Application progress of artificial intelligence in the standardized management of anesthesia for day surgery_West China Medical Journal

Authors：

DONG Xiaoyan , XU Fu ,  SUN Defeng

Department of Anesthesiology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, P. R. China;

Corresponding author：

SUN Defeng, Email: sdf-yl@163.com

Keywords：

Day surgery; artificial intelligence; anesthesia management

DOI：

10.7507/1002-0179.202601113

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Artificial intelligence (AI) is increasingly being integrated into the anesthesia management process to enhance the accessibility and efficiency of day surgery medical services. This article examines the recent advancements in AI technology concerning the standardized management of anesthesia for day surgery. It details the application of AI in perioperative assessment, risk prediction, and patient education, while also discussing AI-driven precision in intraoperative anesthesia management and postoperative recovery decision support. Furthermore, it analyzes the role of AI in optimizing surgical processes and resource scheduling. The article summarizes current challenges related to data, algorithms, and ethics, and anticipates future directions, aiming to provide references that promote the development of intelligent, safe, and efficient day surgery practices in China.

1.	中华医学会麻醉学分会. 日间手术麻醉指南. 中华医学杂志, 2023, 103(43): 3462-3471.
2.	孙德峰. 日间手术麻醉规范化管理策略. 实用医学杂志, 2024, 40(3): 283-288.
3.	光奇, 王建林, 王康太, 等. 基于交互设计信息系统的智能化日间手术全流程管理探索. 华西医学, 2023, 38(9): 1380-1385.
4.	严蓉, 高巨, 束余声, 等. 麻醉科门诊建设与管理: 实践与思考. 中华麻醉学杂志, 2022, 42(3): 358-361.
5.	田梦, 孙忠良, 孙德峰. 日归手术麻醉的规范化管理. 华西医学, 2022, 37(2): 165-170.
6.	蒋丽莎, 李文畅, 马洪升. 基于异构数据学习的日间腹腔镜胆囊切除术患者准入模型探索. 华西医学, 2024, 39(2): 300-303.
7.	Liu R, Stone TAD, Raje P, et al. Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and current procedural terminology set embeddings. Br J Anaesth, 2024, 132(3): 607-615.
8.	Heidegger T, Ghulam A, Bischoff M, et al. Beyond artificial intelligence: a critical appraisal from an airway management perspective. Anesth Analg, 2025, 140(5): 1111-1113.
9.	胡小义, 王迪, 纪木火, 等. 机器学习在麻醉学领域的应用前景. 临床麻醉学杂志, 2024, 40(6): 634-638.
10.	杨展眉, 唐之音, 刘洪涛. 人工智能在预测困难气道方面的研究进展. 中国医药, 2025, 20(10): 1578-1582.
11.	Hayasaka T, Kawano K, Kurihara K, et al. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care, 2021, 9(1): 38.
12.	Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine. N Engl J Med, 2023, 388(13): 1201-1208.
13.	Rupp S, Ahrens E, Rudolph MI, et al. Development and validation of an instrument to predict prolonged length of stay in the postanesthesia care unit following ambulatory surgery. Can J Anaesth, 2023, 70(12): 1939-1949.
14.	徐桂容, 柏斗胜, 王霄霖, 等. 基于智慧信息大数据平台助推全院日间手术服务高质量发展. 华西医学, 2023, 38(2): 278-283.
15.	Gabriel RA, Harjai B, Simpson S, et al. Machine learning-based models predicting outpatient surgery end time and recovery room discharge at an ambulatory surgery center. Anesth Analg, 2022, 135(1): 159-169.
