Bundling medical AI and healthcare expertise to save lives




Challenges per year


AI for Health Engineers


hrs of Engineering

Multiplying Engineering Skills and Effort for Biodiversity

Empowering Medical Professionals and Patients with AI

With the AI for Health Lab we pool resources and share knowledge for the advancement of public health. Working with sensitive patient data is a tricky exercise and so is using AI to do critical diagnosis decisions. Through this Lab we are able to organize more AI for Good Challenges; deliver more real life healthcare solutions to medical centers; and train our machine learning community in specific skills relevant to medical AI.


Improving the quality and access to healthcare

Data science is making its way into medicine beyond labs and clinical research. Healthcare providers increasingly onboard bioinformaticians, medical and clinical data scientists requiring an AI-heavy skillset. AI-powered solutions are assisting with diagnoses, patient care and expanding treatment support beyond medical facilities. We can’t be missing here.  

We are lucky to have a flourishing collaboration ongoing thanks to our AI for Health Chapter based at the Eindhoven University of Technology. Working with health institutes and universities all over the country, they organize AI for Health Challenges and health topic events. 

Thanks to their support, support of our partner ecosystem and our community of AI for Health engineers developing medical AI solutions, we are able to contribute towards UN Sustainable Development Goal 3: Good Health and Well-being.

Left: Sample training picture

Right: Corresponding masks for building a Generative Adversarial Network (GAN) that accurately segments chest X-Rays to detect COVID19 in hospitalized patients

UN Sustainable Development Goal 3: Good Health and Well-being

Ensure healthy lives and promote well-being for all at all ages.









Find out more about Goal 3
SDG Report 2022_Goal 3 infographic
Goal 3 infographic, source:

Make life-saving data available while preserving privacy

Improve the speed & accuracy of diagnostics in radiology

Foster early detection & prevention of diseases instead of treating symptoms

Make preventive diagnostics more affordable

Improve access to mental healthcare

Model the progress and impact of infectious diseases


Medical Imaging

The use of artificial intelligence in diagnostic medical imaging has shown impressive accuracy and sensitivity in the identification of imaging abnormalities. Assisting healthcare professionals with detection, segmentation and classification, it can be of immense value for screenings and precision medicine. Our AI for Health engineers used Generative Adversarial Networks to segment X-rays of lungs to detect Covid-19. They applied U-nets and mask-RCNN for instance segmentation of cervical vertebrae to predict patient developing hernia.

Neck Hernia Gif
Neck Hernia Gif

Sensitive Patient Data

Medical AI is teeming with sensitive patient data used to train machine learning models. We teach our AI engineers how to handle sensitive information without compromising the model accuracy. We deploy techniques like Federated Learning to train algorithms across multiple decentralized servers holding local data without sharing them. Watch a recording from a Federated Learning Masterclass we’ve organized for an AI for Health Challenge.

Blurred Sensitive Patient Data

Explainable AI

For transparent algorithmic decision making we deploy the techniques of Explainable AI or sometimes called Trustworthy AI. In healthcare, it’s important to see how the machine learning model arrived at a certain decision. How does it assess which preterm baby is at risk of getting sepsis or what does the neural network pay attention to when segmenting a medical image (as seen on our heatmap of a lung scan)?

Explainable AI

Biosensor Analysis

We collaborate with medical professionals to deploy state-of-the-art medical analysis in our Challenges. In order to develop a usable AI solution, the participants need to understand the relevant hardware and the medical procedures. To improve hernia detection models for example, it’s of great value to have a neuroscientist explain the principles of radiology imaging, hernia diagnosis and its treatment to Challenge participants. Or the analysis of electrocardiograms to prevent heart failure.

