems for Evidence-Based Medicine”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 1, pp. 28–37, Apr. 2024, Accessed: Nov. 07, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/4

Abstract

AI-Enabled Clinical Decision Support Systems (CDSS) have emerged as powerful tools to assist healthcare providers in making evidence-based medical decisions. These systems leverage artificial intelligence (AI) algorithms to analyze complex clinical data and provide recommendations that align with the best available evidence. This paper presents a comprehensive review of AI-enabled CDSS in the context of evidence-based medicine (EBM). We discuss the role of AI in improving clinical decision-making, the challenges and opportunities in developing these systems, and the potential impact on healthcare delivery. Additionally, we highlight key considerations for the successful implementation of AI-enabled CDSS in clinical practice.

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References

  1. Buddha, Govind Prasad, and Rahul Pulimamidi. "The Future Of Healthcare: Artificial Intelligence's Role In Smart Hospitals And Wearable Health Devices." Tuijin Jishu/Journal of Propulsion Technology 44.5 (2023): 2498-2504.
  2. Kolay, Srikanta, Kumar Sankar Ray, and Abhoy Chand Mondal. "K+ means: An enhancement over k-means clustering algorithm." arXiv preprint arXiv:1706.02949 (2017).
  3. Tatineni, Sumanth. "Applying DevOps Practices for Quality and Reliability Improvement in Cloud-Based Systems." Technix international journal for engineering research (TIJER)10.11 (2023): 374-380.
  4. Dey, Sudipto, et al. "Methods and systems for selecting a machine learning algorithm." U.S. Patent Application No. 18/514,181.
  5. Pillai, Aravind Sasidharan. "Multi-label chest X-ray classification via deep learning." arXiv preprint arXiv:2211.14929 (2022).
  6. Dutta, Ashit Kumar, et al. "Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits." Multimedia Tools and Applications (2024): 1-23.
  7. Raparthi, Mohan, Sarath Babu Dodda, and Srihari Maruthi. "AI-Enhanced Imaging Analytics for Precision Diagnostics in Cardiovascular Health." European Economic Letters (EEL) 11.1 (2021).
  8. Pargaonkar, Shravan. "Bridging the Gap: Methodological Insights from Cognitive Science for Enhanced Requirement Gathering." Journal of Science & Technology 1.1 (2020): 61-66.
  9. Venigandla, Kamala, and Venkata Manoj Tatikonda. "Improving Diagnostic Imaging Analysis with RPA and Deep Learning Technologies." Power System Technology 45.4 (2021).
  10. Reddy, Surendranadha Reddy Byrapu. "Ethical Considerations in AI and Data Science-Addressing Bias, Privacy, and Fairness." Australian Journal of Machine Learning Research & Applications 2.1 (2022): 1-12.
  11. Raparthi, Mohan, et al. "Advancements in Natural Language Processing-A Comprehensive Review of AI Techniques." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 1-10.