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Cross Approach Between Modern Artificial Intelligence and Emergency Medicine: A Review

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Intelligent Computing (SAI 2024)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 1018))

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Abstract

The emergency department (ED) is an intricate facet of the healthcare system, including hospital and prehospital entities that interact closely to make quick and accurate decisions. Therefore, it is the initial point of interaction between medical professionals and individuals presenting an array of symptoms. Emergency physicians face several challenges, such as long hospital stays, diagnostic complexities, waiting times, and appropriate resource allocation dilemmas that arise from traditional medical practices. Furthermore, the COVID-19 pandemic has underscored the limitation of traditional healthcare methods and symbolic Artificial Intelligence (AI), emphasizing the imperative need for solutions rooted in contemporary AI. The integration of modern AI into the sphere of Emergency Medicine (EM) provides promising insight into the future of emergency care, but few articles highlight these advances. The main objective of this scoping review is to provide the community with the importance of the cross-approach between modern AI and EM by highlighting all the hidden advances, limitations, and progress that can be made to improve ED. We also scrutinize modern AI systems, algorithms, and their complexity, as well as the ethics associated with using this cutting-edge technology in EM.

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References

  1. Cowling, T.E., et al.: Access to primary care and visits to emergency departments in England: a cross-sectional, population-based study. PLoS One 8(6), e66699 (2013 ). https://doi.org/10.1371/journal.pone.0066699. PMID: 23776694; PMCID: PMC3680424

  2. Paling, S., Lambert, J., Clouting, J., González-Esquerré, J., Auterson, T.: Waiting times in emergency departments: exploring the factors associated with longer patient waits for emergency care in England using routinely collected daily data. Emerg. Med. J. 37(12), 781–786 (2020 ). https://doi.org/10.1136/emermed-2019-208849. PMID: 32933946; PMCID: PMC7691811

  3. Haag, F., Hopf, K., Vasconcelos, P.M., Staake, T.: Augmented cross-selling through explainable AI–a case from energy retailing. arXiv preprint arXiv:2208.11404 (2022)

  4. Shoman, M., Aboah, A., Morehead, A., Duan, Y., Daud, A., Adu-Gyamfi, Y.: A region-based deep learning approach to automated retail checkout. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3210–3215 (2022)

    Google Scholar 

  5. Hofmann, M., Neukart, F., Bäck, T.: Artificial Intelligence and Data Science in the Automotive Industry (2017)

    Google Scholar 

  6. Wen, L., et al.: On the road with GPT-4V (ision): early explorations of visual-language model on autonomous driving. arXiv preprint arXiv:2311.05332 (2023)

  7. Kumbhar, A., Chougale, A., Lokhande, P., Navaghane, S., Burud, A., Nimbalkar, S.: DeepInspect: an AI-powered defect detection for manufacturing industries. arXiv preprint arXiv:2311.03725 (2023)

  8. Buehler, M.J.: Generative retrieval-augmented ontologic graph and multi-agent strategies for interpretive large language model-based materials design. arXiv preprint arXiv:2310.19998 (2023)

  9. Asha, R.B., Suresh Kumar, K.R.: Credit card fraud detection using artificial neural network. Glob. Transit. Proc. 2(1), 35–41 (2021). ISSN 2666-285X. https://doi.org/10.1016/j.gltp.2021.01.006

  10. Khandani, A.E., Kim, A.J., Lo, A.W.: Consumer credit-risk models via machine-learning algorithms. J. Bank. Finan. 34(11), 2767–2787 (2010). ISSN 0378-4266. https://doi.org/10.1016/j.jbankfin.2010.06.001

  11. Abdusalomov, A.B., Mukhiddinov, M., Whangbo, T.K.: Brain tumor detection based on deep learning approaches and magnetic resonance imaging. Cancers (Basel) 15(16), 4172 (2023). https://doi.org/10.3390/cancers15164172. PMID: 37627200; PMCID: PMC10453020

  12. Talukder, MA.A., et al.: An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Syst. Appl. 230, 120534 (2023). ISSN 0957–4174. https://doi.org/10.1016/j.eswa.2023.120534

  13. Kalluri, H.K., Tulasi Krishna, S.: A deep learning method for prediction of cardiovascular disease using convolutional neural network. Revue d intelligence artificielle 34, 601–606 (2020). https://doi.org/10.18280/ria.340510

    Article  Google Scholar 

  14. Wang, J., Ding, H., Bidgoli, F.A., et al.: Detecting cardiovascular disease from mammograms with deep learning. IEEE Trans. Med. Imaging 36(5), 1172–1181 (2017)

    Article  Google Scholar 

  15. Kumar, A., Bhadauria, H.S., Singh, A.: Descriptive analysis of dental X-ray images using various practical methods: A review. PeerJ Comput. Sci. 7, e620 (2021). https://doi.org/10.7717/peerj-cs.620

    Article  Google Scholar 

  16. Hêche, F., Barakat, O., Desmettre, T., et al.: Offline reinforcement learning in high-dimensional stochastic environments. Neural Comput. Appl. (2023). https://doi.org/10.1007/s00521-023-09029-3

  17. Benitez, K., Malin, B.: Evaluating re-identification risks with respect to the HIPAA privacy rule. J Am. Med. Inf. Assoc. 17(2), 169–177 (2010). https://doi.org/10.1136/jamia.2009.000026. PMID: 20190059; PMCID: PMC3000773

