This study aims to develop a medical “dry laboratory” model that treats healthcare data as the primary research subject and employs computational science and AI as core methodologies. Based on the practice of a large tertiary hospital, we present a construction framework featuring a full-cycle model spanning data cultivation, governance, value extraction, and ecological feedback—transforming medical data from a static resource into a living asset. Innovatively, the “dry laboratory” was integrated into the residency training rotation to systematically cultivate data literacy. Practice has shown that the framework establishes the “dry laboratory” as a digital research entity, creating a closed-loop system that integrates clinical practice with data analytics, significantly enhancing the usability of multimodal data and enabling scalable training in data competence. In conclusion, the medical “dry laboratory” represents a new digital research paradigm distinct from traditional wet laboratories. Its implementation provides a reference framework and introduces an integrated “dry-wet” research model combining wet-lab experiments with data cultivation, demonstrating significant value for enhancing medical innovation, cultivating interdisciplinary talent, and advancing smart healthcare.

Manvydas Mikulėnas, a PhD student at Kaunas University of Technology (KTU), supervised by Professor Lina Šeduikytė, is investigating whether digital building models contain enough data to assess whole-life carbon footprints and circularity – the potential to keep materials and components in use. As part of the research, the team developed a prototype that identifies gaps in model data. Missing information already makes climate impact calculations more difficult and could make it harder to reuse building components in the future.

POSTECH-KAIST Team Demonstrates 4-Inch Wafer-Scale κ-Phase In2Se3 Transistors Fabricated at 250 °C