For nearly two decades, the opioid epidemic has confounded researchers, posing a complex challenge as they strive to comprehend the evolving social and systemic factors driving opioid abuse and identify areas susceptible to overdose incidents. This dilemma persists while clinicians work tirelessly to provide effective treatment and support to those grappling with addiction. However, as both researchers and healthcare professionals continue to grapple with the opioid crisis, there’s a growing curiosity: Can Artificial Intelligence (AI) be the moonshot solution that finally brings an end to this devastating epidemic?

The healthcare industry isn’t typically quick to embrace new technology, often lagging behind in the adoption of advanced tools like electronic health records. This reluctance comes at a significant cost, with the industry reportedly losing over $8.3 billion annually due to this delay. Yet, the toll of the opioid epidemic extends far beyond financial losses. Since 1999, over one million lives have been lost to drug-related overdoses. In 2021 alone, America witnessed 106,699 drug overdose deaths, marking one of the highest per capita volumes in the nation’s history. Alarmingly, around 75% of these overdoses were attributed to opioids, including prescription painkillers such as Vicodin and Percocet, along with illicit drugs like heroin.

Despite substantial investments from organizations like the Centers for Disease Control and Prevention and the National Institutes of Health in outreach, education, and prescription monitoring initiatives, the epidemic has proven remarkably resilient.

Drawing from a decade of research spanning both rural and urban communities across the United States, it’s widely accepted among experts that identifying the intricate risks faced by drug users involves a considerable amount of guesswork. Questions abound: What substances will they use? Will they inject, snort, or smoke them? Will they have someone nearby in case of an overdose? The challenges don’t end there, as practitioners grapple with varying federal and state guidelines on effective treatments for opioid use disorder, such as suboxone. Additionally, they find themselves racing to keep up with the ever-shifting landscape of drug supplies, often contaminated with inexpensive synthetic opioids like fentanyl, a key driver of recent spikes in opioid-related overdose fatalities.

While much attention has been showered on high-profile AI developments like ChatGPT, the field of public health research and biomedical engineering has been quietly ushering in an AI-driven medical revolution, with a focus on addiction prevention and treatment.

Innovations in this realm primarily leverage machine learning to identify individuals at risk of opioid use disorder, treatment disengagement, and relapse. For instance, researchers from the Georgia Institute of Technology have harnessed machine learning to effectively detect individuals on Reddit who may be at risk of misusing fentanyl. Others have developed tools to combat misinformation about opioid use disorder treatments. These AI-powered programs empower peers and advocates to intervene through education. Initiatives like Sobergrid are exploring the ability to identify individuals at risk of relapse based on factors like proximity to bars and connect them with recovery counselors.

Some of the most impactful developments are focused on reducing overdoses, often brought about by drug combinations. Researchers at Purdue University have created a wearable device that can recognize overdose signs and automatically administer naloxone, an overdose-reversing agent. Equally crucial is the emergence of tools designed to detect hazardous contaminants in drug supplies, a breakthrough with the potential to significantly reduce fentanyl-related overdoses.

While these advancements hold immense promise, concerns arise. Could facial recognition technology be misused to locate individuals who appear to be high, potentially leading to discrimination and abuse? Additionally, there is the risk of disinformation or misinformation being embedded into AI systems, potentially misleading drug users about the associated risks.

As we tread further into the AI-driven future, it becomes imperative for not only researchers and clinicians but also patients and the broader public to ensure the responsible and ethical use of AI in addressing humanity’s most daunting challenges, such as the opioid epidemic. By doing so, we can harness the transformative potential of AI to make a meaningful impact on the lives of those affected by this crisis.

By Impact Lab