智能科学与技术学报 ›› 2023, Vol. 5 ›› Issue (2): 234-246.doi: 10.11959/j.issn.2096-6652.202324
赵毅飞1, 申乐1, 叶佩军2, 王静2, 王飞跃2,3
修回日期:
2023-05-30
出版日期:
2023-06-15
发布日期:
2023-06-10
作者简介:
赵毅飞(1993- ),男,博士,中国医学科学院北京协和医院麻醉科临床博士后,主要研究方向为围术期监测及治疗与预后关联建模基金资助:
Yifei ZHAO1, Le SHEN1, Peijun YE2, Jing WANG2, Fei-Yue WANG2,3
Revised:
2023-05-30
Online:
2023-06-15
Published:
2023-06-10
Supported by:
摘要:
基于平行医疗理论提出了平行麻醉理论及方法,该理论旨在通过人工系统建模、计算实验分析及人机交互的平行执行等方式,构建由生物人(医护人员)、机器人(机械自动化设备)及数字人(数字医护)构成的有机统一的智慧型全周期麻醉平台。首先从当前麻醉学科发展的现状及面临的瓶颈入手,从临床麻醉、危机管理、科学研究、教育教学及运营管理等方面构想平行麻醉系统中由生物人、数字人、机器人共同构建的多个智能化场景模式,并分析生物人在每个场景中的作用及三者相辅相成的关系。重点介绍了平行麻醉通过计算实验及虚实结合的执行修正模式对患者医疗安全及医护工作效能的提升与改善。最后通过分析平行麻醉系统构建中的伦理问题,分析自动化与智能设备在麻醉中参与的边界及规定,以“尊重生命、公平透明、高效运转、节省劳力”为系统准则,建立一个减少医护机械化重复劳动、增强智能决策辅助能力、提升精细化治疗水平和管理水平的智慧型全周期麻醉平台。
中图分类号:
赵毅飞, 申乐, 叶佩军, 等. 平行麻醉:从麻醉自动化走向智慧型全周期麻醉平台[J]. 智能科学与技术学报, 2023, 5(2): 234-246.
Yifei ZHAO, Le SHEN, Peijun YE, et al. Parallel anesthesia: from anesthesia automation to intelligent full-cycle anesthesia platform[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(2): 234-246.
[1] | 中国共产党中央委员会,. 中华人民共和国国务院. “健康中国2030”规划纲要[EB/OL].2016-10-25.https://www.gov.cn/zhengce/2016-10/25/content_5124174.htm. 2016. |
CPC Central Committee,. The State Council of PRC. “Healthy China 2030” blueprint[EB/OL].2016-10-25.https://www.gov.cn/zhengce/2016-10/25/content_5124174.htm. 2016. | |
[2] | 中华人民共和国国务院. 国务院关于实施健康中国行动的意见[N]. 人民日报,2019-07-16. |
The State Council of PRC. Guideline to implement the country’s Healthy China initiative[N]. The People’s Daily,2019-07-16. | |
[3] | BELLINI V , GUZZON M , BIGLIARDI B ,et al. Artificial intelligence:a new tool in operating room management.Role of machine learning models in operating room optimization[J]. Journal of medical systems, 2020,44(1): 20. |
[4] | ROBINSON D H , TOLEDO A H . Historical development of modern anesthesia[J]. Journal of Investigative Surgery, 2012,25(3): 141-149. |
[5] | CASAS-ARROYAVE F D , FERNáNDEZ J M , ZULETA-TOBóN J J . Evaluation of a closed-loop intravenous total anesthesia delivery system with BIS monitoring compared to an open-loop target-controlled infusion (TCI) system:randomized controlled clinical trial[J]. Colombian Journal of Anesthesiology, 2019,47(2): 84-91. |
[6] | YANG L , ZHU T , JIA-JIN L , ,et al. Anesthesia workforce and workload in China:a national survey[J]. Journal of Anesthesia and Perioperative Medicine (JAPM), 2017,4(2): 67. |
[7] | 王飞跃 . 平行系统方法与复杂系统的管理和控制[J]. 控制与决策, 2004,19(5): 485-489,514. |
WANG F Y . Parallel system methods for management and control of complex systems[J]. Control and Decision, 2004,19(5): 485-489,514. | |
[8] | 王飞跃 . 平行管理:复杂性管理智能的生态科技与智慧管理之DAO[J]. 自动化学报, 2022,48(11): 2655-2669. |
WANG F Y . Parallel management:the DAO to smart ecological technology for complexity management intelligence[J]. Acta Automatica Sinica, 2022,48(11): 2655-2669. | |
[9] | WANG S , WANG J , WANG X ,et al. Blockchain-powered parallel healthcare systems based on the ACP approach[J]. IEEE Transactions on Computational Social Systems, 2018,5(4): 942-950. |
[10] | 王飞跃 . 数字医生与平行医疗:从医疗知识自动化到系统化智能医学[J]. 协和医学杂志, 2021,12(6): 829-833. |
