Exploration and practice of high-quality development in emergency medicine under the health-prioritized development strategy_West China Medical Journal

Authors：

CHEN Yao ^1,2 , WU Tingting ^1,2 , HE Yarong ^1,2 ,  CAO Yu ^1,2

1. Department of Emergency Medicine, Institute of Disaster Medicine and Institute of Emergency Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Disaster Medical Center, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding?author：

CAO Yu, Email: caoyu@wchscu.cn

Keywords：

Health-prioritized development strategy; emergency medicine; high-quality development

DOI：

10.7507/1002-0179.202511134

Video：

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Abstract Full text Figures/Tables Video References Cited by

China’s “15th Five-Year Plan” explicitly proposes the implementation of a health-prioritized development strategy, aiming to comprehensively enhance emergency medical care and response capabilities. In this context, the development of emergency medicine will face new opportunities and challenges. In order to provide theoretical and practical references for the high-quality development of emergency medicine, this paper analyzes the shortcomings in the identification and early warning of critical illnesses, the timeliness of emergency care, and emergency response capabilities in the development of emergency medicine in China. It reviews current practices with cutting-edge technologies in innovating diagnostic and monitoring techniques and equipment, optimizing and integrating resources, promoting interdisciplinary collaboration, and leveraging the complementary advantages of traditional Chinese and Western medicine. Furthermore, it outlines future development directions.

Citation： CHEN Yao, WU Tingting, HE Yarong, CAO Yu. Exploration and practice of high-quality development in emergency medicine under the health-prioritized development strategy. West China Medical Journal, 2025, 40(11): 1713-1719. doi: 10.7507/1002-0179.202511134 Copy