16.	Shah A, Dhiman P. Artificial intelligence in peri-operative prediction model research: are we there yet?. Anaesthesia, 2024, 79(10): 1017-1022.
17.	Shimada K, Inokuchi R, Ohigashi T, et al. Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis. BMC Anesthesiol, 2024, 24(1): 306.
18.	吴飞, 张冉, 孙卫格, 等. 远程健康教育在日间手术患者中的应用研究进展. 中国卫生质量管理, 2024, 31(9): 43-47.
19.	武汉大学人民医院(湖北省人民医院). 人工智能的个性化内镜检查预约兼术前宣教方法及系统: 中国, CN202311398120.7. 2024-01-05.
20.	Laferrière-Langlois P, Morisson L, Jeffries S, et al. Depth of anesthesia and nociception monitoring: current state and vision for 2050. Anesth Analg, 2024, 138(2): 295-307.
21.	Tu Z, Jeffries S, Pelletier E, et al. Deep reinforcement learning for multi-targets propofol dosing. J Clin Monit Comput, 2025, 39(3): 613-623.
22.	佘守章, 俞卫锋. 加强临床监测智能化研究提升患者围麻醉手术期安全性. 中华麻醉学杂志, 2023, 43(7): 778-780.
23.	Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology, 2018, 129(4): 663-674.
24.	Wijnberge M, van der Ster BJP, et al. Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: a cohort study. Eur J Anaesthesiol, 2021, 38(6): 609-615.
25.	Hua C, Chu Y, Zhou M, et al. Predictive effect of postoperative recovery in general anesthesia patients using interpretable models based on swarm intelligence machine learning. Front Physiol, 2025, 16: 1565548.
26.	Cascella M, Schiavo D, Cuomo A, et al. Artificial intelligence for automatic pain assessment: research methods and perspectives. Pain Res Manag, 2023, 2023: 6018736.
27.	van de Sande D, van Genderen ME, Verhoef C, et al. Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): an external validation study. Surgery, 2022, 172(2): 663-669.
28.	Romero-Brufau S, Wyatt KD, Boyum P, et al. Implementation of artificial intelligence-based clinical decision support to reduce hospital readmissions at a regional hospital. Appl Clin Inform, 2020, 11(4): 570-577.
29.	Rajala M, Kääriäinen M, Tanhua A, et al. Patients’ perspectives on postoperative follow-up calls in day surgery: a qualitative study. Scand J Caring Sci, 2025, 39(3): e70077.
30.	孟婧文, 安璐, 郝欣欣, 等. 日间手术随访方式的研究进展. 北京医学, 2023, 45(9): 822-825.
31.	Kurtz SM, Higgs GB, Chen Z, et al. Patient perceptions of wearable and smartphone technologies for remote outcome monitoring in patients who have hip osteoarthritis or arthroplasties. J Arthroplasty,2022, 37(7S): S488-S492. e2.
32.	Jung SJ, Kim JH, Rhee SJ. Home-based rehabilitation using wearable motion tracker with smart phone application feedback is as effective as conventional self-directed rehabilitation after total knee arthroplasty: randomized controlled trial. BMC Sports Sci Med Rehabil, 2025, 17(1): 370.
33.	Gordon AM, Nian P, Baidya J, et al. Randomized controlled studies on smartphone applications and wearable devices for postoperative rehabilitation after total knee arthroplasty: a systematic review. J Arthroplasty, 2025; 40(8): 2204-2213. e1.
34.	董兰, 胡娟娟, 吕君, 等. 智能急诊预检分诊信息化系统实践. 解放军医院管理杂志, 2019, 26(1): 69-71, 93.
35.	Ahn S, Jung S, Park JH, et al. Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: in-hospital setting. Resuscitation, 2024, 202: 110325.

