AI Scope
Fighting Malaria with AI


User research & Product design

Can we overcome the constraints of classic microscopy in isolated areas by creating AI that helps diagnose malaria without needing trained medical staff? This is a problem that a small ThoughtWorks team, together with AI Scope, set out to solve.

The final vision: artificial intelligence that can diagnose parasitic diseases such as malaria and tuberculosis. In order to develop this, a large database is needed with images of previously detected parasites. The project was therefore divided into two phases. 

The first phase involved the development of an app that microbiologists and trained analysts can integrate with their workflow when doing blood analysis. With their phone mounted to the microscope, they capture positive blood samples, mark the disease in the photo, and save it to a database.

During the second phase, open-source image recognition technology will be combined with a portable, resistant and ultra cheap microscope to help early diagnosis of curable diseases, globally.

New workflow for microbiologists when integrating the app

The team faced many challenges. Due to the pro bono nature of the project, we were especially short on resources. This forced us to think carefully about the bare minimum requirements, and get feedback as quickly as possible. 

Working asynchronously and lacking understanding about the context in which the app would be used, presented additional problems. Collecting user insights was vital but had to be done by instructed people on the ground. Experimenting with phone and image quality as well as internet connectivity in remote areas were a constant.

The initial MVP, consisting of only three screens as the bare minimum.

To get the app off the ground as quickly as possible, we used existing patterns and libraries. Material Design was taken as a starting point for the interface.  A dark skin was chosen for the app, to avoid eye strain for users who are looking through a microscope for the larger part of their day. Similarly, due to the red and pink tones in the microscopic images, green was chosen to stand out as accent color. 

For microbiologists to use the app and collect the necessary images for machine learning, we were depending on their goodwill. It didn’t necessarily solve any of their direct problems; they just believed in and were willing to contribute to the cause. Therefore, the most important principle was that the app was seamlessly integrated with their way of working, and that engagement with it was as short as possible.  

After taking a photo, the user highlights where the parasite is identified.

With the help of a health facility in Zambia, the team was able to get realtime user feedback. An important finding was that the app was not providing enough context for the analysts to understand the intended workflow. More flexibility was also needed between the capturing and masking of images. Here, we intentionally decided to compromise speed of the workflow in exchange for clarity.  

Our consideration of alternatives based on user feedback.

The project is currently ongoing and is open source. Anyone looking to contribute can find the project in this GitHub repository