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Improving real-time data acquisition and mining helps scientists employ artificial intelligence and machine learning to make better decisions faster, accelerating product development and scale-up.
FREMONT, CA: According to most in the life science industry, Artificial Intelligence (AI) will change the life sciences sector from R&D through commercialization. Although AI is still in its infancy and most research is still in its early stages, some benefits are beginning to surface. There are also pockets of early adopters who are pioneering innovative ways in the hopes of gaining a competitive advantage by bringing goods to market faster, improving patient outcomes and care, and reducing costs.
In today's life science industry, AI is used efficiently in a variety of domains. Three of these areas are discussed in the following sections.
Histopathology image analysis and automated diagnosis were ready for AI, given the technological progress in the digitalization of comprehensive histology slides, which allow all microscopic magnifications. In combination with complex algorithms and automated immunohistochemistry measuring methods, AI and pattern recognition have improved pathologists' ability to supervise analysis and focus on more challenging cases.
Product development durations in the industry range from seven to ten years from discovery to launch, to decrease them to five to seven years.
The goal of shortening total product development timelines is supported by advances in AI and machine learning that reduce the time it takes to design manufacture, and launch novel patient medicines. Across the drug development spectrum, scientists combine research, lab, and clinical data with novel information sources (for example, social media and wearables) to provide a holistic view of the drug development candidate. Improving real-time data acquisition and mining helps scientists employ AI and machine learning to make better decisions faster, accelerating product development and scale-up.
Approximately 80 percent of clinical trials miss their patient enrollment deadlines. By highlighting high-probability targets, advanced AI models can improve and accelerate clinical site and patient selection decisions by combining unanalyzed historical structured and unstructured clinical trial data. Continuing to deploy powerful AI models during ongoing clinical programs allows for real-time course modifications and adjustments. When high-probability success targets are engaged at the start of a clinical study, along with a readiness to make real-time course changes, the chances of fulfilling patient enrollment deadlines improve.
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