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As machine learning and deep learning technologies advance thanks to advances in computation, algorithms and data availability, the possibilities of the technology continue to expand in medicine. While these AI-driven approaches have real potential, such systems demand large volumes of representative data, careful privacy and security scrutiny and thoughtful long-term strategic planning. In this Q&A, Kathryn Rough, associate director of the Center for Advanced Evidence Generation at IQVIA, discusses the impact of deep learning on healthcare delivery and recommends steps to take during the design, training, evaluation and deployment phases to increase the likelihood that these models will be safe, effective and ethical when trained on real-world health data. Rough also explores the role of epidemiologists in evaluating these technologies as part of multidisciplinary teams and provides advice for health…BenevolentAI is pioneering AI-driven drug discovery methods
“Can we treat chronic inflammation in ulcerative colitis by reversing immune cell activation in colonic mucosa?” That’s an example of a biological question that the AI-enabled drug discovery firm BenevolentAI (AMS:BAI) would ask when exploring a new drug target.
Incorporating a disease, sign, mechanism and tissue into a single question provides focal points for the company’s AI models to explore when generating target hypotheses, said Anne Phelan, chief scientific officer of Benevolent AI in a presentation at the Royal Society earlier this year.
BenevolentAI’s strategy involves a comprehensive understanding of the biological systems underlying various diseases, breaking down silos in clinical areas and tapping diverse multimodal data to discover novel therapeutic targets. For ulcerative colitis, the company’s AI models sift through vast amounts of scientific literature and data to spot promising targets and pathways that could potentially alleviate …