دوره 8، شماره 3 - ( 1402 )                   دوره 8 شماره 3 صفحات 156-145 | برگشت به فهرست نسخه ها

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Saremi A, Abbasi B, Karimi-MansoorAbad E, Ashourian Y. Revolutionizing Emergency Medicine: A Comprehensive Review of Artificial Intelligence Applications and Their Impact. SJMR 2023; 8 (3) : 2
URL: http://saremjrm.com/article-1-304-fa.html
صارمی ابوطالب، عباسی بهاره، کریمی منصور آباد الهام، عاشوریان یاسین. انقلابی در اورژانس پزشکی: مروری جامع بر کاربردهای هوش مصنوعی و تأثیر آنها. مجله تحقيقات پزشكي صارم. 1402; 8 (3) :145-156

URL: http://saremjrm.com/article-1-304-fa.html

انقلابی در اورژانس پزشکی: مروری جامع بر کاربردهای هوش مصنوعی و تأثیر آنها
ابوطالب صارمی1 ، بهاره عباسی*2 ، الهام کریمی منصور آباد1 ، یاسین عاشوریان1
1- مرکز تحقیقات زنان، زایمان و ناباروری صارم، بیمارستان فوق تخصصی صارم، دانشگاه علوم پزشکی ایران (IUMS)، تهران، ایران. و مرکز تحقیقات سلولی- مولکولی و سلول‌های بنیادی صارم (SCRC)، بیمارستان فوق تخصصی صارم، تهران، ایران.
2- دپارتمان ژنتیک پزشکی، موسسه ملی مهندسی ژنتیک و بیوتکنولوژی (NIGEB)، تهران، ایران.
چکیده:   (2582 مشاهده)
ادغام هوش مصنوعی (AI) در حوزه پزشکی اورژانس، عصر تحول در مراقبت‌های بهداشتی را آغاز کرده است. این بررسی مروری جامع از تأثیر هوش مصنوعی بر طب اورژانس شامل: تحولات تاریخی، برنامه‌های فعلی و چشم‌اندازهای آینده ارائه می‌کند. فناوری‌های هوش مصنوعی مانند: یادگیری ماشینی، پردازش زبان طبیعی، و بینایی رایانه‌ای، شیوه عملکرد بخش‌های اورژانس را متحول می‌کنند. از تریاژ سریع بیمار و تشخیص زودهنگام گرفته تا تصمیم‌گیری آگاهانه و تخصیص منابع، هوش مصنوعی مراقبت از بیمار را بهبود می‌بخشد و گردش کار بیمارستان را ساده می‌کند. در حالی که هوش مصنوعی مزایای بسیار زیادی را ارائه می‌دهد، چالش‌هایی نیز در رابطه با حریم خصوصی داده‌ها، تعصب، مقررات و ملاحظات اخلاقی ایجاد می‌کند. از طریق مطالعات موردی در دنیای واقعی و داستان‌های موفقیت، این بررسی مزایای ملموس پذیرش هوش مصنوعی در پزشکی اورژانس را نشان می‌دهد. همانطور که به آینده می‌پردازیم، روندهای نوظهور و شکاف‌های تحقیقاتی بر پتانسیل هوش مصنوعی برای بهینه‌سازی بیشتر ارائه مراقبت‌های بهداشتی اضطراری تاکید می‌کند. هدف این بررسی روشن کردن چشم‌انداز چندوجهی هوش مصنوعی در پزشکی اورژانس است،‌که بر نقش آن به عنوان یک کاتالیزور برای بهبود نتایج بیماران و سیستم‌های مراقبت بهداشتی کارآمدتر تأکید می‌کند.
شماره‌ی مقاله: 2
واژه‌های کلیدی: هوش مصنوعی در اورژانس پزشکی، ‌ هوش مصنوعی، تریاژ، ‌ تشخیص، پشتیبانی تصمیم.
