Yan Liao^1*, Zhanheng Chen^1*,Wangzheqi Zhang^1*, Lindong Cheng² , Yanchen Lin² , Ping Li³ , Miao Zhou⁴ ,  Mi Li¹ , ChunHua Liao¹ 

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.