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Artificial intelligence in perioperative pain management: A review

Yan Liao1*, Zhanheng Chen1*,Wangzheqi Zhang1*, Lindong Cheng2 , Yanchen Lin2 , Ping Li3 , Miao Zhou4 ,  Mi Li1 , ChunHua Liao


1School of Anesthesiology, Naval Medical University, Shanghai 200433, China. 2Graduate School, Hebei North University, Zhangjiakou 075000, China. 3Graduate School, Wannan Medical College, Wuhu 241000, China. 4Department of Anesthesiology, the Affiliated Cancer Hospital of Nanjing Medical University, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University, Nanjing 210009, China. 

* The authors contribute equally.


Address correspondence to: Miao Zhou, The Affiliated Cancer Hospital of Nanjing Medical University, Department of Anesthesiology, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, Nanjing Medical University, Nanjing 210009, China. E-mail: zhoumiao2613@163.com; Tel: +86  18217567295. Mi Li, School of Anesthesiology, Naval Medical University, 800 Xiangyin Road, Yangpu District, Shanghai 200433, China. E-mail: limi@smmu.edu.cn; Tel: +86-21-81872033. Chunhua Liao, School of Anesthesiology, Naval Medical University, 800 Xiangyin Road, Yangpu District. Shanghai  200433, China. E-mail: Liaochh7@smmu.edu.cn; Tel: +86 21 81872025.


Acknowledgement: This work was supported by the National Natural Science Foundation of China under Grants 62002297, 62073225, and 61836005, the Science and Technology Commission of Shanghai Municipality under Grant 20XD1434400, talent Development Fund of Shanghai under Grant 2020075, Medical-Engineering Cross Fund of Shanghai Jiao Tong University under Grant YG2022QN043, and the Guangxi Science and Technology Base and Talent Special Project under Grant 2021AC19394. The authors would like to thank all the guest editors and anonymous reviewers for their constructive advice.


DOI: https://doi.org/10.61189/275419wdddvs


Received February 21, 2024; Accepted March 25, 2024; Published September 30, 2024


Highlights

● Artificial 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.

Abstract

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.

Keywords: Artificial intelligence, pain management, perioperative pain, acute pain

Yan Liao, Zhanheng Chen,Wangzheqi Zhang, et al. Artificial intelligence in perioperative pain management: A review. Perioper Precis Med. 2024 Sep; 2(3):99-115. doi: 10.61189/275419wdddvs

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