Artificial intelligence in monitoring HIV treatment adherence: A conceptual exploration

Artificial intelligence (AI) has emerged as a powerful tool in healthcare, with the potential to revolutionize the monitoring of HIV treatment adherence. This conceptual exploration delves into the various roles that AI can play in this critical aspect of HIV management, aiming to improve patient outcomes and enhance the effectiveness of treatment programs. The Review will discuss how AI can analyze data from various sources, such as electronic medical records, wearable devices, and patient-reported outcomes, to monitor treatment adherence in real-time. By leveraging machine learning algorithms, AI can identify patterns and trends in patient behavior that may indicate non-adherence, allowing healthcare providers to intervene early and provide targeted support. Furthermore, the Review will highlight the potential of AI to personalize adherence monitoring strategies based on individual patient characteristics and treatment regimens. AI can analyze large datasets to identify factors that influence adherence, such as socioeconomic status, mental health, and comorbidities, enabling tailored interventions that address the unique needs of each patient. Additionally, the Review will discuss how AI can improve patient engagement and education through personalized interventions delivered via mobile applications or virtual assistants. These interventions can provide patients with real-time feedback on their adherence behavior, offer motivational support, and address any barriers to adherence they may be facing. Overall, this conceptual exploration will demonstrate the transformative potential of AI in monitoring HIV treatment adherence. By harnessing the power of AI, healthcare providers can develop more effective strategies for improving adherence, ultimately leading to better health outcomes for patients living with HIV.