متن کامل [PDF 1044 kb]   (1403 دریافت)    
نوع مقاله: مروری سیستماتيک | موضوع مقاله: بهداشت و ايمني
دریافت: 1402/8/15 | پذیرش: 1403/5/9 | انتشار: 1403/5/9
فهرست منابع
1. Abi-Rafeh J, Mroueh VJ, Bassiri-Tehrani B, Marks J, Kazan R, Nahai F. Complications Following Body Contouring: Performance Validation of Bard, a Novel AI Large Language Model, in Triaging and Managing Postoperative Patient Concerns. Aesthetic Plast Surg. 2024. [DOI:10.1007/s00266-023-03819-9] [PMID]
2. Armañanzas R, Liang B, Kanakia S, Bazarian JJ, Prichep LS. Identification of Concussion Subtypes Based on Intrinsic Brain Activity. JAMA Netw Open. 2024;7(2):e2355910. [DOI:10.1001/jamanetworkopen.2023.55910] [PMID] []
3. Bellini V, Semeraro F, Montomoli J, Cascella M, Bignami E. Between human and AI: assessing the reliability of AI text detection tools. Curr Med Res Opin. 2024:1-6. [DOI:10.1080/03007995.2024.2310086] [PMID]
4. Birkun AA. Misinformation on resuscitation and first aid as an uncontrolled problem that demands close attention: a brief scoping review. Public Health. 2024;228:147-9. [DOI:10.1016/j.puhe.2024.01.005] [PMID]
5. Blake SR, Das N. Deploying artificial intelligence software in an NHS trust: a how-to guide for clinicians. Br J Radiol. 2024;97(1153):68-72. [DOI:10.1093/bjr/tqad043] [PMID] []
6. Calderaro A, Piccolo G, Chezzi C. The Laboratory Diagnosis of Malaria: A Focus on the Diagnostic Assays in Non-Endemic Areas. Int J Mol Sci. 2024;25(2). [DOI:10.3390/ijms25020695] [PMID] []
7. Chen J, Huang S, Zhang Y, Chang Q, Zhang Y, Li D, et al. Congenital heart disease detection by pediatric electrocardiogram based deep learning integrated with human concepts. Nat Commun. 2024;15(1):976. [DOI:10.1038/s41467-024-44930-y] [PMID] []
8. Cinteza E, Vasile CM, Busnatu S, Armat I, Spinu AD, Vatasescu R, et al. Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children? Diagnostics (Basel). 2024;14(2). [DOI:10.3390/diagnostics14020132] [PMID] []
9. Dandurand C, Fallah N, Öner CF, Bransford RJ, Schnake K, Vaccaro AR, et al. Predictive Algorithm for Surgery Recommendation in Thoracolumbar Burst Fractures Without Neurological Deficits. Global Spine J. 2024;14(1_suppl):56s-61s. [DOI:10.1177/21925682231203491] [PMID] []
10. Derraz B, Breda G, Kaempf C, Baenke F, Cotte F, Reiche K, et al. New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology. NPJ Precis Oncol. 2024;8(1):23. [DOI:10.1038/s41698-024-00517-w] [PMID] []
11. Dhillon G, Zhang Z, Grewal H, Kashyap R. Editorial: Clinical application of artificial intelligence in emergency and critical care medicine, volume IV. Front Med (Lausanne). 2023;10:1346070. [DOI:10.3389/fmed.2023.1346070] [PMID] []
12. Farhat H, Alinier G, Helou M, Galatis I, Bajow N, Jose D, et al. Perspectives on Preparedness for Chemical, Biological, Radiological, and Nuclear Threats in the Middle East and North Africa Region: Application of Artificial Intelligence Techniques. Health Secur. 2024. [DOI:10.1089/hs.2023.0093] [PMID]
13. Paslı S, Şahin AS, Beşer MF, Topçuoğlu H, Yadigaroğlu M, İmamoğlu M. Assessing the precision of artificial intelligence in emergency department triage decisions: Insights from a study with ChatGPT. Am J Emerg Med. 2024;78:170-5. [DOI:10.1016/j.ajem.2024.01.037] [PMID]
14. Zaboli A, Brigo F, Sibilio S, Mian M, Turcato G. Human intelligence versus Chat-GPT: who performs better in correctly classifying patients in triage? Am J Emerg Med. 2024;79:44-7. [DOI:10.1016/j.ajem.2024.02.008] [PMID]
