Chairs | AI for Integrative Computational Medicine

Biophysics-based AI

  • Learning biophysical parameters for quantitative diagnosis 
  • Predicting evolution of pathologies & effect of therapies (digital twin) 
  • Data augmentation from biophysical simulation

Medical data management 

  • Medical Data Lab (UCA Idex) 
  • Health Data Hub, EDS APHP 
  • International databases 
  • Security, privacy, GDPR 
  • In collaboration with other 3IA institutes 

Data-driven AI 

  • Imaging and omics biomarkers (genetics, transcriptomics, proteomics, metabolomics) & lifestyle, behavior, etc. for patient selection 
  • Video analytics & sensors for patient monitoring 

3IA International Chair 


Sebastien Ourselin (King's College London) 

Sebastien Ourselin is Head of School of Biomedical Engineering & Imaging Sciences, Professor at King’s College London and Director of the EPSRC Image-Guided Therapies UK Network+. He published more than 400 articles (h-index: 64). He is associate editor for IEEE Tr. on Med Imaging,J of Med Imaging, Nature Scient. Reports, and Medical Image Analysis.

He works on the research topic AI for Healthcare & Image-guided Interventions.

3IA Chair holders


Nicholas Ayache (Inria)

AI for e-patients and e-medicine 

We are designing and exploiting modern AI methods: (i) to personalize the parameters of advanced models of the e-patient, (ii) to drive e-medicine algorithms on personalized e-patients (i.e. digital twins) for automated diagnosis, prognosis and therapy, in an efficient, robust, safe and explainable manner. We also seek to augment databases with biophysical simulation. 

François Bremond (Inria)

Video analytics for human behavior understanding 

Video analytics enables us to measure objectively the behavior of humans by recognizing their everyday activities, their emotion, eating habits and lifestyle. Human behavior can be modeled by learning from a large quantity of data from a variety of sensors to improve and optimize, for instance, the quality of life of people suffering from behavior disorders. 

Hervé Delingette (Inria)

Joint biological and imaging biomarkers in oncology 

We exploit joint information from imaging and biological data to improve the diagnosis and treatment planning, focusing on lung cancer. This approach relies on methods involving unsupervised deep learning, uncertainty quantification, sparse Bayesian feature selection and the handling of confounding factors.

Olivier Humbert (Université Côte d'Azur)

Comprehensive omics profiling for precision medicine in Oncology 

I am combining various patient extracted “omics” data, including multimodal imaging features, for integrative and data-driven computational medicine. I focus on challenging fields in oncology such as (i) radiogenomics and outcome-focused research in metastatic breast cancer and (ii) the accurate prediction of response to immunotherapy. 

Jean-Pierre Merlet (Inria) - Emeritus

Non-invasive assessment of disabilities 

We use mathematical/AI methods for (i) designing non-intrusive and affordable monitoring/assistance devices that are adaptable to the user’s/doctor’s needs, (ii) deducing medically pertinent health-indicators from the data, taking into account measurement errors, and (iii) detecting rare events that may be the sign of emerging pathology.

Maxime Sermesant (Inria)

AI and biophysical models for computational cardiology

The application of AI in healthcare is challenging due to its lack of robustness and explainability. This project aims to introduce physiological priors in AI through biophysical models. This can be done by reformulating problems through such models, by learning spatiotemporal dynamics from biophysics or by augmenting features and data with such simulations.


Juliette Raffort-Lareyre (CHU Nice - Université Côte d'Azur)

Applications of AI for patients with vascular diseases

Our team develops AI-derived applications for patients with vascular diseases including aortic aneurysm. We aim to create aid-decision support systems to enhance evidence-based decision and precision medicine through a translational approach including: identification of biomarkers, automatization of vascular imaging analysis, development of predictive scores using machine learning.

Vicente Zarzoso (Université Côte d'Azur)

IAblation: Artificial Intelligence for patient-centered atrial fibrillation ablation 

Atrial fibrillation is the most common sustained arrhythmia in clinical practice and remains the last great frontier of cardiac electrophysiology. The project aims to put forward new AI techniques to help cardiologists perform more effective patient-tailored catheter ablation procedures to treat this challenging cardiac condition.

Maria A. Zuluaga (EURECOM)

Learning-based Models in Medical Imaging: Closing the Gap towards Clinical Translation 

We aim to close the gap hindering the translation of AI systems into clinical practice. To this end, we develop novel AI tools for healthcare that 1) learn incrementally as data become available, 2) generalize across multi-modal data and 3) perform quality control of a model and can generate alerts when a decreased in performance is detected.