Author(s) :

Supritha Bhandarkar.

Volume/Issue :

Abstract :

Chronic pain affects millions globally, presenting significant challenges due to its complex, multifactorial nature and reliance on subjective assessments. AI-driven tools offer a transformative approach by enabling objective, continuous, and personalized pain evaluation and management. Leveraging machine learning, natural language processing, wearable biosensors, and multimodal data integration, these technologies enhance pain assessment accuracy, facilitate early detection of exacerbations, and support tailored treatment plans. Despite promising benefits such as improved patient outcomes, reduced opioid dependency, and enhanced healthcare efficiency, challenges including data privacy, ethical considerations, algorithmic bias, and clinician-patient acceptance must be addressed. This article explores the current landscape of AI applications in chronic pain care, data sources and collection methods, implementation barriers, and ethical implications. It further discusses future research directions, emphasizing the potential of AI to shift chronic pain management from reactive symptom control to proactive, holistic, and patient-centered care.

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