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AI in Substance Use Treatment: Promise, Progress, and Some Pitfalls

What’s real, what’s hype, and what providers need to know right now.

By Jamelia Hand MHS CADC CODP I


“We haven’t rolled it out yet, we’re still figuring out how it fits.”


That’s what one medical director shared during a recent roundtable discussion on emerging tech in behavioral health. And she’s not alone. While artificial intelligence (AI) is making waves across healthcare, many clinicians treating substance use disorders (SUD) are still watching from the sidelines feeling curious, skeptical, and cautious.



There’s a reason for that. SUD treatment is personal, complex, and deeply relational. AI doesn’t easily fit into that framework. But that doesn’t mean it won’t.


This blog breaks down where AI is being explored in SUD care and where real-world challenges remain.


  1. Early Identification & Referral


The Promise: AI models trained on EHRs and behavioral data can flag OUD earlier in the care journey. One NIH-funded model performed as well as human clinicians, catching patients who needed referral to addiction care; thus, reducing costly readmissions.


The Reality: Most health systems still haven’t integrated these models into everyday use. Many clinicians don’t trust “black box” algorithms to make life-or-death decisions, and few have the tech infrastructure to make AI implementation seamless.


“If we can catch OUD early, we can save lives. But most of these tools are still stuck in pilot mode.”- Behavioral Health Director, Midwest Health System


  1. Personalized Treatment Planning


The Promise: AI can analyze data across demographics, comorbidities, medication history, and even brain scans to recommend optimal treatment approaches.


The Reality: While the science is promising, it hasn’t reached most frontline providers. Publicly funded programs often lack resources to use these insights, and some worry that payers may start using AI to deny care instead of personalize it.


“We’d love better decision support, but only it helps (not replaces) our judgement”- Clinical Supervisor, Outpatient SUD Program


  1. Relapse Prediction & Monitoring


The Promise: Apps and wearables track sleep, stress, and substance use risk in real time. AI then analyzes this data to predict when a person might be at risk for relapse and notifies the care team.


The Reality: Adoption is minimal in under-resourced settings. Patients may not have access to devices, or the trust to use them. Without the right conversations, AI monitoring can feel more punitive than supportive.


“Recovery takes trust. Surveillance doesn’t always feel like support.”- Individual in long term recovery “



  1. Virtual Companionship & AI Chatbots


The Promise: Chatbots and tools built on ChatGPT can offer CBT-based support and psychoeducation 24/7, helping people when human therapists aren’t available.


The Reality: While helpful in some cases, these tools raise major questions around accuracy, ethics, and liability. Most treatment programs aren’t using them yet, and many providers aren’t sure how or whether they should.


“AI can help with access, but it can’t replace connection.”- Licensed IN Addiction Counselor


  1. Ethical and Practical Considerations


AI isn’t just a technical challenge, it’s a human one. We must be honest about the risks:


  • Bias: Tools trained on narrow datasets may misdiagnose or misguide treatment, especially in marginalized communities.


  • Privacy: Sensitive health and behavioral data could be misused or exposed.


  • Over reliance: AI should augment (not override) clinical judgment.


⚠️ Real harm can happen if we treat AI as infallible. Caution matters.



  1. The Bigger Threat: Policy Vacuum & Power Consolidation


The danger isn’t AI on its own, it’s how it’s being developed and deployed without oversight.


📌 Here’s the real problem


  1. Weak or Absent Regulation: Many AI models are trained on others’ intellectual property or sensitive data without consent, compensation, or transparency.


    2. Preemption of Local Authority: Earlier drafts of federal legislation included a 10-year ban on state-level AI regulation, stripping communities of power to protect against misuse.


    2. Federal Overreach & Lobbying: Federal overreach & lobbying: Under pressure from Big Tech, some versions of the bill even threatened to withhold broadband funds from states that dared to regulate AI.


    3. Accountability Gaps:

    There’s still no clear way to hold AI companies liable for harm especially in cases involving child safety, mental health, or patient privacy.


✅ Fortunately, in a rare bipartisan moment, the Senate recently voted 99–1 to remove the moratorium on state regulations for 5 years.


But we are still far from safe. Without enforceable guardrails, the people most impacted by SUD- often those already over-surveilled and underserved- stand to lose the most.


Bottom line: Don’t get caught in the “AI is good” vs. “AI is bad” debate.



  1. Drug Discovery & The Next Chapter


AI is accelerating the development of new addiction medications, scanning thousands of compounds and predicting efficacy with unprecedented speed. This could shorten timelines for breakthrough therapies, but only if those advances are shared equitably.


Final Takeaway: Use With Caution and Build With Purpose


AI in substance use treatment has promise, but it must be implemented with eyes wide open. No tech tool can replace the trust, empathy, and expertise at the heart of recovery.

If we move too fast without accountability, we risk doing harm in the name of innovation.



🌐 How Vantage Can Help


At Vantage Clinical Consulting, we guide substance use and behavioral health programs in making thoughtful, ethical, and strategic decisions about AI and digital health. From staff education to implementation planning, we work to ensure that technology enhances (not replaces) human care.


🔗 Visit www.vantageclinicalconsulting.com to learn more.







 
 
 

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