15. Frisch EH, Jain A, Jin M, Duhaime E, Malshe A, Corey S, et al. Artificial Intelligence to Determine Fetal Sex. Am J Perinatol. 2024. [DOI:10.1055/a-2265-9177] [PMID]
16. Li F, Wang B, Li H, Kong L, Zhu B. G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma. BMC Cancer. 2024;24(1):157. [DOI:10.1186/s12885-024-11887-6] [PMID] []
17. Lee YC, Ng CJ, Hsu CC, Cheng CW, Chen SY. Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review. BMC Emerg Med. 2024;24(1):20. [DOI:10.1186/s12873-024-00939-6] [PMID] []
18. Foschi M, Galante A, Ornello R, Necozione S, Marini C, Muselli M, et al. Point-Of-Care low-field MRI in acute Stroke (POCS): protocol for a multicentric prospective open-label study evaluating diagnostic accuracy. BMJ Open. 2024;14(1):e075614. [DOI:10.1136/bmjopen-2023-075614] [PMID] []
19. Fu H, Novak A, Robert D, Kumar S, Tanamala S, Oke J, et al. AI assisted reader evaluation in acute CT head interpretation (AI-REACT): protocol for a multireader multicase study. BMJ Open. 2024;14(2):e079824. [DOI:10.1136/bmjopen-2023-079824] [PMID] []
20. Weller JM, Mahajan R, Fahey-Williams K, Webster CS. Teamwork matters: team situation awareness to build high-performing healthcare teams, a narrative review. Br J Anaesth. 2024. [DOI:10.1016/j.bja.2023.12.035] [PMID]
21. Meng J, Liu J, Song W, Li H, Wang J, Zhang L, et al. PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network. Brief Bioinform. 2024;25(2). [DOI:10.1093/bib/bbae033] [PMID] []
22. Ionescu Miron AI, Atasiei DI, Ionescu RT, Ultimescu F, Barnonschi AA, Anghel AV, et al. Prediction of Subclinical and Clinical Multiple Organ Failure Dysfunction in Breast Cancer Patients-A Review Using AI Tools. Cancers (Basel). 2024;16(2). [DOI:10.3390/cancers16020381] [PMID] []
23. Zhao X, Qiu T, Huang X, Mao Q, Wang Y, Qiao R, et al. Potent and broadly neutralizing antibodies against sarbecoviruses induced by sequential COVID-19 vaccination. Cell Discov. 2024;10(1):14. [DOI:10.1038/s41421-024-00648-1] [PMID] []
24. Rudolph J, Huemmer C, Preuhs A, Buizza G, Hoppe BF, Dinkel J, et al. Non-radiology Healthcare Professionals Significantly Benefit from AI-Assistance in Emergency-Related Chest Radiography Interpretation. Chest. 2024. [DOI:10.1016/j.chest.2024.01.039] [PMID] []
25. Holmstrom L, Bednarski B, Chugh H, Aziz H, Pham HN, Sargsyan A, et al. Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation. Circ Arrhythm Electrophysiol. 2024:e012338. [DOI:10.1161/CIRCEP.123.012338] [PMID] []
26. Mayourian J, La Cava WG, Vaid A, Nadkarni GN, Ghelani SJ, Mannix R, et al. Pediatric ECG-Based Deep Learning to Predict Left Ventricular Dysfunction and Remodeling. Circulation. 2024. [DOI:10.1161/CIRCULATIONAHA.123.067750] [PMID] []
27. Fairag M, Almahdi RH, Siddiqi AA, Alharthi FK, Alqurashi BS, Alzahrani NG, et al. Robotic Revolution in Surgery: Diverse Applications Across Specialties and Future Prospects Review Article. Cureus. 2024;16(1):e52148. [DOI:10.7759/cureus.52148]
28. Panç K, Hürsoy N, Başaran M, Yazici MM, Kaba E, Nalbant E, et al. Predicting COVID-19 Outcomes: Machine Learning Predictions Across Diverse Datasets. Cureus. 2023;15(12):e50932. [DOI:10.7759/cureus.50932] [PMID] []
29. Kim JH, Kim SK, Choi J, Lee Y. Reliability of ChatGPT for performing triage task in the emergency department using the Korean Triage and Acuity Scale. Digit Health. 2024;10:20552076241227132. [DOI:10.1177/20552076241227132] [PMID] []
