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Artificial intelligence tools quickly detect signs of injection drug use in patients’ health records

Artificial intelligence tools quickly detect signs of injection drug use in patients’ health records

In the United States, drug overdoses now kill more people than car accidents. This tragic trend is being fueled in large part by the growing use of powerful opioids like fentanyl.

To combat this problem, some hospitals are turning to artificial intelligence (AI). AI tools can quickly analyze huge volumes of data to find patterns that might indicate drug abuse.

For example, one AI program called “Eve” was able to read through more than four million patient records in just two weeks. Eve flagged 1,200 patients as being at high risk for opioid abuse.

This is just one example of how AI is being used to help fight the opioid epidemic. AI is also being used to develop new painkillers that are less addictive, and to help identify patients who are at risk of overdose.

Overall, AI shows great promise in the fight against opioids. With its ability to quickly analyze large amounts of data, AI can help identify potential drug abuse before it leads to tragedy.

According to a new study, artificial intelligence tools can quickly and accurately detect signs of injection drug use in patients’ health records.

Injection drug use is a major public health problem, and early detection is critical for prevention and treatment. However, manually reviewing health records for signs of injection drug use is time-consuming and often inaccurate.

The new study, published in the journal PLOS ONE, found that two commercial artificial intelligence tools were able to accurately detect signs of injection drug use in health records with high accuracy and low false positive rates.

The first tool, called IDU-Net, was able to detect injection drug use with an accuracy of 96.7%. The second tool, called Drug Abuse Detection (DAD), was accurate 96.3% of the time.

“Our results suggest that these artificial intelligence tools could be used to quickly and accurately identify patients at risk for injection drug use, potentially helping to improve prevention and treatment efforts,” said study co-author Dr.

Injection drug use is a major public health problem, and early detection is critical for prevention and treatment. However, manually reviewing health records for signs of injection drug use is time-consuming and often inaccurate.

The new study, published in the journal PLOS ONE, found that two commercial artificial intelligence tools were able to accurately detect signs of injection drug use in health records with high accuracy and low false positive rates.

The first tool, called IDU-Net, was able to detect injection drug use with an accuracy of 96.7%. The second tool, called Drug Abuse Detection (DAD), was accurate 96.3% of the time.

“Our results suggest that these artificial intelligence tools could be used to quickly and accurately identify patients at risk for injection drug use, potentially helping to improve prevention and treatment efforts,” said study co-author Dr.

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