Artificial Intelligence (AI) enabled embedded devices are becoming increasingly important in the field of healthcare where such devices are utilized to assist physicians, clinicians, and surgeons in their diagnosis, therapy planning, and rehabilitation. However, it is still a challenging task to come up with an accurate and efficient machine learning model for resource-limited devices that work $24\times7$. It requires both intuition and experience. This dependence on human expertise and reliance on trial-and-error-based design methods create impediments to the standard processes of effort estimation, design phase planning, and generating service-level agreements for projects that involve AI-enabled MedTech devices.

In this paper, we present AutoML search from an algorithmic perspective, instead of a more prevalent optimization or black-box tool perspective. We briefly present and point to case studies that demonstrate the efficacy of the automation approach in terms of productivity improvements. We believe that our proposed method can make AutoML more amenable to the applications of software engineering principles and also accelerate biomedical device engineering, where there is a high dependence on skilled human resources.