Exploring the Application of Classical and Intelligent Software Testing in Medicine: A Literature Review

Abstract

This literature review explores the vital role of both classic and intelligent software testing in ensuring the quality and safety of medical software. Classic approaches establish a solid foundation for testing and ensuring adherence to regulatory standards. On the other hand, intelligent testing methods, leveraging artificial intelligence, machine learning, and deep learning, offer valuable advantages such as automation, pattern recognition, and performance insights. However, these approaches also present challenges concerning data quality and potential bias. To optimize medical software testing, the review recommends a combined approach based on specific requirements and available resources. Ultimately, these testing approaches work towards improving the quality and safety of medical software, leading to enhanced patient outcomes and a more efficient healthcare system.