The use of artificial intelligence in medical diagnosis and treatment is quietly revolutionizing health care. Artificial intelligence (AI) tools are already being used across different practices and departments to help streamline processes for staff and offer a better, more personalized, service to patients. By analyzing a vast amount of patient data, including medical history, genetic information and behavioral patterns, AI can help in generating individualized treatment plans.
And from answering phones to sifting through large amounts of health data, and managing workflows to providing virtual support to patients round-the-clock via chatbots, AI also offers endless further possibilities to help improve the level of care patients receive.
The opportunities are just as applicable when it comes to addiction treatment and recovery support. For example, earlier diagnosis could be achieved with AI monitoring of social media activity or browsing habits, and treatment center operations could be streamlined with the right AI tools, to help addiction treatment providers and staff.
17.82% of adults in the U.S. had a substance use disorder in the past year, totaling over 45 million people. And when it comes to battling a substance use disorder (SUD), the relapse rate is estimated to be between 40 and 60 percent. With millions of Americans struggling to overcome addictions, the question health care providers are asking is, can AI help with mental health and addiction treatment?
The answer is, potentially. The use of AI in diagnosis of mental health disorders is in its early stages, but is showing clear potential to improve outcomes. From the early detection of illness through more effective intervention techniques, more accurate relapse prediction, and better in-center care, AI can positively affect the way we diagnose and treat patients with SUD. Here are some of the key benefits that AI technology is already delivering.
AI in early detection
Addiction is not always easy to diagnose, and it can be incredibly challenging for patients to acknowledge. But AI-powered tools could potentially be used to detect addictive behaviors even before obvious symptoms of addiction are present. Tracking social media usage, online behavior and communication patterns could all be ways to identify early signs of addictive behaviors, which could be used to help kickstart a conversation about treatment.
One study analyzing 1.5 billion posts on Twitter (now known as X) found that the language used predicted opioid overdose mortality. Discussion of negative emotions, long work hours and boredom were clear risk factors; whereas discussion of resilience, travel/leisure and positive emotions were protective factors. It provides an interesting case for the future use of social media tools to potentially flag users and communities at greater risk of substance use disorders.
Real-time information gathered using digital tools can also be used by clinics to better customize a support plan in the early stages. Tracking behavior in an honest and transparent way can open better lines of communication between care providers and patients. And real-time data about how often, when and why someone engages in unhealthy behavior can save valuable time when developing a recovery plan.
A study published in 2022 showed just how effective machine learning, a form of AI, can be for collecting clinical data and using it to predict clinical outcomes, such as identification of between-session heavy drinking in those undergoing outpatient treatment for alcohol use disorder.
Virtual counseling and self-help
In 2021, 94% of people aged 12 or older with a substance use disorder did not receive any treatment. Nearly all people with a substance use disorder who did not get treatment at a specialty facility did not think they needed treatment. There are many barriers preventing people from seeking professional help. For example, some patients are unable to meet in person due to financial constraints, physical challenges, lack of childcare, or even a lack of transportation.
In today’s world, AI can help provide therapy support and facilitate access to professional help. One randomized study from Sweden found that participants seeking online help for alcohol use had better outcomes when assigned to a digital intervention program when compared to those only offered information.
Although no substitute for human counselors and in-person empathy, AI chatbots and virtual assistants have the benefit of being available around the clock and in some cases are already being used to supplement in-person therapy. By flagging patterns of usage, virtual counseling can also help patients identify triggers and address them as they appear. Guided self-care with the careful use of AI can also enable patients to remain mindfully controlling their recovery.
Apps like Vorvida and Previct offer AI-based treatment for alcohol and drug addiction, working as tools to support patients between appointments. Vorvida, for example, uses AI to interact with users like a therapist, asking questions, analyzing responses, and providing individual feedback to help manage problematic behavior around alcohol. While careful to stress that the app is no substitute for clinical care, Vorvida’s own study claims that users self-reported lower rates of alcohol consumption, binge-drinking and drunkenness compared to a control group.
Machine learning and personalization
Although human emotional analysis and professional expertise are paramount in addiction treatment, AI can be used to support medical professionals by analyzing vast datasets much more quickly. Machine learning can be used to analyze large quantities of patient data and identify patterns across populations with the potential for less bias.
This can lead to more equitable outcomes, as diverse populations can be studied and data captured about unique challenges or risks that some groups may face. Traditionally, most medical studies have been conducted on Caucasians men, which creates a lack of applicability of medical research in diverse populations and can result in treatments that are less effective or even harmful for certain groups. However, AI can generate more specific insights pertinent to individuals of color or other minoritized groups.
With data from ethnically diverse patients, AI can locate risk factors or correlations specific to certain groups, providing greater equity in health care. This leads to the possibility of developing highly personalized treatment plans, based on data that are especially relevant to a more diverse group of patients.
And from the macro to the micro. With wearable devices, AI can capture and analyze extremely detailed individual health measurements in real-time, such as sleep quality, increased heart rate and blood pressure, and other physical symptoms of drug use or necessity.
These data can enable individuals to be more aware of their actions, with biometric feedback alerting to possible triggers, allowing positive change actions. Devices like Behaivior’s proprietary Recovery™ platform streams real-time data to care providers, screening individuals for physiological and behavioral changes. These can be warning signs that users are at increased risk for substance use or return to use, and enable the opportunity to immediately intervene.
Real-time monitoring and relapse prevention support
From eating disorders to gambling and video games, behavioral addictions are difficult to pin down. AI can help when in recovery and trying to avoid relapse. Monitoring and wearable devices offer in-the-moment insight into what a patient is experiencing and how often. Caregivers can monitor progress remotely, which is critical for bridging gaps between appointments.
Predictive analytics can also be used to anticipate vulnerable periods and potential relapses. A recent study by the National Institute on Drug Abuse used AI to analyze Facebook posts from people in outpatient treatment for addictions, and was able to accurately predict whether they would complete their program. A treatment center could potentially use this tool to identify patients most at risk of relapse, and funnel more resources in their direction.
What are the risks associated with AI in medical settings?
Although AI has the potential to be an unbiased platform, it is only ever as good as the data sets it draws from. If the training data lacks diversity, there is a risk that historical biases may be amplified. With the historical stigma attached to SUDs, there is a real risk that the very tool for reducing discrimination may be used to perpetuate it. With internal decision-making of AI algorithms often opaque, it can also be hard to identify when implicit bias is occurring.
Data security is also a concern. Although predictive analytics can be a powerful tool, they may lead to unwanted interventions or privacy violations. And the more tools with access to confidential patient data, the greater the chance of data breaches and unauthorized access.
Even more worryingly, any new technology has the potential for sinister misuse. Malicious actors could use AI as a marketing tool to identify and target vulnerable individuals as potential customers for addictive substances.
The future of addiction treatment
The use of artificial intelligence in medical diagnosis and treatment is here to stay, and the potential for substance use disorders is profound. Although there are rightful concerns about implicit bias in AI platforms and data security which need to be addressed, there are clear benefits being explored when it comes to using machine learning and AI in early detection, self-help, data analysis, and personalized healthcare and recovery plans.
With careful and targeted use of AI, addiction treatment centers could begin to streamline resources and triage more effectively by making data-driven decisions. This can help enable optimized use of clinical facilities, better support for both patients and staff, reduced rates of relapse, earlier diagnosis and improved predictive capabilities.
