How AI helps clinical trials run faster
What's the story
Clinical trials are integral to the advancement of health care and drug development, but they are often riddled with delays and exorbitant costs. Artificial intelligence, however, is now proving critical in optimizing the process around the world. By streamlining patient recruitment, improving protocol optimization, and site selection, AI is making clinical trials more efficient. Here is how AI technologies are revolutionizing clinical trials to overcome conventional challenges.
#1
Accelerating patient recruitment
AI-powered tools have made patient recruitment easier than ever by analyzing electronic health records with Natural Language Processing. These systems are able to identify eligible candidates three times faster than manual methods, with 96% accuracy. Consequently, enrollment rates have seen a 65% increase, and recruitment cycles have been shortened from months to days. Tools like Tempus xR show how well AI filters out 72% ineligible candidates from initial pools.
#2
Enhancing protocol optimization
Predictive analytics models leveraged for protocol optimization are 85% accurate in predicting trial outcomes. This eliminates costly amendments that can increase a trial timeline by as much as 260 days. By limiting changes and ensuring smoother execution, AI integration speeds up trial timelines by 30% to 50%, while slashing costs by up to 40%. All of these advancements make clinical trials a whole lot more efficient.
#3
Improving site selection efficiency
Machine learning algorithms analyze a lot of past performance data to identify the best trial sites with the highest chances of success. Sites predicted to be strong performers enroll patients five times faster than others. With AI-optimized site selections, enrollment rates improve by up to 37%, accelerating cumulative timelines by an average of three months versus industry benchmarks.
Tip #4
Revolutionizing safety monitoring
AI technology has revolutionized safety monitoring with digital biomarkers that facilitate continuous adverse event detection with 90% sensitivity. This guarantees patient safety while alleviating the burden of manual review from the shoulders of trial staff. Financially, it saves time (up to 20%) with faster enrollment (via better risk management), reduces costs (via better site targeting), and lowers cycle time (for protocol amendments).