Welcome to episode 199 of the AI in Action podcast, the show where we break down the hype and explore the impact that Data Science, Machine Learning and Artificial Intelligence are making on our everyday lives.

Powered by Alldus International, our goal is to share with you the insights of technologists and data science enthusiasts to showcase the excellent work that is being done within AI in the United States and Europe.

Today’s guest is Felix Faber, Founder and CEO at MindPeak GmbH in Hamburg. Founded in 2018, MindPeak’s mission is to make microscopical analysis accessible for everyone in the world. They automate visual processes in the clinical laboratory with Artificial Intelligence and Deep Learning. The demand for medical image analysis is growing rapidly but the number of cancer experts remains the same which results in growing workloads per specialist.

MindPeak help cancer experts meet this challenge. They build automation tools for visual diagnosis using state-of-the-art AI and deep learning. Their tools assist cancer experts in providing reliable and reproducible diagnoses. This frees specialists from tedious, repetitive tasks such as quantification of cells or biomarkers and allows them to focus on tasks where their human expertise is indispensable.

In the episode, Felix will discuss:

The exciting work he is doing with Mindpeak

Applying AI within pathology

How AI and automation is making people’s lives easier in the industry

Challenges they are solving using data science

How Covid-19 has transformed digitalisation in Healthcare

What’s in store for the future at MindPeak

To find out more about Felix and the interesting work they do at MindPeak, check out the website www.mindpeak.ai. You can also connect with Felix on Linkedin or follow him on Twitter @felixfaber_ai.

What did you think of Felix’s podcast? Where do you see the future of AI and Data Science in the Healthcare industry heading in the next few years? We would love to hear your thoughts on this episode, so please leave a comment below.

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