The Pistoia Alliance is currently working to establish the steps needed to turn the widespread adoption of AI/ML into a reality. We want to see how the greater community can help the industry overcome many of the obstacles it is currently facing.
Why is this important?
Artificial Intelligence in Life Sciences is moving from the peak hype to a more realistic view of the capabilities and value of the technology. Now is the time to find the best use cases and define the best practices for the use of Artificial Intelligence and Machine Learning in life sciences.
Without a doubt, Artificial Intelligence and Machine Learning can potentially transform large aspects of Life Sciences and Health workflows, and the industry has made considerable investments in Artificial Intelligence and Machine Learning arenas.
Our aim is to discover and promote the best ways to use these new promising technologies, apply them to solve previously intractable problems of significance to the public and educate our members via cutting-edge presentations and discussions.
What will the project achieve?
Current areas of focus include:
- Developing a Best Practices Toolkit for the Good Machine Learning Practices in life sciences (2021-2023)
- Educating members of the community about use cases and applications through our webinar series and conference presentations
- Defining specific pre-competitive project ideas and spinning out new projects or communities of interest. We recently spun out the DataFAIRy Bioassay Annotation project.
The community has also successfully completed:
How will the project do this?
The Centre of Excellence is organizing events, webinars, and hackathons, which will cover a range of challenges aimed at kicking off AI projects or prototype AIs. This will help us to work out where the main challenges are, and to establish an adoption strategy that serves these needs. The Pistoia Alliance’s community also provides a virtual and physical co-working space, enabling life science professionals to share best practices and learn how best to apply AI, machine, and deep learning to R&D – wherever they are in the world.
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