30. Kim YT, Huh JW, Choi YH, Yoon HK, Nguyen TT, Chun E, et al. Author Correction: Highly secreted tryptophanyl tRNA synthetase 1 as a potential theranostic target for hypercytokinemic severe sepsis. EMBO Mol Med. 2024. [DOI:10.1038/s44321-024-00030-4] [PMID] []
31. Nikouline A, Feng J, Rudzicz F, Nathens A, Nolan B. Machine learning in the prediction of massive transfusion in trauma: a retrospective analysis as a proof-of-concept. Eur J Trauma Emerg Surg. 2024. [DOI:10.1007/s00068-023-02423-5] [PMID]
32. Hofmeister J, Garin N, Montet X, Scheffler M, Platon A, Poletti PA, et al. Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies. Eur Radiol Exp. 2024;8(1):20. [DOI:10.1186/s41747-023-00416-y] [PMID] []
33. Zhang H, Zeng T, Zhang J, Zheng J, Min J, Peng M, et al. Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China. Front Endocrinol (Lausanne). 2024;15:1292346. [DOI:10.3389/fendo.2024.1292346] [PMID] []
34. Hien NTK, Tsai FJ, Chang YH, Burton W, Phuc PT, Nguyen PA, et al. Unveiling the future of COVID-19 patient care: groundbreaking prediction models for severe outcomes or mortality in hospitalized cases. Front Med (Lausanne). 2023;10:1289968. [DOI:10.3389/fmed.2023.1289968] [PMID] []
35. Kherabi Y, Thy M, Bouzid D, Antcliffe DB, Miles Rawson T, Peiffer-Smadja N. Machine learning to predict antimicrobial resistance: future applications in clinical practice? Infect Dis Now. 2024:104864. [DOI:10.1016/j.idnow.2024.104864] [PMID]
36. Thon P, Rahmel T, Ziehe D, Palmowski L, Marko B, Nowak H, et al. AQP3 and AQP9-Contrary Players in Sepsis? Int J Mol Sci. 2024;25(2). [DOI:10.3390/ijms25021209] [PMID] []
37. Ziehe D, Marko B, Thon P, Rahmel T, Palmowski L, Nowak H, et al. The Aquaporin 3 Polymorphism (rs17553719) Is Associated with Sepsis Survival and Correlated with IL-33 Secretion. Int J Mol Sci. 2024;25(3). [DOI:10.3390/ijms25031400] [PMID] []
38. Xie LF, Xie YL, Wu QS, He J, Lin XF, Qiu ZH, Chen LW. A predictive model for postoperative adverse outcomes following surgical treatment of acute type A aortic dissection based on machine learning. J Clin Hypertens (Greenwich). 2024. [DOI:10.1111/jch.14774] [PMID] []
39. Muzammil MA, Javid S, Afridi AK, Siddineni R, Shahabi M, Haseeb M, et al. Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases. J Electrocardiol. 2024;83:30-40. [DOI:10.1016/j.jelectrocard.2024.01.006] [PMID]
40. Xing Z, Zhu Z, Jiang Z, Zhao J, Chen Q, Xing W, et al. Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. J Imaging Inform Med. 2024. [DOI:10.1007/s10278-023-00946-2] [PMID] []
41. Kim D, Lee E, Eom J, Kim Y, Kwon SH, Oh HS, et al. Prevalence and Burden of Human Adenovirus-Associated Acute Respiratory Illness in the Republic of Korea Military, 2013 to 2022. J Korean Med Sci. 2024;39(4):e38. [DOI:10.3346/jkms.2024.39.e38] [PMID] []
42. Park SW, Yeo NY, Kang S, Ha T, Kim TH, Lee D, et al. Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study. J Korean Med Sci. 2024;39(5):e53. [DOI:10.3346/jkms.2024.39.e53] [PMID] []
43. Fudickar S, Bantel C, Spieker J, Töpfer H, Stegeman P, Schiphorst Preuper HR, et al. Natural Language Processing of Referral Letters for Machine Learning-Based Triaging of Patients With Low Back Pain to the Most Appropriate Intervention: Retrospective Study. J Med Internet Res. 2024;26:e46857. [DOI:10.2196/46857] [PMID] []
