Section on Data-driven Clinical Decision Processes

Changes and transformations enabled by complex data-driven research have multiple direct effects on Translational Medicine. First, superior precision anchored to multimodal data is expected to boost individualized medicine developments and accelerate scientific discovery through innovation in diagnosis, therapy, disease management etc. Second, the paradigm of relying on a well-conceived scientific method is now an adaptive one, due to the data diversity potentially reflecting more features through which a disease manifests or progresses. Third, artificial intelligence and machine learning supporting the integration of sources and types of information that underlie the scientific research, now characterize a wide spectrum of patient-focused health solutions with augmented but controllable complexity.

Newly established clinical decision support systems (CDSS) increasingly operate through efficient algorithms defining superior inferential tools for clinical use. Additionally, new protocols for sharing digital information and effectively cross-validating patients' data rely on CDSS for suitable data harmonization and ultimately improved diagnosis, therapy assessment and prevention.

This section welcomes multidisciplinary research, and aims to promote scientific interactions and accelerate the establishment of CDSS in clinical practice.