A Systematic Review of AI-Powered Software Testing in Healthcare: Methodologies, Challenges, and Future Directions

Abstract

AI technology brought into the field of healthcare is a matter of significant importance as it has contributed to a qualitative improvement of patient care, diagnostics as well as treatment planning. The integrity of the AI-driven healthcare applications including the accuracy, reliability, and safety aspects is what the whole game is about. Even a small matter of software bugs can have severe consequences such as a wrong diagnosis being made, the discharging of patients with the wrong medicines, or a data breach. The most common traditional testing techniques, such as manual testing and rule-based automation, are quite often unsatisfactory as they lack the proper adaptability level that is necessary to cope with the ever-increasing complexity of the newest AI-based healthcare applications. The deployment of AI in software testing has turned out to be an effective method to solve these challenges of ensuring the machine learning algorithm has proper test coverage, defect detection automation, and finally, the healthcare software systems more robust. Automated functional testing, performance testing, security testing, and usability testing are the AI-powered testing methodologies that are the gateway to the development of reliable software. Problematic topics are emphasized in this research such as AI-powered software testing methodologies and their impact on healthcare applications, and the challenges addressing widespread adoption. Forward-thinking is also addressed surrounding the creation of explainable AI (XAI) in testing, continuous integration with DevOps, and AI-powered real-time validation frameworks to ensure the reliability and security of AI-driven healthcare systems.

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