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AI tools have great potential to streamline clinical trial processes and reduce drug development time.
FREMONT, CA: Clinical trials are a critical tool for getting new treatments to people. An average clinical trial process spans between 7.5 and 12 years, studies estimate, with costs ranging from 161M - 2.6B dollar per drug. Just 14 percent of clinical trials are successful, and only one in ten drugs under Phase 1 ends up being approved by the FDA. Research also shows that difficulty finding the right volunteer subjects can undermine the effectiveness of clinical trials. Fortunately, experts have designed and tested a new solution that uses artificial intelligence (AI) to effectively identify eligible trial subjects from Electronic Health Records (EHRs), allowing clinical trials to be more efficient. Here is how.
The AI system has natural language processing, which allows computers to understand and analyze human language as the system analyzes data. This makes it possible for AI programs to process data, extract information, and generate knowledge independently. The AI system extracts structured data, including patient demographics and clinical assessments from EHRs. Then it identifies unstructured information from clinical notes, such as the patients' clinical conditions, symptoms, treatments, and so on. The extracted information is then matched with clinical trial eligibility requirements to determine a subject's suitability for a specific trial.
AI tools can also help improve patient selection by reducing population heterogeneity, selecting patients who are more likely to have a measurable clinical endpoint, and determining a population more capable of responding to treatment. Technologies like NLP and ML could improve processes like electronic phenotyping, and a method focused on reducing population heterogeneity. In addition to enhancing electronic phenotyping, AI tools can also help patients understand complicated clinical trial eligibility criteria. In addition to all the above, AI tools are also helping sponsors monitor patient behavior and response to drugs throughout clinical trials, thus assisting them in keeping track of potential patient dropouts.
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