1.	中國共產黨第二十屆中央委員會. 中共中央關于制定國民經濟和社會發展第十五個五年規劃的建議. 人民日報, 2025-10-29(1).
2.	中國高血壓防治指南修訂委員會, 高血壓聯盟(中國), 中國醫療保健國際交流促進會高血壓分會, 等. 中國高血壓防治指南(2024 年修訂版). 中華高血壓雜志, 2024, 32(7): 603-700.
3.	World Health Organization. Noncommunicable diseases-progress monitor 2025; and assessing national capacity for NCD prevention and control (2023 global survey). Geneva: World Health Organization, 2025.
4.	Beasant B, Anderson K, Lee G, et al. Health literacy and primary prevention of cardiovascular disease: a scoping review. Public Health Rep, 2025, 140(4): 342-357.
5.	Li X, Krumholz HM, Yip W, et al. Quality of primary health care in China: challenges and recommendations. Lancet, 2020, 395(10239): 1802-1812.
6.	Zaboli A, Ziller M, Brigo F, et al. The impact of chronic conditions on emergency department length of stay. Intern Emerg Med, 2025: 11.
7.	Saab A, Abi Khalil C, Jammal M, et al. Early prediction of all-cause clinical deterioration in general wards patients: development and validation of a biomarker-based machine learning model derived from rapid response team activations. J Patient Saf, 2022, 18(6): 578-586.
8.	Smith GB, Prytherch DR, Meredith P, et al. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation, 2013, 84(4): 465-470.
9.	Cvach M. Monitor alarm fatigue: an integrative review. Biomed Instrum Technol, 2012, 46(4): 268-277.
10.	Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation, 2016, 102: 1-5.
11.	彭希, 石澤亞, 陳華麗, 等. 湖南省公眾現場救護參與行為現狀及影響因素分析. 現代醫學, 2023, 51(3): 366-371.
12.	中國老年保健協會第一目擊者現場救護專業委員會. 中國現場救護第一目擊者行動公眾指南(2023). 中華危重病急救醫學, 2023, 35(2): 113-123.
13.	岳清春. 我國應急救援多元協同機制研究. 中國應急救援, 2016(3): 26-28.
14.	Ong MEH, Perkins GD, Cariou A. Out-of-hospital cardiac arrest: prehospital management. Lancet, 2018, 391(10124): 980-988.
15.	周書鐸, 金音子, 鄭志杰. 急救醫療體系運行機制優化研究進展及啟示. 中國醫院管理, 2020, 40(11): 52-54.
16.	汪思妤, 李海霞, 傅睿, 等. 中西醫結合心肺復蘇新模式的思考. 中西醫結合心腦血管病雜志, 2025, 23(20): 3192-3196.
17.	趙曉東, 于學忠. 改變中國急診不均衡現狀“共融共享共贏共發展”的中國急診專科醫聯體正在走來. 中國急救醫學, 2019, 39(9): 815-818.
18.	吳焱斌. 習近平關于健康中國論述的理論意蘊、內在邏輯與實踐創新. 衛生法學, 2025, 33(6): 132-143.
19.	中共中央黨史和文獻研究院. 習近平關于健康中國論述摘編. 北京: 中央文獻出版社, 2024: 19.
20.	Kruk ME, Gage AD, Arsenault C, et al. High-quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob Health, 2018, 6(11): e1196-e1252.
21.	Komorowski M, Celi LA. The artificial intelligence clinician: a reinforcement learning approach to optimal treatment of sepsis. Crit Care Med, 2018, 46(3): 418-425.
22.	Prasitlumkum N, Cheungpasitporn W, Chokesuwattanaskul A, et al. Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation: a systematic review and meta-analysis. Arch Cardiovasc Dis, 2021, 114(1): 4-16.
23.	Tran HH, Urgessa NA, Geethakumari P, et al. Detection and diagnostic accuracy of cardiac arrhythmias using wearable health devices: a systematic review. Cureus, 2023, 15(12): e50952.
24.	Chowdhury MEH, Alzoubi K, Khandakar A, et al. Wearable real-time heart attack detection and warning system to reduce road accidents. Sensors (Basel), 2019, 19(12): 2780.
25.	Taylor L, Ding X, Clifton D, et al. Wearable vital signs monitoring for patients with asthma: a review. IEEE Sens J, 2020, 23(3): 1734-1751.
26.	Lai C, Yin M, Kholmovski EG, et al. Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy. Nat Cardiovasc Res, 2025, 4(7): 891-903.
27.	Alamgir A, Mousa O, Shah Z. Artificial intelligence in predicting cardiac arrest: scoping review. JMIR Med Inform, 2021, 9(12): e30798.
28.	Cakar N, Senturk E, Guvendi G. Artificial intelligence (AI) to predict sepsis early in patients admitted to intensive care unit (ICU). Brussels: ISICEM, 2025: 222.
29.	Yadgarov MY, Landoni G, Berikashvili L, et al. Comparing machine learning and traditional scoring systems for early sepsis detection: a network meta-analysis. Brussels: ISICEM, 2025: 251.
30.	Petru?i? I, Chiang CC, Garcia-Azorin D, et al. Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members’ vision - part 2. J Headache Pain, 2025, 26(1): 2.
31.	Leon-Justel A, Navarro Bustos C, Noval-Padillo JA, et al. Point-of-care testing improves care timeliness in the emergency department. A multicenter randomized clinical trial (study POCTUR). Clin Chem Lab Med, 2024, 63(5): 942-951.
32.	Tienpratarn W, Yuksen C, Chukaew L, et al. Point-of-care testing (POCT) for blood gas and electrolyte analysis in out-of-hospital cardiac arrests’ management; a cross-sectional study. Arch Acad Emerg Med, 2025, 13(1): e32.
33.	Meltzer AC, Payette C, Heidish R, et al. Point-of-care respiratory diagnosis and antibiotic utilization in the emergency department: a prospective evaluation of multiplex PCR. Acad Emerg Med, 2025: 10.
