Artificial Intelligence in Pain Management: Advancing Translational Science in Digital Health Research from Bench to Bedside

Abstract

Borges, Julian Y.V.

Artificial Intelligence (AI) is rapidly transforming the landscape of healthcare, with particularly profound implications in the field of pain management. This chapter delves into the integration of AI-driven tools that revolutionize the way pain is assessed, monitored, and treated. Through the use of predictive modeling, real-time monitoring, and personalized treatment plans, AI significantly enhances the precision, efficiency, and effectiveness of pain management strategies.

The discussion extends to various AI applications, shedding light on the ethical considerations that accompany these technological advancements, as well as outlining future research directions. Collectively, these insights underscore the immense potential of AI to not only improve pain management practices but also to significantly elevate patient outcomes.

Central to this transformation is the role of translational science in bridging the gap between theoretical AI models and their practical, clinical applications. This "bench to bedside" approach ensures that innovations in AI are not merely confined to research environments but are actively integrated into real-world patient care. For instance, AI-powered predictive analytics in pain management, driven by sophisticated machine learning algorithms, have progressed from computational experiments to clinical trials, and ultimately, to widespread implementation in healthcare settings.

These AI models are now being utilized in hospitals to assess patient pain levels in real-time, predict opioid requirements, and optimize pain management protocols. This progression exemplifies how translational science is facilitating a paradigm shift in healthcare, positioning AI as an indispensable tool in modern pain management.

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