A Comparative Study of Software Quality Assurance Approaches in AI-Enabled Electronic Health Record Systems
Keywords:
Electronic Health Records, Artificial Intelligence, Software Quality Assurance, Agile Testing, Medical Informatics, Health IT, Risk Management, AI Validation, Clinical Decision SupportAbstract
The integration of Artificial Intelligence (AI) into Electronic Health Record (EHR) systems has revolutionized healthcare delivery, enhancing diagnostics, patient management, and administrative efficiency. However, the rapid expansion of AI in health informatics introduces unique challenges in ensuring software quality. This paper presents a comparative study of prevailing Software Quality Assurance (SQA) methodologies as applied to AI-enabled EHR systems. We analyze traditional, agile, and AI-specific quality assurance models, identifying gaps in adaptability, testing strategies, and regulatory compliance. Through a synthesis of recent literature and analytical visualizations, we recommend a hybrid, risk-driven SQA model tailored for AI health informatics. This study contributes to the understanding of how SQA methods must evolve to meet the safety, reliability, and ethical imperatives in AI-based medical software.
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