The proliferation of electronic health records (EHR) has catalyzed a paradigm shift in healthcare, presenting an opportunity for leveraging artificial intelligence (AI) in medical data analysis. This talk introduces a novel benchmark in neural architecture search, expressly designed for optimizing AI models for edge deployment in EHR contexts. The benchmark synergizes multi-task learning with hardware-efficiency metrics, addressing the exigency of real-time, on-site decision-making in medical care. The discussion will elucidate the importance of applying HW-NAS to medical imaging, highlighting how it addresses the computational constraints and real-time processing requirements inherent in medical diagnostics. The creation process of this benchmark, which incorporates multi-task learning and hardware-efficiency metrics, will be detailed. Initial results demonstrating the benchmark’s impact in refining AI models for efficient and accurate medical image analysis will be presented, showcasing its potential to revolutionize healthcare diagnostics in resource-limited settings.