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Using Artificial Intelligence in Clinical Research – Use Cases and Implications – The Promise, Challenges, and Potential
Michele Bennett, Global Research Lead for Advanced Analytics, Avanade
Abstract:
Using artificial intelligence to positively impact medical research is in its nascent stage, and it seems that the potential and technical feasibilities may finally be aligned. For example, natural language processing (NLP) and deep learning can be used to analyze literature, medical records, and past and present clinical trial data, including notes and images, to identify and stratify patient populations, improve trial design, and optimize site and investigator selection. Computer vision algorithms can help radiologists interpret scans to increase speed and accuracy of review. Predictive analytics can help reduce patient attrition in advance of and during a clinical trial by optimizing patient selection and monitoring patients during trials, in real-time, using digital endpoints. While these innovations are not without challenges, and rigorous research is needed to ensure viability, using AI and data-driven techniques can lead to more informed studies and ideally, shorten time to market of safe and effective treatments.
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