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Member Research and Reports

Member Research and Reports

BU: Artificial Intelligence Can Predict Product Recalls from Customer Reviews

Unsafe, mislabeled, and contaminated foods cause an estimated 76 million illnesses each year in the U.S., including 325,000 hospitalizations and 5,000 deaths. The Food and Drug Administration (FDA) can take months to identify and verify a problem before issuing a recall, so most recalls come from manufacturers, often after enough people have gotten sick to generate bad press.

But soon, artificial intelligence (AI) could comb through online reviews to identify serious threats to public health, and speed the process of a product recall, according to a new study co-authored by a Boston University School of Public Health (BUSPH) researcher.

In the study, published in the Journal of the American Medical Informatics Association (JAMIA) Open, the researchers taught an AI model to predict food product recalls from Amazon reviews with about 74-percent accuracy. The AI then used Amazon reviews to identify thousands of potentially unsafe food products that have not yet been investigated.

“Health departments in the U.S. are already using data from Twitter, Yelp, and Google for monitoring foodborne illnesses,” says the study’s senior author, Dr. Elaine Nsoesie, assistant professor of global health at BUSPH. “Tools like ours can be effectively used by health departments or food product companies to identify consumer reviews of potentially unsafe products, and then use this information to decide whether further investigation is warranted.”

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