Validating Machine Learning Outputs in Medical Software through Scalable Test Case Generation and Evaluation

Nabil Sawsan; Jamal Fayez

Authors

Nabil Sawsan AI Data Specialist, Jordan. Author
Jamal Fayez Automation Engineer, Jordan. Author

Keywords:

Machine learning validation, medical software testing, automated test generation, model reliability, clinical AI evaluation, scalable software QA, algorithmic safety

Abstract

With the growing integration of machine learning (ML) into medical software, ensuring the accuracy, reliability, and safety of algorithmic outputs has become a critical concern. This study presents a scalable framework for test case generation and evaluation to validate ML outputs in clinical decision-support systems. By automating scenario construction and systematically analyzing model predictions, the proposed methodology enhances software robustness without manual overhead. We demonstrate this approach using synthetic and real-world datasets, achieving high coverage of edge cases and critical patient risk profiles. The results support scalable validation as essential to regulatory compliance and clinical trust.

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