Artificial Intelligence (Al)

CARAMEL is a project funded by the European Commission under the Horizon Europe programme. Its aim is to develop a personalized prevention approach based on state-of-the-art artificial intelligence models for cardiovascular risk stratification in women aged 40-60 years. These models are trained using large amounts of multi-source data and complemented by digital self-assessment and self-management tools. The CARAMEL consortium brings together a diverse group of 25 organizations, including technology companies and research centers, clinical institutions, as well as experts in ethics, law, and communication. The CARAMEL-OS study is an observational study aimed at prospectively completing the proposed personalized cardiovascular risk stratification model for women aged 40-60 years that will be developed during the earlier phases of the overall CARAMEL project (https://www.caramel-project.eu/). The study has a purely observational design, and patient stratification will not entail any clinical intervention. The data collected will be used to complete the design of the personalized stratification model, as well as to develop and validate the performance of the predictive models in an independent prospective cohort, distinct from the retrospective cohort initially used. The performance of the models developed using retrospective data will be assessed in terms of accuracy, discrimination, calibration, and other relevant metrics. In addition, the study will enable the development of new predictive models with short-term prediction horizons, incorporating additional variables that are not available retrospectively. The study population will be recruited in four European Union countries: Croatia, Spain (Andalusia and The Basque Country - Gipuzkoa), Greece and Lithuania; and in Colombia. A prospective cohort of 3,000 women aged 40-60 will be recruited, with each partner contributing 500 participants.

This study is a multicenter, prospective, randomized controlled trial designed to evaluate the effectiveness and safety of a multimodal artificial intelligence (AI)-guided postoperative recovery management system in patients after lung cancer surgery. Eligible patients will be enrolled after surgery when their clinical condition is stable and will be randomly assigned to either an AI-guided recovery management group or a usual postoperative care group. Patients in the AI-guided group will receive usual postoperative care plus a multimodal AI-based recovery management system. The system will collect patient-reported symptoms, vital signs, physical activity, respiratory rehabilitation information, recovery-related data, and, when needed, wound or chest-related images or short videos. Based on these data, the system will provide recovery feedback, general nursing advice, respiratory rehabilitation reminders, activity guidance, and risk stratification alerts. For red-flag symptoms or high-risk conditions, the system will advise patients to contact the clinical team or seek medical care. Patients in the usual-care group will receive standard postoperative management after lung cancer surgery and will complete symptom assessments at the same prespecified time points, but they will not receive AI-generated individualized recovery feedback or AI-generated risk alerts. The primary outcome is the number of MDASI-LC-derived target symptom threshold events within 30 days after surgery. Target symptoms include pain, fatigue, disturbed sleep, shortness of breath, and cough. Secondary outcomes include overall target symptom burden, quality of recovery, time to recovery to a mild-symptom state, functional interference, respiratory rehabilitation adherence, physical activity adherence, unplanned healthcare utilization, pulmonary complications, and unplanned readmission.