44. Soltan AAS, Thakur A, Yang J, Chauhan A, D'Cruz LG, Dickson P, et al. A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals. Lancet Digit Health. 2024;6(2):e93-e104. [DOI:10.1016/S2589-7500(23)00226-1] [PMID]
45. Niu Q, Li H, Liu Y, Qin Z, Zhang LB, Chen J, Lyu Z. Toward the Internet of Medical Things: Architecture, trends and challenges. Math Biosci Eng. 2024;21(1):650-78. [DOI:10.3934/mbe.2024028] [PMID]
46. Yang J, Hao S, Huang J, Chen T, Liu R, Zhang P, et al. The application of artificial intelligence in the management of sepsis. Med Rev (2021). 2023;3(5):369-80. [DOI:10.1515/mr-2023-0039] [PMID] []
47. Masters K, Benjamin J, Agrawal A, MacNeill H, Pillow MT, Mehta N. Twelve tips on creating and using custom GPTs to enhance health professions education. Med Teach. 2024:1-5. [DOI:10.1080/0142159X.2024.2305365] [PMID]
48. Wissel BD, Greiner HM, Glauser TA, Pestian JP, Ficker DM, Cavitt JL, et al. Early Identification of Candidates for Epilepsy Surgery: A Multicenter, Machine Learning, Prospective Validation Study. Neurology. 2024;102(4):e208048. [DOI:10.1212/WNL.0000000000208048] [PMID] []
49. Montoya ID, Volkow ND. IUPHAR Review: New strategies for medications to treat substance use disorders. Pharmacol Res. 2024;200:107078. [DOI:10.1016/j.phrs.2024.107078] [PMID] []
50. Patel AK, Trujillo-Rivera E, Chamberlain JM, Morizono H, Pollack MM. External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients. PLoS One. 2024;19(1):e0288233. [DOI:10.1371/journal.pone.0288233] [PMID] []
51. Sardesai N, Hibberd O, Price J, Ercole A, Barnard EBG. Agreement between arterial and end-tidal carbon dioxide in adult patients admitted with serious traumatic brain injury. PLoS One. 2024;19(2):e0297113. [DOI:10.1371/journal.pone.0297113] [PMID] []
52. Semeraro F, Montomoli J, Cascella M, Bellini V, Bignami EG. Trends and insights about cardiac arrest and artificial intelligence on PubMed using ChatGPT-4. Resuscitation. 2024;196:110131. [DOI:10.1016/j.resuscitation.2024.110131] [PMID]
53. Zubair M. Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis. Saudi J Biol Sci. 2024;31(3):103934. [DOI:10.1016/j.sjbs.2024.103934] [PMID] []
54. van Dam P, van Doorn W, van Gils F, Sevenich L, Lambriks L, Meex SJR, et al. Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department. Scand J Trauma Resusc Emerg Med. 2024;32(1):5. [DOI:10.1186/s13049-024-01177-2] [PMID] []
55. Jiang L, Raza A, El Ariss AB, Chen D, Danaher-Garcia N, Lee J, He S. Impact of medical technologies may be predicted using constructed graph bibliometrics. Sci Rep. 2024;14(1):2419. [DOI:10.1038/s41598-024-52233-x] [PMID] []
56. Rahmatinejad Z, Dehghani T, Hoseini B, Rahmatinejad F, Lotfata A, Reihani H, Eslami S. A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department. Sci Rep. 2024;14(1):3406. [DOI:10.1038/s41598-024-54038-4] [PMID] []
57. Wu L, Zhang J, Wang Y, Ding R, Cao Y, Liu G, et al. Pneumonia detection based on RSNA dataset and anchor-free deep learning detector. Sci Rep. 2024;14(1):1929. [DOI:10.1038/s41598-024-52156-7] [PMID] []
58. Krefting D, Mutters NT, Pryss R, Sedlmayr M, Boeker M, Dieterich C, et al. Herding Cats in Pandemic Times - Towards Technological and Organizational Convergence of Heterogeneous Solutions for Investigating and Mastering the Pandemic in University Medical Centers. Stud Health Technol Inform. 2024;310:1271-5. [DOI:10.3233/SHTI231169] [PMID]
