The opioid epidemic has been extremely complex, confounding researchers for the better part of two decades as they have tried to better understand and identify potential drivers of the evolving social and systemic factors that lead people to start using opioids. Hotspot overdose.
These often tedious and flawed efforts occur while doctors work to provide safe and effective treatment and other resources to those in the throes of addiction.
While both researchers and clinicians study the broad and ongoing scope of the opioid epidemic, they are now curiously exploring artificial intelligence and asking: Could this be the breakthrough that ends the opioid epidemic?
Healthcare is not a place to jump on bandwagons, as it is notoriously slow to trial and implement new technology. This trend is not without consequences. One report suggested that The industry loses more than $8.3 billion annually Because they are lagging behind or not reliant on technology such as advanced electronic health records.
Public health researchers and biomedical engineers have been quietly cultivating a revolution integrating medicine with artificial intelligence, with addiction prevention and treatment being the latest beneficiary.
But the opioid epidemic’s toll is greater than what’s on the books. And going back to 1999, More than one million people have died from drug overdoses. In 2021, There have been 106,699 drug overdose deaths in America, among the highest per capita rates in the country’s history. About 75% of all these overdoses are attributed to opioid use, which includes prescription painkillers like Vicodin and Percocet as well as “street” drugs like heroin.
Despite the Centers for Disease Control and Prevention and the National Institutes of Health pumping billions of dollars into outreach, education, and prescription monitoring programs, the epidemic has stubbornly persisted.
Over the past decade, I have researched the opioid epidemic in rural and urban communities across America, including New York City and rural areas of southern Illinois.
Most people in my field agree, albeit reluctantly, that there is an incredible amount of guesswork involved in determining the complex risks that drug users face. What medications will they receive? Will they inject it, snort it, or smoke it? Who, if anyone, would they use in the event of an overdose and needing help?
That’s not it. Practitioners also regularly battle federal and state guidelines on effective treatments for opioid use disorder, such as Suboxone. They also find themselves playing catch-up with an increasingly unpredictable drug supply tainted with cheap synthetic opioids like fentanyl. It is largely responsible for recent increases in deaths from opioid overdose.
While AI developments like ChatGPT are what have captured most people’s imagination, public health researchers and biomedical engineers have been quietly creating an AI-integrated revolution in medicine, and addiction prevention and treatment are the latest beneficiaries.
Innovations in this area primarily use machine learning to identify individuals who may be at risk for developing opioid use disorder, withdrawal from treatment, and relapse. For example, researchers from the Georgia Institute of Technology have recently developed machine learning techniques To effectively identify individuals on Reddit who were at risk of abusing fentanylwhile Other researchers have developed a tool To find misinformation about treatments for opioid use disorder, both of which can allow peers and advocates to intervene in education.
Other programs powered by artificial intelligence, Like supergridare working to develop the ability to detect when individuals are at risk of relapse — for example, based on their proximity to bars — and then connect them with a recovery counselor.
The most impactful developments relate to reducing overdoses, which often occur by mixing drugs. At Purdue University, researchers developed and piloted a A wearable device that can detect signs of overdose and automatically inject the individual with naloxoneOverdose reversal agent. Another critical development was the creation of tools for Detection of hazardous contaminants in pharmaceutical supplieswhich can radically reduce fentanyl overdoses.
Despite this huge promise, there are concerns – could facial recognition technology be used to identify people who appear high, leading to discrimination and abuse? Uber actually took a step forward in developing this type of capability in 2008, trying to patent a technology that could detect a drunk passenger.
What about false/misleading information? A problem that chatbots already suffer from? Could malicious parties include incorrect information in chatbots to mislead drug users about the risks?
Going back to Fritz Lang’s silent film “Metropolis” in 1927, we find that audiences were fascinated by the idea of new human-like technology that would make life easier and richer. From Stanley Kubrick’s “2001: A Space Odyssey” in 1968 to films like “I, Robot” and “Minority Report” in the early 2000s, these melancholy visions have slowly morphed into a kind of existential dread.
It will be up not only to researchers and clinicians, but also to patients and the general public to keep AI faithful and turn humanity’s greatest challenges, such as the opioid epidemic, into insurmountable ones.