Data and Artificial Intelligence as Design Material in Digital Health: Methodological Challenges and Interdisciplinary Design Practices

The development of artificial intelligence and data analytics technologies has radically transformed the nature of digital product design, particularly in the field of Digital Health. In modern medical systems, data and machine learning algorithms increasingly act not merely as supporting tools but as full-fledged design materials that shape interaction logic, how information is interpreted, and user decision-making. The behavior of such systems is determined by probabilistic models trained on historical and streaming data, which fundamentally distinguishes them from traditional deterministic digital interfaces. However, integrating artificial intelligence into design processes is accompanied by significant methodological and practical challenges: high technological complexity, the immaterial nature of data as a design material, limited prototyping capabilities, and communication gaps between designers, data specialists, and clinical experts. In the context of Digital Health, these challenges are further intensified by requirements related to safety, responsibility, interpretability, and trust, as design errors may have direct clinical consequences. This article examines the concept of data and artificial intelligence as design materials based on an analysis of existing research in HCI, UX, data studies, and industry practices in AI system design. Special attention is given to the specificity of Digital Health as a high-risk domain. As a result, a structured methodological framework is proposed, describing the properties of data and AI as materials and enabling more sustainable interdisciplinary design practices for digital medical systems.