Thanks to visit codestin.com
Credit goes to github.com

Skip to content
View m-aljasem's full-sized avatar
👨‍⚕️
Just chillin
👨‍⚕️
Just chillin

Block or report m-aljasem

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Maximum 250 characters. Please don't include any personal information such as legal names or email addresses. Markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
m-aljasem/README.md

🩺 About Me

Hi there ! I'm Mohamad AlJasem , a physician, AI developer, and clinical researcher dedicated to creating technologies that transform healthcare.
My work sits at the crossroads of deep learning, digital health, and clinical insight — where algorithms meet empathy.

  • 🎓 MD + MPH + MSc from TUMS.
  • 🧠 Focus: AI in Cardiology, Epilepsy, & Ophthalmology
  • 🔬 Researching AI for corneal topography, CVD risk modeling, and Non-Communicable Diseases Prediction

“I don’t just code models — I build end-to-end systems that translate the science into real-world soultuoins”


⚙️ My Current Projects

🔬 Project 🧭 Description 🚀 Tech Stack
🫀 CVD-AI Deep neural network for cardiovascular risk prediction PyTorch, Scikit-learn, MONAI
👁 CorneaNet AI system for corneal topography scan interpretation TensorFlow, OpenCV, Streamlit
EpiAI Predictive analytics for seizure forecasting Python, FastAPI, LSTM
💊 SmartEHR Intelligent hospital workflow optimization system Flask, PostgreSQL, FHIR


🧠 Research Interests

  • Cardiovascular AI models for precision risk prediction
  • AI in ophthalmology — image-based diagnostics
  • Seizure prediction and digital biomarkers in epilepsy
  • EHR mining, hospital automation, and data-driven clinical decisions

🧬 My long-term vision: to build trustworthy AI systems that augment the intuition of clinicians — not replace it.


🌍 Vision

“Medicine is data.
AI is understanding.
Together, they form the future of care.”

I believe in a healthcare future where AI is not a tool, but a colleague — one that listens, learns, and helps save lives.


📈 Highlights

  • 🏅 Research in cardiology AI & digital transformation of healthcare
  • 🧩 Building open-source AI pipelines for medical research
  • 💡 Speaker: Digital Transformation in Hospitals
  • 🌐 Global collaborations in clinical informatics

🧰 Tech Arsenal

Category Tools
🤖 AI / ML PyTorch, TensorFlow, Keras, scikit-learn
🧠 Data Science Pandas, NumPy, Matplotlib, DICOM, SQL
🧬 Clinical Data FHIR, HL7, EHR, MONAI, Pydicom
💻 Dev / Infra Python, FastAPI, Docker, GitHub Actions, Linux

💫 GitHub Activity


🧭 Philosophy

“AI should not aim to replace the physician’s judgment —
it should help reveal patterns that no human could see.”


🌐 Connect with Me


Fun Fact

When I’m not coding or reading clinical data,
you’ll find me designing hospital systems, exploring medical imaging datasets —
or imagining what the AI-powered hospital of 2030 will look like.


Typing SVG


Pinned Loading

  1. FireLMS FireLMS Public

    a light-weight learning management based on Angular and Firebase

    HTML 5

  2. OpenMedicalData OpenMedicalData Public

    A repository of open medical data that can be accessed freely.

    TypeScript 2

  3. PyCVDRisk PyCVDRisk Public

    a small python Library for calculating cardiovascular diseases risk using different clinically validated algorithms

    Python 1

  4. AutoGBD AutoGBD Public

    AutoGBD is an intelligent, open-source framework for harmonizing health data to Global Burden of Disease (GBD) standards. It transforms chaotic raw health data into structured, analysis-ready forma…

    Python

  5. CardioPredictNet CardioPredictNet Public

    A deep learning-based clinical support tool for cardiovascular disease risk stratification

    Jupyter Notebook

  6. Sample-Size-Calculator Sample-Size-Calculator Public

    The Biomedical Sample Size Calculator is a sophisticated, single-page web application designed to provide accurate sample size estimations for a wide array of research designs in biomedical and epi…

    Vue