Developing a Comprehensive Quality Assurance Lifecycle for Deep Learning-Based Medical Imaging Software

Hasina Persa

Authors

Hasina Persa Quality Assurance Engineer, Egypt Author

Keywords:

Deep learning, medical imaging, quality assurance, validation lifecycle, model robustness, healthcare AI, reproducibility

Abstract

Purpose
Deep learning (DL) has achieved remarkable success in medical imaging applications; however, persistent concerns regarding reliability, reproducibility, and generalizability limit its safe clinical adoption. The purpose of this study is to propose a comprehensive Quality Assurance (QA) framework specifically tailored to the lifecycle of DL-based medical imaging software, addressing both technical and clinical validation requirements.

Design/methodology/approach
This paper develops a lifecycle-oriented QA framework that systematically spans data curation, model development, validation, deployment, and post-market monitoring. The framework is informed by an extensive review of current literature, regulatory guidelines, and practical deployment experiences in clinical environments. Structured process flowcharts, decision pathways, and performance visualization strategies are incorporated to ensure traceability, transparency, and continuous monitoring across development stages.

Findings
The proposed framework identifies critical gaps in existing QA practices, particularly in dataset governance, model drift detection, and post-deployment auditing. By integrating standardized evaluation checkpoints and clinical relevance metrics throughout the DL lifecycle, the framework enhances model robustness, interpretability, and long-term reliability in real-world clinical settings.

Practical implications
The framework provides actionable guidance for researchers, developers, and regulatory stakeholders seeking to implement robust QA processes for DL-based medical imaging systems. It supports compliance with emerging regulatory expectations while facilitating safer deployment, ongoing performance assessment, and continuous improvement of AI-driven clinical tools.

Originality/value
This study contributes a holistic, lifecycle-driven QA methodology specifically designed for DL-based medical imaging software. Unlike existing approaches that focus on isolated development stages, the proposed framework emphasizes end-to-end quality management, post-deployment accountability, and clinical alignment, offering a practical and scalable model for trustworthy medical AI systems.

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