1. 中华医学会麻醉学分会. 日间手术麻醉指南. 中华医学杂志, 2023, 103(43): 3462-3471.
2. 孙德峰. 日间手术麻醉规范化管理策略. 实用医学杂志, 2024, 40(3): 283-288.
3. 光奇, 王建林, 王康太, 等. 基于交互设计信息系统的智能化日间手术全流程管理探索. 华西医学, 2023, 38(9): 1380-1385.
4. 严蓉, 高巨, 束余声, 等. 麻醉科门诊建设与管理: 实践与思考. 中华麻醉学杂志, 2022, 42(3): 358-361.
5. 田梦, 孙忠良, 孙德峰. 日归手术麻醉的规范化管理. 华西医学, 2022, 37(2): 165-170.
6. 蒋丽莎, 李文畅, 马洪升. 基于异构数据学习的日间腹腔镜胆囊切除术患者准入模型探索. 华西医学, 2024, 39(2): 300-303.
7. Liu R, Stone TAD, Raje P, et al. Development and multicentre validation of the FLEX score: personalised preoperative surgical risk prediction using attention-based ICD-10 and current procedural terminology set embeddings. Br J Anaesth, 2024, 132(3): 607-615.
8. Heidegger T, Ghulam A, Bischoff M, et al. Beyond artificial intelligence: a critical appraisal from an airway management perspective. Anesth Analg, 2025, 140(5): 1111-1113.
9. 胡小义, 王迪, 纪木火, 等. 机器学习在麻醉学领域的应用前景. 临床麻醉学杂志, 2024, 40(6): 634-638.
10. 杨展眉, 唐之音, 刘洪涛. 人工智能在预测困难气道方面的研究进展. 中国医药, 2025, 20(10): 1578-1582.
11. Hayasaka T, Kawano K, Kurihara K, et al. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care, 2021, 9(1): 38.
12. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine. N Engl J Med, 2023, 388(13): 1201-1208.
13. Rupp S, Ahrens E, Rudolph MI, et al. Development and validation of an instrument to predict prolonged length of stay in the postanesthesia care unit following ambulatory surgery. Can J Anaesth, 2023, 70(12): 1939-1949.
14. 徐桂容, 柏斗胜, 王霄霖, 等. 基于智慧信息大数据平台助推全院日间手术服务高质量发展. 华西医学, 2023, 38(2): 278-283.
15. Gabriel RA, Harjai B, Simpson S, et al. Machine learning-based models predicting outpatient surgery end time and recovery room discharge at an ambulatory surgery center. Anesth Analg, 2022, 135(1): 159-169.
16. Shah A, Dhiman P. Artificial intelligence in peri-operative prediction model research: are we there yet?. Anaesthesia, 2024, 79(10): 1017-1022.
17. Shimada K, Inokuchi R, Ohigashi T, et al. Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis. BMC Anesthesiol, 2024, 24(1): 306.
18. 吴飞, 张冉, 孙卫格, 等. 远程健康教育在日间手术患者中的应用研究进展. 中国卫生质量管理, 2024, 31(9): 43-47.
19. 武汉大学人民医院(湖北省人民医院). 人工智能的个性化内镜检查预约兼术前宣教方法及系统: 中国, CN202311398120.7. 2024-01-05.
20. Laferrière-Langlois P, Morisson L, Jeffries S, et al. Depth of anesthesia and nociception monitoring: current state and vision for 2050. Anesth Analg, 2024, 138(2): 295-307.
21. Tu Z, Jeffries S, Pelletier E, et al. Deep reinforcement learning for multi-targets propofol dosing. J Clin Monit Comput, 2025, 39(3): 613-623.
22. 佘守章, 俞卫锋. 加强临床监测智能化研究提升患者围麻醉手术期安全性. 中华麻醉学杂志, 2023, 43(7): 778-780.
23. Hatib F, Jian Z, Buddi S, et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology, 2018, 129(4): 663-674.
24. Wijnberge M, van der Ster BJP, et al. Clinical performance of a machine-learning algorithm to predict intra-operative hypotension with noninvasive arterial pressure waveforms: a cohort study. Eur J Anaesthesiol, 2021, 38(6): 609-615.
25. Hua C, Chu Y, Zhou M, et al. Predictive effect of postoperative recovery in general anesthesia patients using interpretable models based on swarm intelligence machine learning. Front Physiol, 2025, 16: 1565548.
26. Cascella M, Schiavo D, Cuomo A, et al. Artificial intelligence for automatic pain assessment: research methods and perspectives. Pain Res Manag, 2023, 2023: 6018736.
27. van de Sande D, van Genderen ME, Verhoef C, et al. Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE): an external validation study. Surgery, 2022, 172(2): 663-669.
28. Romero-Brufau S, Wyatt KD, Boyum P, et al. Implementation of artificial intelligence-based clinical decision support to reduce hospital readmissions at a regional hospital. Appl Clin Inform, 2020, 11(4): 570-577.
29. Rajala M, Kääriäinen M, Tanhua A, et al. Patients’ perspectives on postoperative follow-up calls in day surgery: a qualitative study. Scand J Caring Sci, 2025, 39(3): e70077.
30. 孟婧文, 安璐, 郝欣欣, 等. 日间手术随访方式的研究进展. 北京医学, 2023, 45(9): 822-825.
31. Kurtz SM, Higgs GB, Chen Z, et al. Patient perceptions of wearable and smartphone technologies for remote outcome monitoring in patients who have hip osteoarthritis or arthroplasties. J Arthroplasty,2022, 37(7S): S488-S492. e2.
32. Jung SJ, Kim JH, Rhee SJ. Home-based rehabilitation using wearable motion tracker with smart phone application feedback is as effective as conventional self-directed rehabilitation after total knee arthroplasty: randomized controlled trial. BMC Sports Sci Med Rehabil, 2025, 17(1): 370.
33. Gordon AM, Nian P, Baidya J, et al. Randomized controlled studies on smartphone applications and wearable devices for postoperative rehabilitation after total knee arthroplasty: a systematic review. J Arthroplasty, 2025; 40(8): 2204-2213. e1.
34. 董兰, 胡娟娟, 吕君, 等. 智能急诊预检分诊信息化系统实践. 解放军医院管理杂志, 2019, 26(1): 69-71, 93.
35. Ahn S, Jung S, Park JH, et al. Artificial intelligence for predicting shockable rhythm during cardiopulmonary resuscitation: in-hospital setting. Resuscitation, 2024, 202: 110325.

West China Medical Journal

Latest ArticlesApplication progress of artificial intelligence in the standardized management of anesthesia for day surgery

Abstract Full text Figures/Tables Video References Cited by

Format

Content