WANG F Y . Digital doctors and parallel healthcare:from medical knowledge automation to intelligent metasystems medicine[J]. Medical Journal of Peking Union Medical College Hospital, 2021,12(6): 829-833. | |
[11] | 王飞跃, 李长贵, 国元元 ,等. 平行高特:基于ACP的平行痛风诊疗系统框架[J]. 模式识别与人工智能, 2017,30(12): 1057-1068. |
WANG F Y , LI C G , GUO Y Y ,et al. Parallel gout:an ACP-based system framework for gout diagnosis and treatment[J]. Pattern Recognition and Artificial Intelligence, 2017,30(12): 1057-1068. | |
[12] | 王飞跃, 苟超, 王建功 ,等. 平行皮肤:基于视觉的皮肤病分析框架[J]. 模式识别与人工智能, 2019,32(7): 577-588. |
WANG F Y , GOU C , WANG J G ,et al. Parallel skin:a vision-based dermatological analysis framework[J]. Pattern Recognition and Artificial Intelligence, 2019,32(7): 577-588. | |
[13] | 张梅, 陈鸰, 王飞跃 ,等. 平行胃肠:基于ACP的智能胃肠疾病诊疗[J]. 模式识别与人工智能, 2019,32(12): 1061-1071. |
ZHANG M , CHEN L , WANG F Y ,et al. Parallel gastrointestine:an ACP-based approach for intelligent operations[J]. Pattern Recognition and Artificial Intelligence, 2019,32(12): 1061-1071. | |
[14] | SHEN T Y , GOU C , WANG J G ,et al. Parallel medical imaging:an ACP-based approach for intelligent medical image recognition with small samples[C]// Proceedings of 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). Piscataway:IEEE Press, 2021: 226-229. |
[15] | 王飞跃 . 平行医学:从医学的温度到智慧的医学[J]. 智能科学与技术学报, 2021,3(1): 1-9. |
WANG F Y . Parallel medicine:from warmness of medicare to medicine of smartness[J]. Chinese Journal of Intelligent Science and Technology, 2021,3(1): 1-9. | |
[16] | 王拥军, 王飞跃, 王戈 ,等. 平行医院:从医院信息管理系统到智慧医院操作系统[J]. 自动化学报, 2021,47(11): 2585-2599. |
WANG Y J , WANG F Y , WANG G ,et al. Parallel hospitals:from hospital information system (HIS)to hospital smart operating system (HSOS)[J]. Acta Automatica Sinica, 2021,47(11): 2585-2599. | |
[17] | WANG F Y . Parallel healthcare:robotic medical and health process automation for secured and smart social healthcares[J]. IEEE Transactions on Computational Social Systems, 2020,7(3): 581-586. |
[18] | WANG F Y , YANG J , WANG X X ,et al. Chat with ChatGPT on industry 5.0:learning and decision-making for intelligent industries[J]. IEEE/CAA Journal of Automatica Sinica, 2023,10(4): 831-834. |
[19] | BHARGAVA H K , MISHRA A N . Electronic medical records and physician productivity:evidence from panel data analysis[J]. Management Science, 2014,60(10): 2543-2562. |
[20] | TAHERI MOGHADAM S , SADOUGHI F , VELAYATI F ,et al. The effects of clinical decision support system for prescribing medication on patient outcomes and physician practice performance:a systematic review and meta-analysis[J]. BMC Medical Informatics and Decision Making, 2021,21(1): 1-26. |
[21] | SHEN Y T , CHEN L , YUE W W ,et al. Artificial intelligence in ultrasound[J]. European Journal of Radiology, 2021,139:109717. |
[22] | ZHOU J , DU M , CHANG S ,et al. Artificial intelligence in echocardiography:detection,functional evaluation,and disease diagnosis[J]. Cardiovascular Ultrasound, 2021,19(1): 1-11. |
[23] | KANG A R , LEE J , JUNG W ,et al. Development of a prediction model for hypotension after induction of anesthesia using machine learning[J]. PLoS One, 2020,15(4): e0231172. |
[24] | VEN W H V D , VEELO D P , WIJNBERGE M ,et al. One of the first validations of an artificial intelligence algorithm for clinical use:the impact on intraoperative hypotension prediction and clinical decisionmaking[J]. Surgery, 2021,169(6): 1300-1303. |