34.	Zheng B, Li Y, Gu G, et al. Comparing 5G mobile stroke unit and emergency medical service in patients acute ischemic stroke eligible for t-PA treatment: a prospective, single-center clinical trial in Ya’an, China. Brain Behav, 2023, 13(11): e3231.
35.	Alexandrov AW, Arthur AS, Bryndziar T, et al. High-resolution CT with arch/neck/head CT angiography on a mobile stroke unit. J Neurointerv Surg, 2022, 14(6): 623-627.
36.	Roberts DR, McGeorge T, Abrams D, et al. Mobile point-of-care MRI demonstration of a normal volunteer in a telemedicine-equipped ambulance. J Stroke Cerebrovasc Dis, 2023, 32(10): 107301.
37.	魏秀麗, 趙聆宇, 杜雙. 騰訊“5 分鐘社會救援圈”: 科技賦能下的可持續社會價值創新實踐. 清華管理評論, 2025(9): 93-100.
38.	Xie P, Deng L, Ma Y, et al. EV-Call 120: a new-generation emergency medical service system in China. J Transl Int Med, 2024, 12(2): 209-212.
39.	夏偉, 常偉, 王鴻, 等. 泰州市“上車即入院”智慧急救新模式建設與實踐. 江蘇衛生事業管理, 2024, 35(10): 1511-1514.
40.	張遠林, 李遠杉, 盧熙, 等. “上車即入院”智能化急診急救系統的實施與探索. 實用休克雜志(中英文), 2022, 6(4): 227-230.
41.	Barbieri G, Cipriano A, Coccolini F, et al. Trauma center model application in the university hospital of Pisa: a single-center comparative study. Intern Emerg Med, 2025, 20(1): 267-272.
42.	楊寧雨, 謝海香, 曹清蓮, 等. 數字化平臺聯合多學科協作診療模式在嚴重創傷患者救治中的應用效果. 重慶醫學, 2023, 52(增刊 1): 104-107.
43.	劉暢, 肖鐳, 荊琳, 等. 區域性創傷救治體系中鄭州市“1 小時創傷急救圈”模式初步探索. 中華創傷雜志, 2022, 38(4): 354-358.
44.	蘇州工業園區東方華夏心血管健康研究院, 國家放射與治療臨床醫學研究中心胸痛中心專家委員會, 霍勇, 等. 《胸痛中心質控報告(2024)》概要. 中國介入心臟病學雜志, 2025, 33(7): 361-367.
45.	Sharma S, Rawal R, Shah D. Addressing the challenges of AI-based telemedicine: best practices and lessons learned. J Educ Health Promot, 2023, 12: 338.
46.	Rossi M, Rehman S. Integrating artificial intelligence into telemedicine: evidence, challenges, and future directions. Cureus, 2025, 17(8): e90829.
47.	Panlaqui OM, Broadfield E, Champion R, et al. Outcomes of telemedicine intervention in a regional intensive care unit: a before and after study. Anaesth Intensive Care, 2017, 45(5): 605-610.
48.	Watanabe T, Ohsugi K, Suminaga Y, et al. An evaluation of the impact of the implementation of the tele-ICU: a retrospective observational study. J Intensive Care, 2023, 11(1): 9.
49.	Saini B, Singh D, Sharma KC, et al. AI-enhanced telemedicine: transforming resource allocation and cost-efficiency analysis via advanced queueing model. Sci Rep, 2025, 15(1): 31551.
50.	Berikol GB, Kanbakan A, Ilhan B, et al. Mapping artificial intelligence models in emergency medicine: a scoping review on artificial intelligence performance in emergency care and education. Turk J Emerg Med, 2025, 25(2): 67-91.
51.	Hu Y, Xiang Y, Zhou YJ, et al. AI-based diagnosis of acute aortic syndrome from noncontrast CT. Nat Med, 2025, 31(11): 3832-3844.
52.	Yahav-Dovrat A, Saban M, Merhav G, et al. Evaluation of artificial intelligence-powered identification of large-vessel occlusions in a comprehensive stroke center. AJNR Am J Neuroradiol, 2021, 42(2): 247-254.
53.	Guermazi A, Tannoury C, Kompel AJ, et al. Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology, 2022, 302(3): 627-636.
54.	Hinson JS, Taylor RA, Venkatesh A, et al. Accelerated chest pain treatment with artificial intelligence-informed, risk-driven triage. JAMA Intern Med, 2024, 184(9): 1125-1127.
55.	Lu SC, Chen GY, Liu AS, et al. Deep learning-based electrocardiogram model (EIANet) to predict emergency department cardiac arrest: development and external validation study. J Med Internet Res, 2025, 27: e67576.
56.	楊烽濤, 馬連韜, 余劍波, 等. 人工智能在急診醫學中的應用進展. 中國現代醫學雜志, 2025, 35(14): 38-43.
57.	Strodthoff N, Strodthoff C. Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol Meas, 2019, 40(1): 015001.
58.	Düvel JA, Lampe D, Kirchner M, et al. An AI-based clinical decision support system for antibiotic therapy in sepsis (KINBIOTICS): use case analysis. JMIR Hum Factors, 2025, 12: e66699.
59.	Schaut M, Schaefer M, Trost U, et al. Integrated antibiotic clinical decision support system (CDSS) for appropriate choice and dosage: an analysis of retrospective data. Germs, 2022, 12(2): 203-213.
60.	陳香美, 王聰慧, 張勤修, 等. 面向未來, 高質量發展中西醫結合. 四川大學學報(醫學版), 2025, 56(1): 1-4.
61.	李志軍. 天津特色的中西醫結合急救醫學體系. 中華危重病急救醫學, 2018, 30(5): 401-404.
62.	顧亞楠, 竇清理, 張文武, 等. 寶安公眾急救培訓模式的實踐——基于第一響應人按需培訓的研究. 中國急救醫學, 2022, 42(4): 303-306.
63.	胡盛壽. 實施健康優先發展戰略, 推動生活方式醫學發展. 中國循環雜志, 2025, 40(1): 1-5.
64.	余偉, 徐月萌, 艾麗, 等. 科教融創的醫工復合型人才培養探索與實踐. 華西醫學, 2025, 40(10): 1658-1662.
65.	Plotzky C, Lindwedel U, Sorber M, et al. Virtual reality simulations in nurse education: a systematic mapping review. Nurse Educ Today, 2021, 101: 104868.