59. Majouni S, Tennankore K, Abidi SSR. Predicting Urgent Dialysis at Ambulance Transport to the Emergency Department Using Machine Learning Methods. Stud Health Technol Inform. 2024;310:891-5. [DOI:10.3233/SHTI231093] [PMID]
60. Wu Y, Hughes JA, Lyrstedt AL, Hazelwood S, Brown NJ, Jones L, et al. Developing Robust Clinical Text Deep Learning Models - A "Painless" Approach. Stud Health Technol Inform. 2024;310:705-9. [DOI:10.3233/SHTI231056] [PMID]
61. Thoma B, Bernard J, Wang S, Yilmaz Y, Bandi V, Woods RA, et al. Deidentifying Narrative Assessments to Facilitate Data Sharing in Medical Education. Acad Med. 2023. [DOI:10.1097/ACM.0000000000005596] [PMID]
62. Thoma B, Spadafore M, Sebok-Syer SS, George BC, Chan TM, Krumm AE. Considering the Secondary Use of Clinical and Educational Data to Facilitate the Development of Artificial Intelligence Models. Acad Med. 2023. [DOI:10.1097/ACM.0000000000005605] [PMID]
63. Preiksaitis C, Nash C, Gottlieb M, Chan TM, Alvarez A, Landry A. Brain versus bot: Distinguishing letters of recommendation authored by humans compared with artificial intelligence. AEM Educ Train. 2023;7(6). [DOI:10.1002/aet2.10924] [PMID] []
64. Akhter M. Accuracy of GPT's artificial intelligence on emergency medicine board recertification exam. Am J Emerg Med. 2024;76:254-5. [DOI:10.1016/j.ajem.2023.11.061]
65. Iserson KV. Informed consent for artificial intelligence in emergency medicine: A practical guide. Am J Emerg Med. 2024;76:225-30. [DOI:10.1016/j.ajem.2023.11.022] [PMID]
66. Lee S, Lee J, Park J, Park J, Kim D, Lee J, Oh J. Deep learning-based natural language processing for detecting medical symptoms and histories in emergency patient triage. Am J Emerg Med. 2024;77:29-38. [DOI:10.1016/j.ajem.2023.11.063] [PMID]
67. Sosa PA, Firnberg M, Tsung JW. Point-of-care ultrasound evaluation of suspected necrotizing enterocolitis in the ED. Am J Emerg Med. 2024;76:270.e1-.e4. [DOI:10.1016/j.ajem.2023.12.032] [PMID]
68. Yilmaz S, Ozel M, Tatliparmak AC, Ak R. START: The fusion of rapid treatment and triage - A broader perspective for artificial intelligence comparison. Am J Emerg Med. 2024;76:241-2. [DOI:10.1016/j.ajem.2023.12.018]
69. Rubulotta F, Blanch Torra L, Naidoo KD, Aboumarie HS, Mathivha LR, Asiri AY, et al. Mechanical Ventilation, Past, Present, and Future. Anesth Analg. 2024;138(2):308-25. [DOI:10.1213/ANE.0000000000006701] [PMID]
70. Nasser L, McLeod SL, Hall JN. Evaluating the Reliability of a Remote Acuity Prediction Tool in a Canadian Academic Emergency Department. Ann Emerg Med. 2024. [DOI:10.1016/j.annemergmed.2023.11.018] [PMID]
71. Kaffas AE, Vo-Phamhi JM, Griffin JFt, Hoyt K. Critical Advances for Democratizing Ultrasound Diagnostics in Human and Veterinary Medicine. Annu Rev Biomed Eng. 2024. [DOI:10.1146/annurev-bioeng-110222-095229] [PMID] []
72. Henriksson A, Pawar Y, Hedberg P, Nauclér P. Multimodal fine-tuning of clinical language models for predicting COVID-19 outcomes. Artif Intell Med. 2023;146:102695. [DOI:10.1016/j.artmed.2023.102695] [PMID]
73. Lombardi A, Arezzo F, Di Sciascio E, Ardito C, Mongelli M, Di Lillo N, et al. A human-interpretable machine learning pipeline based on ultrasound to support leiomyosarcoma diagnosis. Artif Intell Med. 2023;146:102697. [DOI:10.1016/j.artmed.2023.102697] [PMID]
74. Maghami M, Sattari SA, Tahmasbi M, Panahi P, Mozafari J, Shirbandi K. Diagnostic test accuracy of machine learning algorithms for the detection intracranial hemorrhage: a systematic review and meta-analysis study. Biomed Eng Online. 2023;22(1):114. [DOI:10.1186/s12938-023-01172-1] [PMID] []
