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：

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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.	中華醫學會麻醉學分會. 日間手術麻醉指南. 中華醫學雜志, 2023, 103(43): 3462-3471.
2.	孫德峰. 日間手術麻醉規范化管理策略. 實用醫學雜志, 2024, 40(3): 283-288.
3.	光奇, 王建林, 王康太, 等. 基于交互設計信息系統的智能化日間手術全流程管理探索. 華西醫學, 2023, 38(9): 1380-1385.
4.	嚴蓉, 高巨, 束余聲, 等. 麻醉科門診建設與管理: 實踐與思考. 中華麻醉學雜志, 2022, 42(3): 358-361.
5.	田夢, 孫忠良, 孫德峰. 日歸手術麻醉的規范化管理. 華西醫學, 2022, 37(2): 165-170.
6.	蔣麗莎, 李文暢, 馬洪升. 基于異構數據學習的日間腹腔鏡膽囊切除術患者準入模型探索. 華西醫學, 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.	胡小義, 王迪, 紀木火, 等. 機器學習在麻醉學領域的應用前景. 臨床麻醉學雜志, 2024, 40(6): 634-638.
10.	楊展眉, 唐之音, 劉洪濤. 人工智能在預測困難氣道方面的研究進展. 中國醫藥, 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, 柏斗勝, 王霄霖, 等. 基于智慧信息大數據平臺助推全院日間手術服務高質量發展. 華西醫學, 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.	吳飛, 張冉, 孫衛格, 等. 遠程健康教育在日間手術患者中的應用研究進展. 中國衛生質量管理, 2024, 31(9): 43-47.
19.	武漢大學人民醫院(湖北省人民醫院). 人工智能的個性化內鏡檢查預約兼術前宣教方法及系統: 中國, 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, 俞衛鋒. 加強臨床監測智能化研究提升患者圍麻醉手術期安全性. 中華麻醉學雜志, 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, K??ri?inen 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. 日間手術隨訪方式的研究進展. 北京醫學, 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.	董蘭, 胡娟娟, 呂君, 等. 智能急診預檢分診信息化系統實踐. 解放軍醫院管理雜志, 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. 中華醫學會麻醉學分會. 日間手術麻醉指南. 中華醫學雜志, 2023, 103(43): 3462-3471.
2. 孫德峰. 日間手術麻醉規范化管理策略. 實用醫學雜志, 2024, 40(3): 283-288.
3. 光奇, 王建林, 王康太, 等. 基于交互設計信息系統的智能化日間手術全流程管理探索. 華西醫學, 2023, 38(9): 1380-1385.
4. 嚴蓉, 高巨, 束余聲, 等. 麻醉科門診建設與管理: 實踐與思考. 中華麻醉學雜志, 2022, 42(3): 358-361.
5. 田夢, 孫忠良, 孫德峰. 日歸手術麻醉的規范化管理. 華西醫學, 2022, 37(2): 165-170.
6. 蔣麗莎, 李文暢, 馬洪升. 基于異構數據學習的日間腹腔鏡膽囊切除術患者準入模型探索. 華西醫學, 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. 胡小義, 王迪, 紀木火, 等. 機器學習在麻醉學領域的應用前景. 臨床麻醉學雜志, 2024, 40(6): 634-638.
10. 楊展眉, 唐之音, 劉洪濤. 人工智能在預測困難氣道方面的研究進展. 中國醫藥, 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, 柏斗勝, 王霄霖, 等. 基于智慧信息大數據平臺助推全院日間手術服務高質量發展. 華西醫學, 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. 吳飛, 張冉, 孫衛格, 等. 遠程健康教育在日間手術患者中的應用研究進展. 中國衛生質量管理, 2024, 31(9): 43-47.
19. 武漢大學人民醫院(湖北省人民醫院). 人工智能的個性化內鏡檢查預約兼術前宣教方法及系統: 中國, 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, 俞衛鋒. 加強臨床監測智能化研究提升患者圍麻醉手術期安全性. 中華麻醉學雜志, 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, K??ri?inen 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. 日間手術隨訪方式的研究進展. 北京醫學, 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. 董蘭, 胡娟娟, 呂君, 等. 智能急診預檢分診信息化系統實踐. 解放軍醫院管理雜志, 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

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