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Sithungu, S. P., & Ehlers, E. M. (2023). SupervisedimmuneNet: Training Artificial Immune Networks Using a Supervised Learning Approach for Improved Multi-Class Classification. In Proceedings of the 2023 6th International Conference on Computational Intelligence and Intelligent Systems (pp. 118-123). ACM.
https://doi.org/10.1145/3638209.3638227

has been cited by the following article:

• TITLE: Immune System Intelligence as a Blueprint for AI-Based Disease Detection

  AUTHORS: Cynthia Silvia, Karolina Kopczynski, Zoe Auezov

  KEYWORDS: Artificial Intelligence, Human Immune System, Disease Detection, Bio-Inspired Models, Pattern Recognition, Adaptive Learning, Anomaly Detection, Personalized Medicine, Human-Centered AI, Healthcare Innovation

  JOURNAL NAME: Creative Education, Vol.17 No.6, June 29, 2026

  ABSTRACT: The human immune system can be viewed as a conceptual model of pattern recognition, adaptive learning, and anomaly detection, providing a rich theoretical basis for the future development of AI in disease detection. As a framework, not for empirical evidence; instead, it sheds light on how principles of self/non-self discrimination, continuous surveillance, and adaptive responses to emerging threats contribute to the design of AI-based healthcare systems. In this conceptualization, AI is presented not simply as a tool but as an extension of biologically inspired intelligence—that is able to aid in better diagnosis, early detection, and personalized treatment. This perspective emphasizes AI systems’ potential to embody traits of adaptability, precision, and resilience through an analogy between biological immune processes and computational architectures. Thus, framing immune system intelligence as a conceptual perspective provides a more holistic view of the direction in which bio-inspired models will guide the development of effective, ethical, and human-centered strategies for disease detection in modern healthcare.