75. Heo KN, Seok JY, Ah YM, Kim KI, Lee SB, Lee JY. Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population. BMC Geriatr. 2023;23(1):830. [DOI:10.1186/s12877-023-04523-8] [PMID] []
76. Chen G, Zhang W, Wang C, Chen M, Hu Y, Wang Z. Screening of four lysosome-related genes in sepsis based on RNA sequencing technology. BMC Immunol. 2023;24(1):50. [DOI:10.1186/s12865-023-00588-7] [PMID] []
77. Zhang Y, Xu W, Yang P, Zhang A. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis. BMC Med Inform Decis Mak. 2023;23(1):283. [DOI:10.1186/s12911-023-02383-1] [PMID] []
78. Vigdorovits A, Köteles MM, Olteanu GE, Pop O. Breaking Barriers: AI's Influence on Pathology and Oncology in Resource-Scarce Medical Systems. Cancers (Basel). 2023;15(23). [DOI:10.3390/cancers15235692] [PMID] []
79. Wang X, Jiang S, Ma W, Li X, Wei K, Xie F, et al. Enhanced neutralization of SARS-CoV-2 variant BA.2.86 and XBB sub-lineages by a tetravalent COVID-19 vaccine booster. Cell Host Microbe. 2024;32(1):25-34.e5. [DOI:10.1016/j.chom.2023.11.012] [PMID]
80. Franc JM, Cheng L, Hart A, Hata R, Hertelendy A. Repeatability, reproducibility, and diagnostic accuracy of a commercial large language model (ChatGPT) to perform emergency department triage using the Canadian triage and acuity scale. Cjem. 2024;26(1):40-6. [DOI:10.1007/s43678-023-00616-w] [PMID]
81. Okada Y, Ning Y, Ong MEH. Explainable artificial intelligence in emergency medicine: an overview. Clin Exp Emerg Med. 2023;10(4):354-62. [DOI:10.15441/ceem.23.145] [PMID] []
82. Shin TG, Lee Y, Kim K, Lee MS, Kwon JM. ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department. Clin Exp Emerg Med. 2023;10(4):438-45. [DOI:10.15441/ceem.22.360] [PMID] []
83. Chen ZM, Liao Y, Zhou X, Yu W, Zhang G, Ge Y, et al. Pancreatic cancer pathology image segmentation with channel and spatial long-range dependencies. Comput Biol Med. 2024;169:107844. [DOI:10.1016/j.compbiomed.2023.107844] [PMID]
84. Tsai CH, Hu YH. Application of Machine Learning Techniques to Development of Emergency Medical Rapid Triage Prediction Models in Acute Care. Comput Inform Nurs. 2024;42(1):35-43. [DOI:10.1097/CIN.0000000000001057] [PMID]
85. Wang CH, Hwang T, Huang YS, Tay J, Wu CY, Wu MC, et al. Deep Learning-Based Localization and Detection of Malpositioned Endotracheal Tube on Portable Supine Chest Radiographs in Intensive and Emergency Medicine: A Multicenter Retrospective Study. Crit Care Med. 2024;52(2):237-47. [DOI:10.1097/CCM.0000000000006046] [PMID] []
86. Patel A, Arora GS, Roknsharifi M, Kaur P, Javed H. Artificial Intelligence in the Detection of Barrett's Esophagus: A Systematic Review. Cureus. 2023;15(10):e47755. [DOI:10.7759/cureus.47755]
87. Bellomo TR, Goudot G, Lella SK, Landau E, Sumetsky N, Zacharias N, et al. Feasibility of Encord Artificial Intelligence Annotation of Arterial Duplex Ultrasound Images. Diagnostics (Basel). 2023;14(1). [DOI:10.3390/diagnostics14010046] [PMID] []
88. Suciu CI, Marginean A, Suciu VI, Muntean GA, Nicoară SD. Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt. Diagnostics (Basel). 2023;14(1). [DOI:10.3390/diagnostics14010076] [PMID] []
89. Tsai MC, Lojanapiwat B, Chang CC, Noppakun K, Khumrin P, Li SH, et al. Risk Prediction Model for Chronic Kidney Disease in Thailand
