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

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publisher

Elsevier

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

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Link to publisher version (DOI)

10.1016/j.procs.2024.09.652