AI’s potential to combat the opioid epidemic is being explored as researchers and clinicians seek innovative solutions. Amidst the opioid crisis complexities, AI-driven advancements offer hope. Machine learning identifies risks, predicts relapses, and detects overdose signs. However, concerns arise, like misuse of facial recognition for discrimination. It’s vital for all stakeholders, including the public, to ensure AI’s ethical use and prevent the exacerbation of challenges like the opioid crisis.
The opioid pandemic has proven to be a difficult problem that, like the game of whack-a-mole, has stumped researchers for almost 20 years. The understanding of the constantly changing social and systemic causes of opioid consumption, as well as the identification of probable overdose hotspots, have been the subject of laborious and frequently unsuccessful efforts. Healthcare professionals work to provide safe, efficient treatment and resources to people who are struggling with addiction.
As the opioid epidemic’s pervasive impact persists, researchers and clinicians have turned their attention to an unconventional solution: artificial intelligence (AI). A question arises—Could AI hold the key to finally conquering this crisis?
In the realm of healthcare, embracing new technology is a gradual process, notorious for its reluctance to quickly adopt and implement innovations. This cautious approach comes at a cost. Reports indicate that the industry faces losses of over $8.3 billion annually due to tardiness or resistance to adopting advanced technologies like electronic health records.
However, the toll exacted by the opioid epidemic is far greater than mere financial figures. Stretching back to 1999, more than a million lives have been claimed by drug-related overdoses. In 2021 alone, the United States witnessed 106,699 deaths due to drug overdoses, marking one of the highest per capita volumes in the nation’s history. A staggering 75% of these fatalities can be attributed to opioid usage, encompassing both prescription painkillers like Vicodin and Percocet, as well as illicit drugs such as heroin.
Despite substantial investments by organizations like the Centers for Disease Control and Prevention and the National Institutes of Health into outreach, education, and prescription monitoring programs, the epidemic’s grip has remained distressingly tenacious.
Over the past decade, I have undertaken research into the opioid epidemic, examining its impact on both urban and rural communities across America, including regions such as New York City and rural southern Illinois.
Consensus within my field is reluctantly reached—unraveling the intricate risks faced by drug users involves a significant degree of conjecture. Which substances will they encounter? How will they administer them—by injection, inhalation, or ingestion? And with whom will they use, should the need for aid arise in case of overdose?
The complexity extends further. Practitioners consistently grapple with capricious federal and state regulations dictating effective treatments for opioid use disorder, such as suboxone. Additionally, they find themselves in a perpetual game of catch-up with the erratic influx of drugs contaminated with inexpensive synthetic opioids like fentanyl, a primary contributor to the recent upsurge in opioid-linked overdose fatalities.
While AI marvels like ChatGPT might captivate the public imagination, a quiet revolution merging AI with medicine has been underway, with addiction prevention and treatment as its prime beneficiaries.
Advancements in this arena predominantly leverage machine learning to pinpoint individuals susceptible to opioid use disorder, treatment discontinuation, and relapse. For instance, researchers from the Georgia Institute of Technology have recently employed machine learning techniques to effectively detect individuals on Reddit who may be at risk of misusing fentanyl. Other scholars have created tools to identify misinformation about opioid use disorder treatments, enabling peers and advocates to intervene through education.
AI-driven initiatives like Sobergrid are even developing the ability to predict relapse risks, considering factors such as proximity to bars, and then linking individuals with recovery counselors.
The most influential developments focus on reducing overdoses, often stemming from drug combinations. Researchers at Purdue University have designed and tested a wearable device capable of detecting overdose signs and autonomously administering naloxone—an antidote for overdoses. Equally significant is the emergence of tools designed to identify hazardous contaminants within drug supplies, potentially leading to a significant decrease in fentanyl-driven overdoses.
Despite these promising prospects, concerns loom. Could facial recognition technology be misused to locate individuals who appear to be under the influence, leading to discrimination and maltreatment? Instances such as Uber’s 2008 attempt to patent technology to detect inebriated passengers raise apprehensions.
Moreover, the specter of misinformation, a challenge that already plagues chatbots, emerges. Might malicious actors manipulate chatbots to disseminate erroneous information, misguiding drug users about associated risks?
Harking back to Fritz Lang’s iconic silent film “Metropolis” in 1927, society has long been captivated by the concept of human-like technology simplifying and enriching lives. However, the optimism of films like Stanley Kubrick’s “2001: A Space Odyssey” and the subsequent cautionary tales in movies like “I, Robot” and “Minority Report” have signaled a shift towards a more nuanced perspective.
Ultimately, the responsibility of maintaining the integrity of AI extends not only to researchers and healthcare professionals but also to patients and the broader public. Preventing AI from exacerbating humanity’s most daunting challenges, such as the opioid epidemic, rests on vigilance and concerted effort.
