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The National Library of Medicine’s Impact on AI Research

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The National Library of Medicine (NLM) has been providing the medical field with information since 1836. Starting off as a small collection of books, the entity has grown into the world’s largest biomedical library, housing biomedical research and computational health data research.

NLM supports two research programs, the Division of Extramural Programs (EP) and Intramural Research Program (IRP), that seek to catalyze advancements in the intersection of medicine and computer science. Specifically, these programs focus on biomedical informatics, data science, computational biology, and computational health. Through these programs NLM is making major investments in finding new ways that Artificial Intelligence (AI) can be used in the medical field. 

A couple of examples are mentioned in the NIH Director’s Blog, “National Library of Medicine Helps Lead the Way in AI Research”.

“Corey Lester and his colleagues at the University of Michigan College of Pharmacy, Ann Arbor, MI, are using AI to assist in pill verification , a standard procedure in pharmacies across the land. They want to help pharmacists avoid dangerous and costly dispensing errors. To do so, Lester is using AI to develop a real-time computer vision model. It views pills inside of a medication bottle, accurately identifies them, and determines that they are the correct or incorrect contents.”

“NLM investigator Sameer Antani is collaborating with researchers in other NIH institutes to explore how AI can help us understand oral cancer, echocardiography, and pediatric tuberculosis . His research also is examining how images can be mined for data to predict the causes and outcomes of conditions. Examples of Antani’s work can be found in mobile radiology vehicles, which allow professionals to take chest X-rays (right) and screen for HIV and tuberculosis using software containing algorithms developed in his lab.”

These are only a few examples. NLM-funded research is applying AI solutions to common medical problems such as early disease detection, disease management, clinical treatment decision making and more. You can read more on the NIH Director’s Blog here.

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