Biosensor Analysis
July 17, 2023

AI against Toxic Clouds

Use Computer Vision to detect toxic clouds emitted by large factories and report the authorities on the sightings
March 26, 2023

AI against Diabetes

Construct Computer vision models that estimate the risk for diabetic ulcer formation, as well as highlight areas that indicate the risk.
January 8, 2023

AI for Preterm Babies

Infer the circadian rhythm of preterm babies from UMC patients monitoring data and develop ML models to regulate light and sound to improve their development in incubators.
May 25, 2022

AI for Health - Heart Failure Detection

Developing a machine learning model to predict blood values from electrocardiograms as an indication of heart failure.
March 23, 2022

AI for Health - Preventing Sepsis

Early prediction of the risk of a preterm born baby developing sepsis by extending an existing classification model with more features, exploring more advanced models and developing a new explainable time-series model.
November 12, 2021

AI for Health - Predicting Deterioration

Adapting and evaluating the deterioration index (DI) model to better predict the risk of deterioration of patients in the hospital beds.
October 13, 2021

AI for Health - Hernia Detection

Detecting hernia on X-ray images in collaboration with hospital radiologists.
June 18, 2021

AI for Health - Covid Detection

Improve the workflow of radiologists by automating lung segmentation in thorax X-rays to detect Covid
March 18, 2020

AI against Covid-19 Challenge

How can we predict the growth of new infections? How do we know if our measures against COVID-19 are working? What are the implications for how the virus is spreading?

Submit an AI for Good Challenge

Do you have a problem that can be solved by applying machine learning technology? We’ll crowdsource an AI solution in 10 weeks.

Submit a challenge

We get some machine learning in medicine done

  • Develop prediction models for blood values from electrocardiograms as an indication of heart failure
  • Early risk prediction of a preterm born baby developing sepsis
  • Develop explainable time-series models for prediction
  • Detect hernia on X-ray images of a anonymized patient dataset
  • Automatic segmentation and tracking of cervical vertebrae over time
  • Segment lungs using Generative Adversarial Models
  • Predict economic effect of Covid 19 and its snowball effect on wellbeing
  • Model the burden of Covid 19 on the healthcare system
SPOTS Robi Beninca launching the flying ranger UAV (unmanned aerial vehicle) & Poacher detection running on the thermal video camera footage on edge hardware of the UAV
Results: Watch and read

From our YouTube channel

How can you participate?

Become a certified AI for Health Engineer

Individual engineers who collaborate in the AI for Earth Challenges are trained in working with sensitive patient data; applying computer vision techniques for medical imaging and in other machine learning skills essential to medical AI.

FruitPunch AI for Health Community

Sign-up at the platform for free and fill your profile. Pick your interests and join corresponding communities. Create a skill tree you’ll be developing with every AI for Good activity.

Start my journey
Upgrade your skills with AI bootcamps & masterclasses

Learn how to apply your acquired AI knowledge in the real world. Join AI for Good challenges and collaborate in teams all over the globe.

Upgrade my skills
AI for Health Certificate

Every activity adds to your development. You’ll be accredited with badges for specific hard and soft skills and certifications after an accomplished challenge.

Build my skill tree
AI for Earth Skills
Computer Vision
Domain knowledge injection
Explainable AI
Human aided AI
Differential Privacy
Federated Learning
Anomaly detection
Linear models
Wematic Segmentation
Grey box methods
Signal Processing
Biometric sensoring
Data anonimization
How can you participate?

Become a certified AI for Wildlife Engineer

Individual engineers who collaborate in the AI for Wildlife Challenges are trained in edge computing, computer vision, model pruning and other skills essential for the applied AI  projects.

Join the community

Sign-up at the platform for free and fill your profile. Pick your interests and join corresponding communities. Create a skill tree you’ll be developing with every AI for Good activity.
Start my journey
Start your personalized learning journey

Participate in a challenge

Join our machine learning focused learning events led by experts collaborating with the AI for Good community. Learn with the pros and from one another.
Join the community platform
FruitPunch AI challenge and skilltree

Get certified

Every activity adds to your development. You’ll be accredited with badges for specific hard and soft skills and certifications after an accomplished challenge.
Get certified
Get your badges and certificates
For Organizations
Solve your challenges!

Challenge Partners

Medical & healthcare organizations and experts applying the results of the AI for Health Challenges. Do you have a medical or healthcare problem that can be solved with AI? Submit your challenge


Contributing Partners

Use their teams, expertise and resources to advance AI for Health Lab & Challenges.Can your experts or technology contribute to solving an AI for Health Challenge? Partner up! 👇

Get your community involved!

Community Partners

Use their reach to spread the AI for Good news, advocate for AI for Good Lab & Challenges.Do you have a network we could reach out to with medical AI and healthcare topics? Connect on Linkedin, Twitter or drop us a note 👇

Come on board the AI for Health Lab!

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