Drug addiction is a rising concern globally that has deeply attracted the attention of the healthcare sector. The United States is not an exception, and the drug addiction crisis there is even more serious, with 10% of adults having faced substance use disorder, while around 75% of this number has been reported as not having received any treatment. Surprisingly, there are annually over 70,000 deaths reported as being due to drug overdose. Researchers are continually searching for solutions, as the current strategies have been ineffective. Health informatics platforms like electronic health records, telemedicine, and the clinical decision support system have great potential in tracking the healthcare data of patients on an individual basis and provide precise medical support in a private space. Such technologies have been found to be useful in identifying the risk factors of drug addiction among people and mitigating them. Moreover, the platforms can be used to check prescriptions of addictive drugs such as opioids and caution healthcare providers. Programs such as the Prescription Drug Monitoring Program (PDMP) and the Drug and Alcohol Services Information Systems (DASIS) are already in action in the US, but the situation demands more in-depth studies in order to mitigate substance use disorders. Artificial intelligence (AI), when combined with health informatics, can aid in the analysis of large amounts of patient data and aid in classifying nature of addiction to assist in the provision of personalized care.

Opioid use disorder (OUD) is a chronic disease that remains difficult to treat, even with significant improvements in available medications. While current treatments work well for some, they often do not account for the unique needs of individual patients, leading to less-than-ideal results. Precision medicine offers a new path forward by tailoring treatments to fit each person’s genetic, psychological, and social needs. This review takes a close look at medications for OUD, including methadone, buprenorphine, and naltrexone, as well as long-acting options that may improve adherence and convenience. Beyond medications, the review highlights the importance of addressing mental health co-morbidities, trauma histories, and social factors like housing or support systems to create personalized care plans. The review also explores how emerging technologies, including artificial intelligence and digital health tools, can enhance how care is delivered. By identifying research gaps and challenges in implementing precision medicine into practice, this review emphasizes the potential to transform OUD treatment. A more individualized approach could improve outcomes, reduce relapse, and establish a new standard of care focused on recovery and patient well-being.