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Background and ObjectivesMachine Learning offers opportunities to improve patient outcomes, team performance, and reduce healthcare costs. Yet only a small fraction of all Machine Learning models for health care have been successfully integrated into the clinical space. There are no current guidelines for clinical model integration, leading to waste, unnecessary costs, patient harm, and decreases in efficiency when improperly implemented. Systems engineering is widely used in industry to achieve an integrated system of systems through an interprofessional collaborative approach to system design, development, and integration. We propose a framework based on systems engineering to guide the development and integration of Machine Learning models in healthcare.MethodsApplied systems engineering, software engineering and health care Machine Learning software development practices were reviewed and critically appraised to establish an understanding of limitations and challenges within these domains. Principles of systems engineering were used to develop solutions to address the identified problems. The framework was then harmonized with the Machine Learning software development process to create a systems engineering-based Machine Learning software development approach in the healthcare domain.ResultsWe present an integration framework for healthcare Artificial Intelligence that considers the entirety of this system of systems. Our proposed framework utilizes a combined software and integration engineering approach and consists of four phases: (1) Inception, (2) Preparation, (3) Development, and (4) Integration. During each phase, we present specific elements for consideration in each of the three domains of integration: The Human, The Technical System, and The Environment. There are also elements that are considered in the interactions between these domains.ConclusionClinical models are technical systems that need to be integrated into the existing system of systems in health care. A systems engineering approach to integration ensures appropriate elements are considered at each stage of model design to facilitate model integration. Our proposed framework is based on principles of systems engineering and can serve as a guide for model development, increasing the likelihood of successful Machine Learning translation and integration.

Anomalous aortic origin of a coronary artery (AAOCA) is reported as the second leading cause of sudden cardiac death in otherwise healthy young individuals. Several surgical studies have reported a shallow operative risk, describing repair as safe and effective with short or medium-term follow-up. However, surgical repair can also be associated with a high risk of complications. Numerous repair techniques have been described in the literature, but each technique's indications and limitations are often not well-understood or understated. Since explicit technical knowledge of the most appropriate surgical technique is highly desirable, we sought to thoroughly and clearly outline the safeguards and pitfalls of the most common surgical techniques used to repair AAOCA.