AIM-SAFE Kick-off
Our lab recently participated in the kick-off meeting of AIM-SAFE, a European-funded project dedicated to reducing medication-related harm and supporting clinicians through a modular, privacy-preserving AI Safety Co-Pilot.
With medication errors contributing to an estimated 200,000 annual deaths in Europe, AIM-SAFE addresses a critical healthcare challenge. The project leverages a multi-agent architecture powered by lightweight Small Language Models to provide traceable, evidence-based clinical recommendations. To protect sensitive patient data, the system utilizes a federated learning framework, ensuring information remains securely within hospital infrastructures.
The kick-off meeting focused on project organization, short-term objectives, and initial technical specifications. A major highlight was a series of dedicated, collaborative workshops:
- Work Package Workshops: Partners aligned on early operational steps, analyzing data requirements, formatting, potential modeling approaches, and interpretability.
- Ethics Workshop: All partners participated in a joint session focused on "embedded ethics", establishing a framework to integrate ethical considerations into the AI models from the very beginning of development.
Our lab is taking on leadership roles within the project's technical and clinical tracks:
- Predictive Modeling Leadership (WP4): We lead Work Package 4, focusing on personalized predictive modeling for prescribers and hospital pharmacists. Our team is designing core models that analyze heterogeneous clinical data to anticipate patient-specific risks and support personalized medication management.
- Clinical Communication (T6.2): We lead Task 6.2 to develop a dynamic deprescribing and communication agent. This agent will generate clear, natural-language explanations and evidence-backed recommendations to facilitate shared decision-making between clinicians and patients.
Our efforts are central to advancing three primary clinical use cases within the project:
- High-Risk Medication Identification: Automatically flagging medications suitable for tapering or discontinuation based on patient comorbidities, frailty, and clinical profiles.
- Safe Medication Prioritization: Assessing individual risks and benefits to support safe, evidence-based deprescribing decisions.
- Phenoconversion Risk Prediction: Integrating multi-drug interactions, patient comorbidities, lifestyle factors, pharmacogenomics, and tumor characteristics to anticipate adverse drug reactions.
By reducing the cognitive burden on healthcare professionals and improving prescribing safety, particularly for older adults and vulnerable populations, our team is proud to contribute to AIM-SAFE’s vital mission