1. 中國共產黨第二十屆中央委員會. 中共中央關于制定國民經濟和社會發展第十五個五年規劃的建議. 人民日報, 2025-10-29(1).
2. 中國高血壓防治指南修訂委員會, 高血壓聯盟(中國), 中國醫療保健國際交流促進會高血壓分會, 等. 中國高血壓防治指南(2024 年修訂版). 中華高血壓雜志, 2024, 32(7): 603-700.
3. World Health Organization. Noncommunicable diseases-progress monitor 2025; and assessing national capacity for NCD prevention and control (2023 global survey). Geneva: World Health Organization, 2025.
4. Beasant B, Anderson K, Lee G, et al. Health literacy and primary prevention of cardiovascular disease: a scoping review. Public Health Rep, 2025, 140(4): 342-357.
5. Li X, Krumholz HM, Yip W, et al. Quality of primary health care in China: challenges and recommendations. Lancet, 2020, 395(10239): 1802-1812.
6. Zaboli A, Ziller M, Brigo F, et al. The impact of chronic conditions on emergency department length of stay. Intern Emerg Med, 2025: 11.
7. Saab A, Abi Khalil C, Jammal M, et al. Early prediction of all-cause clinical deterioration in general wards patients: development and validation of a biomarker-based machine learning model derived from rapid response team activations. J Patient Saf, 2022, 18(6): 578-586.
8. Smith GB, Prytherch DR, Meredith P, et al. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation, 2013, 84(4): 465-470.
9. Cvach M. Monitor alarm fatigue: an integrative review. Biomed Instrum Technol, 2012, 46(4): 268-277.
10. Churpek MM, Adhikari R, Edelson DP. The value of vital sign trends for detecting clinical deterioration on the wards. Resuscitation, 2016, 102: 1-5.
11. 彭希, 石澤亞, 陳華麗, 等. 湖南省公眾現場救護參與行為現狀及影響因素分析. 現代醫學, 2023, 51(3): 366-371.
12. 中國老年保健協會第一目擊者現場救護專業委員會. 中國現場救護第一目擊者行動公眾指南(2023). 中華危重病急救醫學, 2023, 35(2): 113-123.
13. 岳清春. 我國應急救援多元協同機制研究. 中國應急救援, 2016(3): 26-28.
14. Ong MEH, Perkins GD, Cariou A. Out-of-hospital cardiac arrest: prehospital management. Lancet, 2018, 391(10124): 980-988.
15. 周書鐸, 金音子, 鄭志杰. 急救醫療體系運行機制優化研究進展及啟示. 中國醫院管理, 2020, 40(11): 52-54.
16. 汪思妤, 李海霞, 傅睿, 等. 中西醫結合心肺復蘇新模式的思考. 中西醫結合心腦血管病雜志, 2025, 23(20): 3192-3196.
17. 趙曉東, 于學忠. 改變中國急診不均衡現狀“共融共享共贏共發展”的中國急診專科醫聯體正在走來. 中國急救醫學, 2019, 39(9): 815-818.
18. 吳焱斌. 習近平關于健康中國論述的理論意蘊、內在邏輯與實踐創新. 衛生法學, 2025, 33(6): 132-143.
19. 中共中央黨史和文獻研究院. 習近平關于健康中國論述摘編. 北京: 中央文獻出版社, 2024: 19.
20. Kruk ME, Gage AD, Arsenault C, et al. High-quality health systems in the sustainable development goals era: time for a revolution. Lancet Glob Health, 2018, 6(11): e1196-e1252.
21. Komorowski M, Celi LA. The artificial intelligence clinician: a reinforcement learning approach to optimal treatment of sepsis. Crit Care Med, 2018, 46(3): 418-425.
22. Prasitlumkum N, Cheungpasitporn W, Chokesuwattanaskul A, et al. Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation: a systematic review and meta-analysis. Arch Cardiovasc Dis, 2021, 114(1): 4-16.
23. Tran HH, Urgessa NA, Geethakumari P, et al. Detection and diagnostic accuracy of cardiac arrhythmias using wearable health devices: a systematic review. Cureus, 2023, 15(12): e50952.
24. Chowdhury MEH, Alzoubi K, Khandakar A, et al. Wearable real-time heart attack detection and warning system to reduce road accidents. Sensors (Basel), 2019, 19(12): 2780.
