AI for Mental Health
The AI in Mental Health group uses AI and large-scale data infrastructures to understand how lifelong environmental, lifestyle and social exposures (“the exposome”) shape mental and physical health. Based at the University of Barcelona, the team works at the interface of psychiatry, epidemiology, biology and data science, developing federated learning frameworks and harmonised multi-cohort datasets to study disease risk, prevention and trajectories across the life course.
Principal Investigators
Karim Lekadir
ICREA Researcher
Technologies & Methods
- Machine learning
- Deep learning
- Federated learning
- Predictive modelling
- Trustworthy AI
- Bias mitigation
- Fairness estimation algorithms
- Uncertainty estimation algorithms
Research Team
Esmeralda Ruiz Pujadas
Postdoctoral Researcher
Álvaro Passi-Solar
Postdoctoral Researcher
Laura Arbeláez
Postdoctoral Trainee
Jorge Fabila
Technician
Active Projects
HappyMums: Understanding, predicting and treating depression in pregnancy to improve mother’s and offspring’s mental health outcomes
European Commision. 101057390. Karim Lekadir.
Youth-GEMs: Gene and Environment interactions in Mental health trajectories of Youth
European Commision. 101057182. Karim Lekadir.
STAGE: Stay Healthy Through Ageing. An Integrated Life-Course Approach for Person-Centred Solutions and Care for Ageing with Multi-morbidity in the European Regions
European Commision. 101137146. Karim Lekadir.
YOUTHreach: Bridging Gaps in Mental Health Support. A Comprehensive European Strategy
European Commision. 101156514. Karim Lekadir.
Selected publications
- Lekadir, K., Frangi, A.F., Porras, A.R. et al. FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ 388, e081554 (2025). https://doi.org/10.1136/bmj-2024-081554
- Casamitjana, A., Sala-Llonch, R., Lekadir, K. et al. USLR: An open-source tool for unbiased and smooth longitudinal registration of brain MRI. Med Image Anal 105, 103662 (2025). https://doi.org/10.1016/j.media.2025.103662
- Pujadas, E.R., Díaz-Caneja, C.M., Stevanovic, D. et al.Longitudinal Prediction of Mental Health Outcomes in Vulnerable Youth using Machine Learning. Cogn Comput 17, 152 (2025). https://doi.org/10.1007/s12559-025-10509-y
- Dang, V.N., Cecil, C., Pariante, C.M. et al. Characterizing the role of early life factors in machine learning-based multimorbidity risk prediction. PLOS Digit Health 4, e0000982 (2025). https://doi.org/10.1371/journal.pdig.0000982
- Dang, V.N., Cascarano, A., Mulder, R.H. et al.Fairness and bias correction in machine learning for depression prediction across four study populations. Sci Rep 14, 7848 (2024). https://doi.org/10.1038/s41598-024-58427-7
- Mariani, N., Borsini, A., Cecil, C.A.M. et al. Identifying causative mechanisms linking early-life stress to psycho-cardio-metabolic multi-morbidity: The EarlyCause project. PLoS ONE 16, e0245475 (2021). https://doi.org/10.1371/journal.pone.0245475