AI-Enhanced Blood Testing for Disease Detection and Monitoring


Authors : Soumya A.V; Archana Menon; Arathy Joshy; Devika Denson; Diya Santhosh

Volume/Issue : Volume 9 - 2024, Issue 12 - December

Google Scholar : https://tinyurl.com/msfkzfkf

Scribd : https://tinyurl.com/mt8nzk8r

DOI : https://doi.org/10.5281/zenodo.14557989

Abstract : This paper presents a survey of artificial intelligence (AI) applications in blood testing for disease detection and patient monitoring. It covers several machine learning (ML) models like deep neural networks (DNN), decision trees, and support vector machines (SVM) used to interpret blood test results. The integration of AI with routine blood tests promises to enhance the diagnostic accuracy, reduce costs, and also improve patient outcomes. This paper compares different AI approaches in this domain, discusses the challenges and limitations, and explores the future scope of AI in clinical settings.AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.

Keywords : AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.

References :

  1. Meiseles, A., et al. ”Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests.” Computers in Biology and Medicine, 2022.
  2. J1. Smith, J., & Johnson, A. ”AI in blood diagnostics: A review of applications and advancements.” Journal of Medical AI, 2021.
  3. Brown, C., et al. ”Machine learning applications in routine blood tests: A comprehensive review.” Clinical AI Journal, 2020.
  4. Turner, M., & Harris, B. ”Deep learning for blood disease detection: Challenges and opportunities.” Artificial Intelligence in Medicine, 2019.
  5. Clark, L., et al. ”Interpretable models for blood test analysis using SHAP.” Journal of Explainable AI, 2023
  6. Garcia, M. R., & Lee, A. ”Predicting disease outcomes using machine learning on blood biomarkers: A systematic review.” International Journal of Medical Informatics, 2021
  7. Lee, S., et al. ”AI-based hematological disease detection: A focus on leukemia using convolutional neural networks.” Journal of Biomedical Science and Engineering, 2020
  8. Zhao, Y., & Chen, K. ”Machine learning for sepsis prediction from blood tests in emergency departments.” Journal of Emergency Medical AI, 2021.
  9. Patel, R., & Kumar, N. ”The role of random forests in detecting anemia from routine blood tests.” Journal of Clinical Hematology, 2022
  10. Wang, X., et al. ”Blood test anomaly detection using support vector machines: A comparative study.” Computers in Healthcare, 2019
  11. Nguyen, T. D., & Miller, P. ”AI in blood diagnostics: From laboratory to bedside.” Artificial Intelligence in Healthcare, 2021.
  12. Xu, L., & Huang, J. ”Using XGBoost for early detection of chronic diseases based on blood test results.” Journal of Machine Learning in Medicine, 2022.
  13. Almeida, F., & Silva, R. ”Explainable AI in medical diagnostics: Case studies in blood test analysis.” AI and Health Informatics, 2020
  14. Kapoor, S., & Singh, M. ”The integration of artificial intelligence in routine blood tests for precision medicine.” Healthcare AI Innovations, 2023.
  15. Martin, P., & Wilson, J. ”Leveraging machine learning for cardiovascular risk prediction using routine blood tests.” Journal of Medical Data Science, 2022
  16. Anderson, C., et al. ”Interpreting AI predictions in blood diagnostics: A focus on SHAP values.” Journal of Explainable Healthcare AI, 2021
  17. Gupta, A., & Thomas, R. ”A comprehensive survey on deep learning applications in hematology.” Journal of AI in Medicine, 2020
  18. Fernandez, M., et al. ”AI-assisted blood diagnostics for early detection of infectious diseases.” Journal of Clinical AI, 2021.
  19. Kaur, P., & Mehta, D. ”Comparing decision trees and random forests in predicting blood-related disorders.” Journal of Healthcare Informatics Research, 2022.
  20. Liu, Y., & Zhang, W. ”AI-driven precision diagnostics: Applications of deep learning in routine blood tests.” Journal of Digital Medicine, 2020.

This paper presents a survey of artificial intelligence (AI) applications in blood testing for disease detection and patient monitoring. It covers several machine learning (ML) models like deep neural networks (DNN), decision trees, and support vector machines (SVM) used to interpret blood test results. The integration of AI with routine blood tests promises to enhance the diagnostic accuracy, reduce costs, and also improve patient outcomes. This paper compares different AI approaches in this domain, discusses the challenges and limitations, and explores the future scope of AI in clinical settings.AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.

Keywords : AI in Healthcare, Blood Test Analysis, Machine Learning, Disease Detection, Deep Learning, Clinical Diagnostics.

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