Intelligent Health.tech Issue 22 | Page 42

I N D U S T R Y I N V E S T I G A T I O N
AI-generated candidates and new datahungry AI models means labs need to run experiments at a vastly higher scale .
That can ’ t be achieved by simply optimising the manual processes at the bench that labs rely on today . Instead , labs need to be reinvented around complex imaging and single-cell omics assays that allow a more complete understanding of biology , along with robotic automation that enables these assays to be run at scale . Although this trend has already started , AI applications can rapidly accelerate it .
The dearth of specialised engineers needed to analyse data from complex assays and build robotic orchestration is one key bottleneck . AI models like scGPT can accelerate data analysis by automating codeintensive tasks such as reference mapping or cell annotation . AI agents will also enable scientists to set up robotic automation through just natural language , democratising access across the industry .
Advancements in AI also have the potential to address key challenges in clinical trials . Take as an example patient recruitment , the most time-consuming part of a trial . Even though millions of people are needed to participate in clinical trials , fewer than 5 % of Americans have participated in clinical research of any kind .
Sound clinical trial design requires randomisation , but that takes participants out of the driver ’ s seat – they no longer have final say over their treatment decisions , creating a barrier to recruiting . In 2022 , the European Medicines Agency ( EMA ) provided a qualification opinion allowing the use of AI models to develop predicted control outcomes for Phase 2 / 3 trials from historical control data , ultimately requiring fewer participants to make this difficult randomisation choice .
Beyond the lab work and clinical trials required to get a product to market , there is also so much knowledge work , from
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