Three work packages focusing on the integration of clinical decision support in the clinic, hybrid AI for trustworthy explanations, and collecting and using patient-reported measures as input for AI models.
This project addresses the crucial societal challenge to offer high-quality personalized oncological care despite an increasing number of patients. This can be achieved by developing and integrating state-of-the-art AI technology for extracting statistical medical knowledge, developing clinical models, and offering personalized care with novel insights in explanation and justification of AI-generated advice in CDSSs. We aim to put Hybrid Intelligence challenges into practice. This involves achieving explainable Hybrid Intelligence by creating comprehensive explanations for the responses generated by the CDSSs. Additionally, we are committed to ensuring responsible Hybrid Intelligence by providing guarantees for the accuracy and safety of CDSSs responses.
In the context of this programme we will operationalize some of these long term challenges through the following work packages:
3.1 Integration in the clinic: Maastricht University; PIs Johan van Soest and Inigo Bermejo; Maastro Clinic; Andre Dekker; vacancies for two PhD candidates.
3.2 Hybrid AI for explainable and trustworthy systems; Vrije Universiteit; PIs Annette ten Teije and Frank van Harmelen; Postdoctoral researcher Syed Ihtesham Hussain Shah.
3.3 QoL preferences in treatment; Radboud University; PI Martijn Vastenburg; and TNO; PI Cor Veenman; joint PhD vacancy (industrial doctorate).