25. Taylor L, Ding X, Clifton D, et al. Wearable vital signs monitoring for patients with asthma: a review. IEEE Sens J, 2020, 23(3): 1734-1751.
26. Lai C, Yin M, Kholmovski EG, et al. Multimodal AI to forecast arrhythmic death in hypertrophic cardiomyopathy. Nat Cardiovasc Res, 2025, 4(7): 891-903.
27. Alamgir A, Mousa O, Shah Z. Artificial intelligence in predicting cardiac arrest: scoping review. JMIR Med Inform, 2021, 9(12): e30798.
28. Cakar N, Senturk E, Guvendi G. Artificial intelligence (AI) to predict sepsis early in patients admitted to intensive care unit (ICU). Brussels: ISICEM, 2025: 222.
29. Yadgarov MY, Landoni G, Berikashvili L, et al. Comparing machine learning and traditional scoring systems for early sepsis detection: a network meta-analysis. Brussels: ISICEM, 2025: 251.
30. Petru?i? I, Chiang CC, Garcia-Azorin D, et al. Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members’ vision - part 2. J Headache Pain, 2025, 26(1): 2.
31. Leon-Justel A, Navarro Bustos C, Noval-Padillo JA, et al. Point-of-care testing improves care timeliness in the emergency department. A multicenter randomized clinical trial (study POCTUR). Clin Chem Lab Med, 2024, 63(5): 942-951.
32. Tienpratarn W, Yuksen C, Chukaew L, et al. Point-of-care testing (POCT) for blood gas and electrolyte analysis in out-of-hospital cardiac arrests’ management; a cross-sectional study. Arch Acad Emerg Med, 2025, 13(1): e32.
33. Meltzer AC, Payette C, Heidish R, et al. Point-of-care respiratory diagnosis and antibiotic utilization in the emergency department: a prospective evaluation of multiplex PCR. Acad Emerg Med, 2025: 10.
34. Zheng B, Li Y, Gu G, et al. Comparing 5G mobile stroke unit and emergency medical service in patients acute ischemic stroke eligible for t-PA treatment: a prospective, single-center clinical trial in Ya’an, China. Brain Behav, 2023, 13(11): e3231.
35. Alexandrov AW, Arthur AS, Bryndziar T, et al. High-resolution CT with arch/neck/head CT angiography on a mobile stroke unit. J Neurointerv Surg, 2022, 14(6): 623-627.
36. Roberts DR, McGeorge T, Abrams D, et al. Mobile point-of-care MRI demonstration of a normal volunteer in a telemedicine-equipped ambulance. J Stroke Cerebrovasc Dis, 2023, 32(10): 107301.
37. 魏秀麗, 趙聆宇, 杜雙. 騰訊“5 分鐘社會救援圈”: 科技賦能下的可持續社會價值創新實踐. 清華管理評論, 2025(9): 93-100.
38. Xie P, Deng L, Ma Y, et al. EV-Call 120: a new-generation emergency medical service system in China. J Transl Int Med, 2024, 12(2): 209-212.
39. 夏偉, 常偉, 王鴻, 等. 泰州市“上車即入院”智慧急救新模式建設與實踐. 江蘇衛生事業管理, 2024, 35(10): 1511-1514.
40. 張遠林, 李遠杉, 盧熙, 等. “上車即入院”智能化急診急救系統的實施與探索. 實用休克雜志(中英文), 2022, 6(4): 227-230.
41. Barbieri G, Cipriano A, Coccolini F, et al. Trauma center model application in the university hospital of Pisa: a single-center comparative study. Intern Emerg Med, 2025, 20(1): 267-272.
42. 楊寧雨, 謝海香, 曹清蓮, 等. 數字化平臺聯合多學科協作診療模式在嚴重創傷患者救治中的應用效果. 重慶醫學, 2023, 52(增刊 1): 104-107.
43. 劉暢, 肖鐳, 荊琳, 等. 區域性創傷救治體系中鄭州市“1 小時創傷急救圈”模式初步探索. 中華創傷雜志, 2022, 38(4): 354-358.
44. 蘇州工業園區東方華夏心血管健康研究院, 國家放射與治療臨床醫學研究中心胸痛中心專家委員會, 霍勇, 等. 《胸痛中心質控報告(2024)》概要. 中國介入心臟病學雜志, 2025, 33(7): 361-367.
