Development of AI/ML solutions is, however, hampered by a lack of clear guidelines. Over the last few years, here at Zühlke we have been given the opportunity to develop and successfully implement regulatory compliant AI solutions for a number of pharmaceutical companies.
Our 17-page whitepaper explores best practices and regulatory requirements for AI in diagnostics. We also share our understanding of good practice in machine learning, distilled from our experience in medical machine learning projects. The whitepaper will enable you to create safe, reliable artificial intelligence solutions that satisfy regulatory requirements and are able to genuinely improve patients’ lives.
If you would like to receive a copy of our whitepaper, please fill in the form below.
How to build medical AI: Combining best practices from machine learning and medical device development
AI offers huge promise for the pharma and life sciences sectors. For diagnostics in particular, early adopters have an opportunity to play a major role in shaping the ecosystem as a whole.
Contact person for Switzerland
Dr. Gabriel Krummenacher
Head of Data Science
Gabriel Krummenacher leads the Data Science Team at Zühlke and has several years of experience in conducting data analytics and machine learning projects. His main focus is on medical machine learning applications and bringing prototypes to production. He holds a PhD and M.Sc. from the Institute for Machine Learning at ETH, where he worked on scalable methods for large-scale and robust learning, wheel defect detection and sleep stage prediction with deep learning.
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