Abstract
Generative AI (GenAI) represents a significant advancement in artificial intelligence research, offering numerous benefits and opening new avenues for innovation across various domains. In healthcare, Generative AI has shown promise in applications such as drug discovery, personalized medicine, and medical imaging. This paper examines the role of Generative AI in rule-based systems, where vulnerabilities are detected with the help of formal logic. In this context, the ruleset is generated and tested to evaluate the performance of rule-based systems with the aid of GenAI. The effectiveness of the GenAI tool was evaluated using a publicly available case study from a laboratory setting. The results show that using generative artificial intelligence in rule-based systems leads to increased creativity, continuous learning, and robust performance. GenAI responded to each use case and provided the desired results compared to traditional rule-based systems. This integration of advanced AI techniques with traditional rule-based systems ensures that these hybrid systems perform reliably and effectively.
RAS ID
76513
Document Type
Conference Proceeding
Date of Publication
1-1-2024
Volume
246
Issue
C
School
School of Science
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Publisher
Elsevier
Identifier
Kulsoom S. Bughio: https://orcid.org/0000-0003-4046-9578
David M. Cook: https://orcid.org/0000-0002-2264-8719
Syed Afaq A. Shah: https://orcid.org/0000-0003-2181-8445
Comments
Bughio, K. S., Cook, D. M., & Shah, S. A. A. (2024). GenAI in rule-based systems for IoMT security: Testing and evaluation. Procedia Computer Science, 246, 5330-5339. https://doi.org/10.1016/j.procs.2024.09.652