This article highlights a groundbreaking three-year study conducted by a team of doctors and scientists at UCSD, funded by Wellcome Leap, to develop a model that utilizes artificial intelligence to assess a patient’s risk level of addiction to opioids. Led by Dr. Rodney Gabriel, the study aims to create a single test or battery of tests that can identify high-risk patients, enabling doctors to allocate resources more effectively and provide targeted treatment. The model takes into account a patient’s genetic, biological, and environmental conditions to assess their risk for addiction. What’s innovative about this approach is that it can track a patient’s healthcare journey in real-time, adjusting their risk assessment as their health changes. Dr. Gabriel acknowledges concerns about AI transparency and plans to address this by ensuring the model provides clear explanations for its predictions. If successful, this technology has the potential to be adopted by institutions worldwide, revolutionizing pain management and opioid addiction prevention.
AI-Powered Opioid Risk Assessment
A patient’s healthcare journey changes every day, if we can track using the electronic medical records system how their risk changes as their healthcare changes and let doctors know hey because of this, the patients risk of addiction is increasing.
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