One such tool gaining attention is NarxCare, a data-driven monitoring system that assigns numerical ratings to patients' medication histories. This data provides doctors with a basic understanding of potential addiction risks. However, the effectiveness of such systems has sparked a divide among professionals.

Jason Gibbons, a health economist, emphasized the need for comprehensive evaluation to ensure that AI interventions do not inadvertently cause more harm than good. He expresses concerns that the intended impact of such monitoring systems might not align with their real-world outcomes, leading to unintended negative consequences for patients.

AI models are entering the forefront by providing algorithmic assessments of individual patients to help professionals gauge their susceptibility to addiction. These AI-powered evaluations consider various data points, including prescription history, dosage details, and the prescribing physicians' profiles. The resulting scores are designed to aid medical professionals in their decision-making processes, although they are not meant to be the sole determining factor for patient care.

Tech firms and health experts however advise caution, emphasizing the importance of using AI as a supplementary tool rather than a replacement for medical intervention. The blend of human insight and AI-enabled data can lead to more informed decisions, ensuring that patients receive the most appropriate care.

As t AI continues to surge, with predictions of over 37% annual growth between now and 2030, the World Health Organization (WHO) has issued an advisory related to the subject. WHO has called for the development and implementation of "safe and ethical AI for health." The advisory specifically highlights the need to exercise prudence while using AI-generated language model tools (LLMs) to safeguard human well-being, autonomy, and public health.
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