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zhoumiao2613@163.com
limi@smmu.edu.cn
Liaochh7@smmu.edu.cnArtificial intelligence (AI) is lauded for its capacity to resolve intricate problems with unwavering efficiency, devoid of fatigue. To elucidate the potential of AI in perioperative pain management, we have meticulously surveyed a vast array of scholarly works to discern the landscape of research in this multifaceted domain.
Conventional perioperative pain studies have primarily confined their scope to clinical aspects. However, this review delves into the amalgamation of AI and perioperative pain, heralding a diverse methodology for pain control.
AI's applicability in medical domains, particularly anesthesia, has spawned numerous inquiries into its synergy with perioperative pain. Yet, a dearth of comprehensive reviews encapsulating the current research milieu, pin pointing hurdles, and envisioning future directions in this sphere necessitated the present discourse.
We herein offer horizontal and vertical assessments of diverse models and algorithms employed in periopera tive pain management, encapsulated in diagrammatic form for reader accessibility. The compilation of this review draws from a spectrum of online scholarly repositories, thus ensuring a thorough and relevant assembly of insights.
Open Accesszhoumiao2613@163.comlimi@smmu.edu.cnLiaochh7@smmu.edu.cnArtificial intelligence (AI) is lauded for its capacity to resolve intricate problems with unwavering efficiency, devoid of fatigue. To elucidate the potential of AI in perioperative pain management, we have meticulously surveyed a vast array of scholarly works to discern the landscape of research in this multifaceted domain.
Conventional perioperative pain studies have primarily confined their scope to clinical aspects. However, this review delves into the amalgamation of AI and perioperative pain, heralding a diverse methodology for pain control.
AI's applicability in medical domains, particularly anesthesia, has spawned numerous inquiries into its synergy with perioperative pain. Yet, a dearth of comprehensive reviews encapsulating the current research milieu, pin pointing hurdles, and envisioning future directions in this sphere necessitated the present discourse.
We herein offer horizontal and vertical assessments of diverse models and algorithms employed in periopera tive pain management, encapsulated in diagrammatic form for reader accessibility. The compilation of this review draws from a spectrum of online scholarly repositories, thus ensuring a thorough and relevant assembly of insights.
Artificial intelligence (AI) leverages its swift, precise, and fatigue-resistant problem-solving abilities to significantly influence anesthetic practices, ranging from monitoring the depth of anesthesia to controlling its delivery and predicting events. Within the domain of anesthesia, pain management plays a pivotal role. This review examines the promises and challenges of integrating AI into perioperative pain management, offering an in-depth analysis of their converging interfaces. Given the breadth of research in perioperative pain management, the review centers on the quality of training datasets, the integrity of experimental outcomes, and the diversity of algorithmic approaches. We conducted a thorough examination of studies from electronic databases, grouping them into three core themes: pain assessment, therapeutic interventions, and the forecasting of pain management-related adverse effects. Subsequently, we addressed the limitations of AI application, such as the need for enhanced predictive accuracy, privacy concerns, and the development of a robust database. Building upon these considerations, we propose avenues for future research that harness the potential of AI to effectively contribute to perioperative pain management, aiming to refine the clinical utility of this technology.
ISSN: 2957-5443
Volume 2, Issue 3
September 2024
Pages: 73-123
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