Notaufnahme up2date, Table of Contents Notaufnahme up2date 2024; 06(03): 215-218DOI: 10.1055/a-2300-6235 Editorial Einsatz von Künstlicher Intelligenz in der Notaufnahme Contributor(s): Philipp Kümpers , Sebastian Casu , Bernhard Kumle Recommend Article Abstract Buy Article All articles of this category Full Text References Literatur 1 Ivanov O, Wolf L, Brecher D. et al. Improving ED Emergency Severity Index Acuity Assignment Using Machine Learning and Clinical Natural Language Processing. J Emerg Nurs 2021; 47: 265-278 e267 2 Herman R, Demolder A, Vavrik B. et al. Validation of an automated artificial intelligence system for 12‑lead ECG interpretation. J Electrocardiol 2024; 82: 147-154 3 Al-Zaiti SS, Martin-Gill C, Zegre-Hemsey JK. et al. Machine learning for ECG diagnosis and risk stratification of occlusion myocardial infarction. Nat Med 2023; 29: 1804-1813 4 Herman R, Meyers HP, Smith SW. et al. International evaluation of an artificial intelligence-powered electrocardiogram model detecting acute coronary occlusion myocardial infarction. Eur Heart J Digit Health 2024; 5: 123-133 5 Himmelreich JCL, Harskamp RE. Diagnostic accuracy of the PMcardio smartphone application for artificial intelligence-based interpretation of electrocardiograms in primary care (AMSTELHEART-1). Cardiovasc Digit Health J 2023; 4: 80-90 6 Gohar E, Herling A, Mazuz M. et al. Artificial Intelligence (AI) versus POCUS Expert: A Validation Study of Three Automatic AI-Based, Real-Time, Hemodynamic Echocardiographic Assessment Tools. J Clin Med 2023; 12: 1352 7 Mika S, Gola W, Gil-Mika M. et al. Ultrasonographic Applications of Novel Technologies and Artificial Intelligence in Critically Ill Patients. J Pers Med 2024; 14: 286 8 Baloescu C, Rucki AA, Chen A. et al. Machine Learning Algorithm Detection of Confluent B-Lines. Ultrasound Med Biol 2023; 49: 2095-2102 9 Martinez-Gutierrez JC, Kim Y, Salazar-Marioni S. et al. Automated Large Vessel Occlusion Detection Software and Thrombectomy Treatment Times: A Cluster Randomized Clinical Trial. JAMA Neurol 2023; 80: 1182-1190 10 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: 247-254 11 Boonstra A, Laven M. Influence of artificial intelligence on the work design of emergency department clinicians a systematic literature review. BMC Health Serv Res 2022; 22: 669 12 Chenais G, Lagarde E, Gil-Jardine C. Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges. J Med Internet Res 2023; 25: e40031 13 Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K. et al. The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. Arch Acad Emerg Med 2023; 11: e38 14 Preiksaitis C, Ashenburg N, Bunney G. et al. The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review. JMIR Med Inform 2024; 12: e53787 15 Thirunavukarasu AJ, Ting DSJ, Elangovan K. et al. Large language models in medicine. Nat Med 2023; 29: 1930-1940 16 Rao A, Pang M, Kim J. et al. Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study. J Med Internet Res 2023; 25: e48659 17 Smith J, Choi PM, Buntine P. Will code one day run a code? Performance of language models on ACEM primary examinations and implications. Emerg Med Australas 2023; 35: 876-878 18 Van Veen D, Van Uden C, Blankemeier L. et al. Adapted large language models can outperform medical experts in clinical text summarization. Nat Med 2024; 30: 1134-1142 19 Zaretsky J, Kim JM, Baskharoun S. et al. Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format. JAMA Netw Open 2024; 7: e240357 20 Haberle T, Cleveland C, Snow GL. et al. The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inform Assoc 2024; 31: 975-979 21 Chi EA, Chi G, Tsui CT. et al. Development and Validation of an Artificial Intelligence System to Optimize Clinician Review of Patient Records. JAMA Netw Open 2021; 4: e2117391 22 Gandhi TK, Classen D, Sinsky CA. et al. How can artificial intelligence decrease cognitive and work burden for front line practitioners?. JAMIA Open 2023; 6: ooad079