[25] | KOUL A , BAWA R K , KUMAR Y . Artificial intelligence techniques to predict the airway disorders illness:a systematic review[J]. Archives of Computational Methods in Engineering, 2023,30(2): 831-864. |
[26] | Mclennan K M , MILLER A L , DALLA COSTA E ,et al. Conceptual and methodological issues relating to pain assessment in mammals:the development and utilisation of pain facial expression scales[J]. Applied Animal Behaviour Science, 2019,217: 1-15. |
[27] | SETHI N , DUTTA A , PURI G D ,et al. Evaluation of automated delivery of propofol using a closed-loop anesthesia delivery system in patients undergoing thoracic surgery:a randomized controlled study[J]. Journal of Cardiothoracic and Vascular Anesthesia, 2021,35(4):: 1089-1095. |
[28] | XU C , ZHU Y , WU L ,et al. Evaluating the effect of an artificial intelligence system on the anesthesia quality control during gastrointestinal endoscopy with sedation:a randomized controlled trial[J]. BMC anesthesiology, 2022,22(1): 1-11. |
[29] | GOUDRA B , SINGH P M . Failure of Sedasys:destiny or poor design?[J]. Anesthesia & Analgesia, 2017,124(2): 686-688. |
[30] | VANHONACKER D , VERDONCK M , NOGUEIRA CARVALHO H . Impact of closed-loop technology,machine learning,and artificial intelligence on patient safety and the future of anesthesia[J]. Current Anesthesiology Reports, 2022,12(4): 451-460. |
[31] | WINGERT T , LEE C , CANNESSON M . Machine learning,deep learning,and closed loop devices—anesthesia delivery[J]. Anesthesiology Clinics, 2021,39(3): 565-581. |
[32] | KUMAR S , TIWARI P , ZYMBLER M . Internet of things is a revolutionary approach for future technology enhancement:a review[J]. Journal of Big data, 2019,6(1): 1-21. |
[33] | KRICHEN M , MECHTI S , ALROOBAEA R ,et al. A formal testing model for operating room control system using internet of things[J]. Computers,Materials & Continua, 2021,66(3): 2997-3011. |
[34] | USHIMARU Y , TAKAHASHI T , SOUMA Y ,et al. Innovation in surgery/operating room driven by Internet of things on medical devices[J]. Surgical Endoscopy, 2019,33(10): 3469-3477. |
[35] | COPPENS M , VAN CAELENBERG E , DE REGGE M . Postoperative innovative technology for ambulatory anesthesia and surgery[J]. Current Opinion in Anaesthesiology, 2021,34(6): 709-713. |
[36] | JIN Z , LEE C , ZHANG K ,et al. Utilization of wearable pedometer devices in the perioperative period:a qualitative systematic review[J]. Anesthesia & Analgesia, 2023,136(4): 646-654. |
[37] | BURDEN A . The history of crises and crisis management in anesthesia:prevention,detection,and recovery[J]. International Anesthesiology Clinics, 2020,58(1): 2-6. |
[38] | WILCOCKS K , KAPRALOS B , QUEVEDO A U ,et al. The anesthesia crisis scenario builder for authoring anesthesia crisis-based simulations[J]. IEEE Transactions on Games, 2020,12(4): 361-366. |
[39] | ANDRAS I , MAZZONE E , VAN LEEUWEN F W B ,et al. Artificial intelligence and robotics:a combination that is changing the operating room[J]. World Journal of Urology, 2020,38(10): 2359-2366. |
