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Key considerations for Cell & Gene Therapy developers to tap into AI

Yoshio Hagiwara Paolo Siciliano Willem van Asperen

By Yoshio Hagiwara, Dr Paolo Siciliano, Willem van Asperen

Cell & Gene

24 October 2023

PA Consulting life sciences expert Yoshio Hagiwara, PA cell and gene expert Paolo Siciliano, and Willem van Asperen, the chief data scientist at PA Consulting, describe some of the challenges cell and gene therapy developers may face when trying to implement AI solutions. This is the second part of a two-part series that published in Cell & Gene. Their first article can be viewed here.

It is undeniable that artificial intelligence (AI) and machine learning (ML) are going to play a key role in the cell & gene therapy (CGT) space, as we are currently witnessing in the broader pharmaceutical sector, and beyond. As discussed in our previous article, Opportunities for AI to assist Cell & Gene Therapy companies, AI and ML can find a number of applications in the CGT sector, from facilitating target discovery and patient recruitment in clinical trials to optimizing manufacturing and supply chain. However, AI/ML is still an emerging field, and while it is currently one of the most talked about topics in boardrooms across the globe, around 90% of all AI/ML models never make it into production to create real business value. This is due to various factors including data-related challenges and lack of algorithm model management and handling practices. In addition, AI must be responsible, ethical, and geared toward patient safety, while retaining human oversight. CGT developers interested in leveraging AI technologies need to be aware of such aspects as well and take into account a number of strategic considerations before adopting AI/ML technologies within their organizations.

Challenges associated with developing AI solutions in the CGT space

To deliver meaningful and measurable impact from AI solutions, therapy developers and key players across the CGT ecosystem need to carefully consider and address several challenges associated with data and regulations. Here we briefly describe some of the challenges organizations are likely to face when trying to implement AI solutions.

Read the full Cell & Gene article here

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