This paper evaluates the effectiveness of artificial intelligence (AI)-based collision detection methods on hash functions, which is of great importance due to the vulnerabilities of these functions in cryptographic applications, particularly in the security of healthcare data. The study systematically reviews existing hash function algorithms, analyzes their collision frequencies, and compares different AI techniques in terms of performance and detection accuracy. The main findings show that some AI methodologies significantly outperform traditional collision detection approaches, reducing collision rates by up to 30% and increasing the speed of detection processes without compromising data integrity. This achievement is particularly important in the healthcare sector, where strong encryption and data protection mechanisms are essential for protecting sensitive patient data and maintaining trust in digital healthcare systems. The results of this study go beyond theoretical contributions, demonstrating that integrating AI-based strategies into hash function optimization can strengthen the overall security framework of healthcare IT systems, thereby reducing the risks associated with data breaches and ensuring compliance with regulatory standards. Ultimately, this study paves the way for future research on the large-scale implementation of AI methodologies in cryptography and supports their application to strengthen the healthcare security landscape.
Ai-Based Collision Detection Methods In Hash Functions
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References
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