Clinical Data Scientist & AI Lead· Casablanca
Data science and clinical AI, supporting medical decision-making.
I lead healthcare AI projects alongside medical teams, turning clinical data into decision-support tools that are reliable and easy to understand.
A major healthcare player in Morocco
Leading artificial intelligence projects in cardiology
Design and development
Modeling, validation and software integration
From software engineering to artificial intelligence
3 degrees: technology diploma, professional bachelor’s and master’s at INSEA
International experience and collaborations
France: Henri-Mondor University Hospital United States: LIU and Dassault Systèmes
An artificial intelligence engineer specializing in data science and AI for healthcare, I lead cardiology AI projects at Akdital Innov, in an international collaboration with Long Island University (LIU) and Dassault Systèmes. My responsibilities cover framing needs with medical teams, designing and developing AI solutions, and evaluating them and preparing their integration into clinical settings.
With a master’s degree in Information Systems and Intelligent Systems from the National Institute of Statistics and Applied Economics (INSEA), I have built a path combining software development, business intelligence, data science and research applied to healthcare. In particular, I took part in a collaborative research project bringing together Henri-Mondor University Hospital in Créteil, through the clinical epidemiology and aging research laboratory (CEPIA) of the Mondor Institute for Biomedical Research (IMRB), and the interventional neuropsychology laboratory (NPI) of the École normale supérieure in Paris. This experience deepened my skills in data science, biostatistics and the analysis of clinical and neuroimaging data, in a multidisciplinary research setting.
My mission
Putting data and AI to work for patients and care teams, with reliable, understandable tools that truly support clinical decisions.
From raw clinical data to reliable models: quality, harmonization, deep learning and explainability.
Projects grounded in the state of the art, with results evaluated methodically.
A constant link between clinicians, technical teams and decision-makers.
Higher School of Technology · Oujda
Gained
Higher School of Technology · Oujda
Gained
INSEA, National Institute of Statistics and Applied Economics · Rabat
Gained
All in one profile
Akdital Innov · Akdital Group Casablanca, Morocco
IMRB, Mondor Institute for Biomedical Research · Henri-Mondor University Hospital Créteil, France
Manahij Conseil Tangier, Morocco
A few pieces of feedback received through my collaborations, presentations and professional exchanges, reflecting different dimensions of my work and approach.
Diagnosis and prediction in cardiology
Developed at Akdital Innov, CardioGuard AI is a decision-support solution that brings artificial intelligence to diagnosis and prediction in cardiology, with results clinicians can understand.
CardioGuard AI appears in Akdital’s annual results presentation (March 2026).
View the CardioGuard page (opens in a new tab)IMRB · Henri-Mondor Hospital · 2023
Tools and techniques
Tools and techniques
Tools and techniques
Tools and techniques
In healthcare, the value of an artificial intelligence model goes beyond its predictive performance. It also rests on its reliability, its clinical relevance and its ability to inform medical decisions.
Define the need with clinicians: which problem, for which patients, for which decision.
Review the literature: clinical need, added value, existing evidence and real use cases.
Ensure data quality, harmonization, anonymization and interoperability.
Choose the right approach for the problem, then evaluate its performance carefully.
Make results understandable and build clinician feedback into every iteration.
Understanding clinicians’ language and turning their needs into technical requirements.
At ease in multidisciplinary teams.
Presenting projects and bringing leadership, technical teams and partners together around shared goals.
Exploring options, alternatives and fallbacks before moving forward.
Following a project end to end, from the first prototype to deployment preparation.
Recruiting an intern and supporting her day to day.
Tools: Python, PyTorch, scikit-learn, R, STATA, SQL, Databricks, Power BI, FastAPI, React, Docker, MLflow, Git, FreeSurfer, FHIR, DICOM, OMOP
DeepLearning.AI · Coursera
Medical AIDeepLearning.AI · Coursera
OpenClassrooms
BCG · customer churn prediction
PwC · data analysis
On the cardiology track, I helped coordinate the work between leadership, cardiologists, IT teams and partners, so we could move toward shared goals together.
I start from the field: understanding cardiologists’ needs, translating them into data and technical requirements, then validating choices with them.
In Paris/Créteil, a multidisciplinary research team; in Casablanca, clinicians, engineers and decision-makers. I adapt how I communicate to each of them, to connect their expertise.
I took part in recruiting an intern, from screening to interviews, then supported her day to day in her work.
“My vision is to help build medicine where data and artificial intelligence strengthen the quality of care, support healthcare professionals in their decisions and help improve care pathways and hospital organization, while keeping clinical judgment at the center.”
A question or a conversation about healthcare AI or research? Feel free to write to me.
wassima.mansssour@gmail.com