90. Using Artificial Intelligence and SHAP. Diagnostics (Basel). 2023;13(23). [DOI:10.3390/diagnostics13132309] [PMID] []
91. Choi SJ, Kim DK, Kim BS, Cho M, Jeong J, Jo YH, et al. Mask R-CNN based multiclass segmentation model for endotracheal intubation using video laryngoscope. Digit Health. 2023;9:20552076231211547. [DOI:10.1177/20552076231211547] [PMID] []
92. Kim YT, Huh JW, Choi YH, Yoon HK, Nguyen TT, Chun E, et al. Highly secreted tryptophanyl tRNA synthetase 1 as a potential theranostic target for hypercytokinemic severe sepsis. EMBO Mol Med. 2024;16(1):40-63. [DOI:10.1038/s44321-023-00004-y] [PMID] []
93. Stewart J, Freeman S, Eroglu E, Dumitrascu N, Lu J, Goudie A, et al. Attitudes towards artificial intelligence in emergency medicine. Emerg Med Australas. 2023. [DOI:10.1111/1742-6723.14345] [PMID]
94. Zhou S, Lu Z, Liu Y, Wang M, Zhou W, Cui X, et al. Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation. Eur J Med Res. 2024;29(1):14. [DOI:10.1186/s40001-023-01593-7] [PMID] []
95. van den Wittenboer GJ, van der Kolk BYM, Nijholt IM, Langius-Wiffen E, van Dijk RA, van Hasselt B, et al. Diagnostic accuracy of an artificial intelligence algorithm versus radiologists for fracture detection on cervical spine CT. Eur Radiol. 2024. [DOI:10.1007/s00330-023-10559-6] [PMID]
96. Buddenkotte T, Rundo L, Woitek R, Escudero Sanchez L, Beer L, Crispin-Ortuzar M, et al. Deep learning-based segmentation of multisite disease in ovarian cancer. Eur Radiol Exp. 2023;7(1):77. [DOI:10.1186/s41747-023-00388-z] [PMID] []
97. Nicoara AI, Sas LM, Bita CE, Dinescu SC, Vreju FA. Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review. Front Med (Lausanne). 2023;10:1280266. [DOI:10.3389/fmed.2023.1280266] [PMID] []
98. Chen X, Chen F, Liang C, He G, Chen H, Wu Y, et al. MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations. Front Microbiol. 2023;14:1308149. [DOI:10.3389/fmicb.2023.1308149] [PMID] []
99. Guida F, Andreozzi L, Zama D, Prete A, Masetti R, Fabi M, Lanari M. Innovative strategies to predict and prevent the risk for malnutrition in child, adolescent, and young adult cancer survivors. Front Nutr. 2023;10:1332881. [DOI:10.3389/fnut.2023.1332881] [PMID] []
100. Palacios-Ariza MA, Morales-Mendoza E, Murcia J, Arias-Duarte R, Lara-Castellanos G, Cely-Jiménez A, et al. Prediction of patient admission and readmission in adults from a Colombian cohort with bipolar disorder using artificial intelligence. Front Psychiatry. 2023;14:1266548. [DOI:10.3389/fpsyt.2023.1266548] [PMID] []
101. Tiwari A, Patel MG, Borah N, Singh A, Shah S, Prabhakar A. COVID-19 SAFETY MEASURES AND THEIR EFFECTS ON GAMBLING HABITS: AN INVESTIGATIVE STUDY. Georgian Med News. 2023(343):144-52.
102. Ainiwaer A, Hou WQ, Qi Q, Kadier K, Qin L, Rehemuding R, et al. Deep learning of heart-sound signals for efficient prediction of obstructive coronary artery disease. Heliyon. 2024;10(1):e23354. [DOI:10.1016/j.heliyon.2023.e23354] [PMID] []
103. Gebremichael LG, Champion S, Nesbitt K, Pearson V, Bulamu NB, Dafny HA, et al. Effectiveness of cardiac rehabilitation programs on medication adherence in patients with cardiovascular disease: A systematic review and meta-analysis. Int J Cardiol Cardiovasc Risk Prev. 2024;20:200229. [DOI:10.1016/j.ijcrp.2023.200229] [PMID] []
104. Dadon Z, Orlev A, Butnaru A, Rosenmann D, Glikson M, Gottlieb S, Alpert EA. Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care
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