Dagstuhl-Seminar — From Research to Certification with Data-Driven Medical Decision Support Systems

Artificial intelligence has made tremendous strides across many spheres of life, however deploying this technology in safety critical domains remains challenging. This seminar focuses on clinical practice where data-driven models can streamline the work of healthcare professionals and democratise access to personalised medicine, thus have lasting positive impact on society, but also where deploying such tools without adequate foresight and safeguards can be perilous. This duality – anticipated benefits that may come along with unintended consequences – requires new technologies to be thoroughly vetted, e.g., with clinical trials and medical certification processes, before they can be deployed to avoid any harmful fallout. However, fulfilling such regulatory requirements is a lengthy and complex process plagued with many challenges, hence while prototype systems are becoming increasingly ubiquitous, they often remain indefinitely designated as research tools that can be used exclusively for research purposes. Their lacklustre adoption is compounded by pervasive reproducibility issues; history of unsafe systems being deployed prematurely; scarce data that are inherently private, difficult to collect or share, and often riddled with numerous biases; and prevalence of automation promises that never come to fruition. Such hurdles result in healthcare remaining one of the least digitised spheres of life.