Artificial intelligence-based intrusion detection for the internet of medical things: Practical insights and a...

Rapid developments in the Internet of Medical Things (IoMT) technology have revolutionized healthcare by enabling real-time patient monitoring and remote diagnosis. However, increased connectivity raises significant cybersecurity concerns, such as data breaches, unauthorized access, and potential tampering with medical devices. Techniques such as threat intelligence, log monitoring, and intrusion detection systems are used to identify and respond to cyberattacks on the IoMT. As attackers evolve their strategies, they are increasingly turning to artificial intelligence to improve attack detection accuracy. This shift highlights the urgent need for smart, scalable, and privacy-preserving IDS solutions that can protect life-critical medical systems with limited computational resources. This research investigates the use of artificial intelligence (AI), specifically machine learning (ML), deep learning (DL), and hybrid techniques, to improve intrusion detection systems (IDS) for IoMT security. It includes an overview of publicly available IoMT datasets, an analysis of existing IDS frameworks, and a discussion of the limitations of AI-powered security solutions. Unlike prior surveys that mainly focus on general IoT or isolated AI methods, this study introduces a novel IoMT-specific taxonomy that integrates threat models, device capabilities, and regulatory/ethical constraints, and proposes a unified evaluation framework that balances detection accuracy with latency, resource consumption, and privacy preservation. Furthermore, current IDS approaches do not fully address regulatory requirements, real-time constraints, and dataset heterogeneity, which remain major barriers to practical IoMT deployment. In addition, this study outlines key research challenges and highlights the importance of scalable, privacy-preserving, and resilient detection systems for securing IoMT ecosystems.