45. Sharma S, Rawal R, Shah D. Addressing the challenges of AI-based telemedicine: best practices and lessons learned. J Educ Health Promot, 2023, 12: 338.
46. Rossi M, Rehman S. Integrating artificial intelligence into telemedicine: evidence, challenges, and future directions. Cureus, 2025, 17(8): e90829.
47. Panlaqui OM, Broadfield E, Champion R, et al. Outcomes of telemedicine intervention in a regional intensive care unit: a before and after study. Anaesth Intensive Care, 2017, 45(5): 605-610.
48. Watanabe T, Ohsugi K, Suminaga Y, et al. An evaluation of the impact of the implementation of the tele-ICU: a retrospective observational study. J Intensive Care, 2023, 11(1): 9.
49. Saini B, Singh D, Sharma KC, et al. AI-enhanced telemedicine: transforming resource allocation and cost-efficiency analysis via advanced queueing model. Sci Rep, 2025, 15(1): 31551.
50. Berikol GB, Kanbakan A, Ilhan B, et al. Mapping artificial intelligence models in emergency medicine: a scoping review on artificial intelligence performance in emergency care and education. Turk J Emerg Med, 2025, 25(2): 67-91.
51. Hu Y, Xiang Y, Zhou YJ, et al. AI-based diagnosis of acute aortic syndrome from noncontrast CT. Nat Med, 2025, 31(11): 3832-3844.
52. Yahav-Dovrat A, Saban M, Merhav G, et al. Evaluation of artificial intelligence-powered identification of large-vessel occlusions in a comprehensive stroke center. AJNR Am J Neuroradiol, 2021, 42(2): 247-254.
53. Guermazi A, Tannoury C, Kompel AJ, et al. Improving radiographic fracture recognition performance and efficiency using artificial intelligence. Radiology, 2022, 302(3): 627-636.
54. Hinson JS, Taylor RA, Venkatesh A, et al. Accelerated chest pain treatment with artificial intelligence-informed, risk-driven triage. JAMA Intern Med, 2024, 184(9): 1125-1127.
55. Lu SC, Chen GY, Liu AS, et al. Deep learning-based electrocardiogram model (EIANet) to predict emergency department cardiac arrest: development and external validation study. J Med Internet Res, 2025, 27: e67576.
56. 楊烽濤, 馬連韜, 余劍波, 等. 人工智能在急診醫學中的應用進展. 中國現代醫學雜志, 2025, 35(14): 38-43.
57. Strodthoff N, Strodthoff C. Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiol Meas, 2019, 40(1): 015001.
58. Düvel JA, Lampe D, Kirchner M, et al. An AI-based clinical decision support system for antibiotic therapy in sepsis (KINBIOTICS): use case analysis. JMIR Hum Factors, 2025, 12: e66699.
59. Schaut M, Schaefer M, Trost U, et al. Integrated antibiotic clinical decision support system (CDSS) for appropriate choice and dosage: an analysis of retrospective data. Germs, 2022, 12(2): 203-213.
60. 陳香美, 王聰慧, 張勤修, 等. 面向未來, 高質量發展中西醫結合. 四川大學學報(醫學版), 2025, 56(1): 1-4.
61. 李志軍. 天津特色的中西醫結合急救醫學體系. 中華危重病急救醫學, 2018, 30(5): 401-404.
62. 顧亞楠, 竇清理, 張文武, 等. 寶安公眾急救培訓模式的實踐——基于第一響應人按需培訓的研究. 中國急救醫學, 2022, 42(4): 303-306.
63. 胡盛壽. 實施健康優先發展戰略, 推動生活方式醫學發展. 中國循環雜志, 2025, 40(1): 1-5.
64. 余偉, 徐月萌, 艾麗, 等. 科教融創的醫工復合型人才培養探索與實踐. 華西醫學, 2025, 40(10): 1658-1662.
65. Plotzky C, Lindwedel U, Sorber M, et al. Virtual reality simulations in nurse education: a systematic mapping review. Nurse Educ Today, 2021, 101: 104868.

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