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.