NSF Workshop on Algorithm-Hardware Co-design for Medical Applications

Aim

Medical applications play a pivotal role in healthcare, bridging the gap between in-depth academic research and everyday clinical practice. Their purpose is dual-faceted: they aim to enhance health at an individual level while also striving to foster societal wellbeing. In the realm of medical applications, the role of electronic design automation (EDA) is crucial. EDA enables high accuracy in the development of algorithms, software, and hardware, ensuring that they not only function effectively but also meet vital safety requirements. Despite significant progress, there's a notable lack of comprehensive discussions on the co-design of algorithms and hardware in the medical field. This project will fund a workshop that assembles healthcare practitioners, academic researchers, industry experts, and government officials. The focus will be a collaborative dialogue on the needs, challenges, and solutions in the realm of algorithm/hardware co-design for medical applications. Additionally, the workshop aims to sketch out a roadmap for future initiatives. The workshop will be attended by a diverse group of participants, including individuals from underrepresented communities and women. A summary report of the workshop will be prepared and made publicly accessible to ensure widespread dissemination of the information discussed.

The main aim of this workshop is to strategize ways to overcome obstacles and accelerate advancements in both algorithmic and hardware design technologies for the development of computer-assisted medical applications. The workshop agenda includes discussions on a variety of topics. These include: 1. Grasping the life cycle of medical data, from its acquisition and processing, to sharing and management. 2. Comprehending the specific requirements of health practitioners for algorithms, systems, and hardware. 3. Evaluating the efficiency, robustness, privacy, explainability, and fairness of algorithm/hardware co-design in medical applications. 4. Envisioning the future of AI algorithm and hardware design, as well as design automation, within the context of medical applications. 5. Fostering collaboration among academia, industry, and government. 6. Ensuring the long-term sustainability of medical infrastructure and devices.