Hack4Health runs research-driven hackathons where students build and validate machine-learning models for real clinical challenges — from cardiovascular risk to neurodegenerative disease. Mentored by researchers, judged on rigor.
Free to enter · Open to students everywhere
The tools and research areas our builders work with
Hack4Health unites students, mentors, and researchers to turn bold ideas into computational medicine that makes a real-world difference.
The Cardiovascular AI Research Hackathon
A multi-month research hackathon challenging students to build machine-learning models that predict and interpret cardiovascular disease risk. Work with de-identified clinical datasets, collaborate with mentors, and submit a reproducible solution on Kaggle.
$1,500
Prize pool
235+
Entrants
Research
Format
Submissions close July 1, 2026 · 5:00 PM EDT
Datasets provided
Each hackathon runs over several months and is built so anyone — regardless of experience — can produce meaningful, reproducible research. Here's the journey.
Sign up on Kaggle solo or with up to four members. New researchers are paired with mentors so no one builds alone.
Get de-identified clinical data, baseline notebooks, and Learn2Hack tutorials to scope a real, well-defined research question.
Over several months, develop and validate your models with live mentorship — iterating toward rigorous, reproducible results.
Submit your solution and write-up on Kaggle. Entries are evaluated for performance, interpretability, and clinical insight.
An online research hackathon where students trained machine-learning models for early detection and progression forecasting of Alzheimer's disease, working from MRI datasets such as the Augmented Alzheimer MRI Dataset.
Each season we tackle a new health challenge. Subscribe below to be the first to know when our next hackathon is announced.
New to ML? Our curated tutorials get you from zero to your first biomedical model — fast.
You only need a minimal subset of PyTorch to participate.
Focus on torch.tensor, shapes, nn.Linear, nn.Sequential, and optimizer.step().
Pandas lets you explore biomedical data quickly.
Mastering these covers 80% of what you will do in early experiments.
Hack4Health was my first hackathon ever. The mentors believed in our Alzheimer's detection model before we did — now it's a research project I'm genuinely proud of.
Priya Sharma
High school senior · AI 4 Alzheimer's winner
The level of organization rivals events run by major universities. Clear tracks, real clinicians as judges, and a community that actually wants you to succeed.
Daniel Okafor
Mentor · ML Engineer
We sponsored Byte 2 Beat and were blown away by the talent. These students are tackling problems most companies haven't even scoped yet.
Lena Müller
Partnerships Lead · HealthTech sponsor
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Everything you need to know before joining your first — or next — Hack4Health event.
Byte 2 Beat is our current research hackathon focused on cardiovascular disease. Participants build machine-learning models that predict and interpret heart-disease risk using de-identified clinical datasets, then submit reproducible solutions on Kaggle. Submissions close July 1, 2026.
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