[40] | HASHIMOTO D A , WITKOWSKI E , GAO L ,et al. Artificial intelligence in anesthesiology:current techniques,clinical applications,and limitations[J]. Anesthesiology, 2020,132(2): 379-394. |
[41] | GORDON L , GRANTCHAROV T , RUDZICZ F . Explainable artificial intelligence for safe intraoperative decision support[J]. JAMA Surgery, 2019,154(11): 1064-1065. |
[42] | OH A S . Development of a smart supply-chain management solution based on logistics standards utilizing artificial intelligence and the internet of things[J]. Journal of Information and Communication Convergence Engineering, 2019,17(3): 198-204. |
[43] | BATES D W , LEVINE D , SYROWATKA A ,et al. The potential of artificial intelligence to improve patient safety:a scoping review[J]. NPJ Digital Medicine, 2021,4(1): 54. |
[44] | LEE D H , YOON S N . Application of artificial intelligence-based technologies in the healthcare industry:opportunities and challenges[J]. International Journal of Environmental Research and Public Health, 2021,18(1): 271. |
[45] | WOLFF J , PAULING J , KECK A ,et al. The economic impact of artificial intelligence in health care:systematic review[J]. Journal of Medical Internet Research, 2020,22(2): e16866. |
[46] | ABDELDAYEM M M , ALDULAIMI S H . Trends and opportunities of artificial intelligence in human resource management:aspirations for public sector in Bahrain[J]. International Journal of Scientific and Technology Research, 2020,9(1): 3867-3871. |
[47] | ACHCHAB S , TEMSAMANI Y K . Artificial intelligence use in human resources management:strategy and operation’s impact[C]// Proceedings of 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML). Piscataway:IEEE Press, 2021: 311-315. |
[48] | BOGGS S D , LUEDI M M . Nonoperating room anesthesia education:preparing our residents for the future[J]. Current Opinion in Anesthesiology, 2019,32(4): 490-497. |
[49] | BOWNESS J , VARSOU O , TURBITT L ,et al. Identifying anatomical structures on ultrasound:assistive artificial intelligence in ultrasoundguided regional anesthesia[J]. Clinical Anatomy, 2021,34(5): 802-809. |
[50] | RIGBY M J . Ethical dimensions of using artificial intelligence in health care[J]. AMA Journal of Ethics, 2019,21(2): 121-124. |
[51] | World Health Organization. Ethics and governance of artificial intelligence for health:WHO guidance[R]. Geneva:World Health Organization. 2021. |
[52] | GERKE S , MINSSEN T , COHEN G . Ethical and legal challenges of artificial intelligence-driven healthcare[M]// Artificial Intelligence in Healthcare. Amsterdam: Elsevier, 2020: 295-336. |
[53] | AMANN J , BLASIMME A , VAYENA E ,et al. Explainability for artificial intelligence in healthcare:a multidisciplinary perspective[J]. BMC Medical Informatics and Decision Making, 2020,20(1): 1-9. |
[54] | CHAR D S , ABRàMOFF M D , FEUDTNER C . Identifying ethical considerations for machine learning healthcare applications[J]. The American Journal of Bioethics, 2020,20(11): 7-17. |
[55] | FISKE A , HENNINGSEN P , BUYX A . Your robot therapist will see you now:ethical implications of embodied artificial intelligence in psychiatry,psychology,and psychotherapy[J]. Journal of Medical Internet Research, 2019,21(5): e13216. |
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