Bridging the Translational Gap: How Software Engineering Practices Enable the Deployment of Applied AI in Clinical Settings

Abstract

The potential of artificial intelligence (AI) in healthcare has been significant, with developments in clinical decision support, disease prediction, diagnostic imaging, and personalized medicine. However, the translation of AI systems from experimental settings to clinical use is still somewhat limited. Much of the literature thus far has focused on model-related successes, such as predictive power and algorithmic correctness, and has paid little attention to the software engineering skills required to make it reliable, safe, scalable, and clinically actionable. As a result, many high-performing models have failed to successfully be adopted in real-world healthcare settings because of issues with interoperability, integration, reproducibility, observability, regulatory compliance and clinician trust. The paper proposes that using applied Artificial Intelligence in the clinical environment is more than just a machine learning problem; it is a software engineering and systems integration problem. The study also employs a structured literature synthesis of research to pinpoint clinical AI implementation tools for building a successful clinical AI, such as data lineage and version control, continuous integration/continuous delivery for machine learning systems, containerization, monitoring and failure-mode analysis, human-in-the-loop monitoring, fairness auditing, and regulatory traceability. On this basis, a framework is proposed for the safe, scalable and trustworthy application of applied AI into clinical workflows, that is, a framework for deployment. The study also highlights the importance of incorporating regulatory and governance considerations into engineering practices to enhance readiness for deployment and institutional trust. To bridge the translational gap in the field of healthcare AI, the focus needs to shift from merely emphasizing the performance of individual algorithms to developing comprehensive, sustainable, and clinically relevant systems that can contribute to the practical implementation of care.