STORY · FORSKNING_
AI closes the data loop in drug development
MIT Technology Review describes how artificial intelligence addresses the exponentially rising cost problem in drug development, which since the 1950s has doubled every nine years (Eroom's law). AI systems can now close the data loop by using outcomes from clinical trials to continuously improve predictive models, reducing the time from laboratory to market from an average of 10-15 years.
WHY IT MATTERS
This tackles pharmacy's biggest bottleneck: the cost spiral that hinders innovation. When AI models learn directly from real clinical data, it could fundamentally change the economics for small companies and biotech startups that currently lose competition to large pharma companies.
SOURCES
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