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Unlock the full potential of AI in pharma

AI offers huge promise for the pharma and life sciences sectors. But progress in areas like diagnostics or therapeutics is hampered by a lack of clear guidelines.

When it comes to AI, pharma companies have created impressive applications. However, most of them are developed for non-regulated areas. In our white paper, we share best practices from over 100 AI projects that will allow you to unlock the full potential of AI in regulated application areas.


Fill out our form below to download the whitepaper.

You will find eight essential principles for developing and successfully implementing regulatory compliant AI solutions:  

  1. High data and labelling quality  
  2. Good machine learning practices  
  3. Simple, transparent, interpretable models  
  4. Thorough evaluation  
  5. Reproducible machine learning pipelines  
  6. Manage data, model and code history  
  7. Comprehensively document code, data, models and design decisions  
  8. Communicate results and uncertainty carefully  

This whitepaper will enable you to create safe, reliable AI solutions that satisfy regulatory requirements and are able to genuinely improve patients’ lives. 

Sorry, our whitepaper is currently unavailable due to technical issues. Please contact us directly if you are interested to read it: steffen.spendel[at]