  18. Silva, M.: On the history of discrete event systems. Ann. Rev. Control 45, 213–222 (2018). ISSN 1367–5788. https://doi.org/10.1016/j.arcontrol.2018.03.004

  19. Rodwin, V.G.: The health care system under French national health insurance: lessons for health reform in the United States. Am. J. Public Health 93(1), 31–37 (2003). https://doi.org/10.2105/AJPH.93.1.31

    Article  Google Scholar 

  20. Muthukrishnan, N., Maleki, F., Ovens, K., Reinhold, C., Forghani, B., Forghani, R.: Brief history of artificial intelligence. Neuroimaging Clin. N Am. 30(4), 393–399 (2020). https://doi.org/10.1016/j.nic.2020.07.004. PMID: 33038991

  21. Benko, A., Sik Lányi, C.: History of artificial intelligence. In: Mehdi Khosrow-Pour, D.B.A. (ed.) Encyclopedia of Information Science and Technology, 2nd edn., pp. 1759–1762. IGI Global (2009). https://doi.org/10.4018/978-1-60566-026-4.ch276

  22. Haenlein, M., Kaplan, A.: A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif. Manag. Rev. 61(4), 5–14 (2019)

    Article  Google Scholar 

  23. Alzaid, N., et al.: Revolutionizing dental care: a comprehensive review of artificial intelligence applications among various dental specialties. Cureus. 15(10), e47033 (2023). https://doi.org/10.7759/cureus.47033. PMID: 37965397; PMCID: PMC10642940

  24. Shafaf, N., Malek, H.: Applications of machine learning approaches in emergency medicine; a review article. Arch. Acad. Emerg. Med. 7(1), 34 (2019). PMID: 31555764; PMCID: PMC6732202

    Google Scholar 

  25. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995). https://doi.org/10.1007/BF00994018

    Article  Google Scholar 

  26. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2012)

    Article  Google Scholar 

  27. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  28. Mittal, S., Hasija, Y.: Applications of deep learning in healthcare and biomedicine. In: Dash, S., Acharya, B.R., Mittal, M., Abraham, A., Kelemen, A. (eds.) Deep Learning Techniques for Biomedical and Health Informatics. SBD, vol. 68, pp. 57–77. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-33966-1_4

    Chapter  Google Scholar 

  29. Cunningham, P., Cord, M., Delany, S.J.: Supervised learning. In: Cord, M., Cunningham, P. (eds.) Machine Learning Techniques for Multimedia. Cognitive Technologies. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-75171-7_2

  30. Vermeulen, A.F.: Unsupervised learning: using unlabeled data. In: Industrial Machine Learning. Apress, Berkeley (2020). https://doi.org/10.1007/978-1-4842-5316-8_6

  31. van Engelen, J.E., Hoos, H.H.: A survey on semi-supervised learning. Mach. Learn. 109, 373–440 (2020). https://doi.org/10.1007/s10994-019-05855-6

    Article  MathSciNet  Google Scholar 

  32. Coronato, A., Naeem, M., De Pietro, G., Paragliola, G.: Reinforcement learning for intelligent healthcare applications: a survey. Artif. Intell. Med. 109, 101964 (2020). https://doi.org/10.1016/j.artmed.2020.101964. PMID: 34756216

  33. Kaelbling, L.P., et al.: Reinforcement learning: a survey. J. Artif. Intell. Res. 4, 237–285 (1996)

    Article  Google Scholar 

  34. Abdiansah, A., Wardoyo, R.: Time complexity analysis of support vector machines (SVM) in LibSVM. Int. J. Comput. Appl. 128, 28–34 (2015)

    Google Scholar 

  35. Singh, J.: Computational complexity and analysis of supervised machine learning algorithms. In: Kumar, R., Pattnaik, P.K., R. S. Tavares, J.M. (eds.) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol. 445, pp. 195–206. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_16

  36. Mahesh, B.: Machine learning algorithms -a review (2019). https://doi.org/10.21275/ART20203995

  37. Myung, I.J. The importance of complexity in model selection. J. Math. Psychol. 44(1), 190–204 (2000). https://doi.org/10.1006/jmps.1999.1283. PMID: 10733864

  38. Panch, T., Szolovits, P., Atun, R.: Artificial intelligence, machine learning and health systems. J. Glob. Health. 8(2), 020303 (2018). https://doi.org/10.7189/jogh.08.020303. PMID: 30405904; PMCID: PMC6199467

  39. Wang, F., Casalino, L.P., Khullar, D.: Deep learning in medicine-promise, progress, and challenges. JAMA Int. Med. 179(3), 293–294 (2019). https://doi.org/10.1001/jamainternmed.2018.7117. PMID: 30556825

  40. Wilson, M.H., Habig, K., Wright, C., Hughes, A., Davies, G., Imray, C.H.E.: Pre-hospital emergency medicine. The Lancet 386(10012), 2526–2534 (2015). ISSN 0140-6736

    Google Scholar 

  41. Hasan, et al.: Goodacre, Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: development, application and comparison of machine learning and deep learning methods. Comput. Biol. Med. 151(Part A), 106024 (2022). ISSN 0010-4825,

    Google Scholar 

  42. Tollinton, L., Metcalf, A.M., Velupillai, S.: Enhancing predictions of patient conveyance using emergency call handler free text notes for unconscious and fainting incidents reported to the London Ambulance Service. Int. J. Med. Inf. 141, 104179 (2020 ). https://doi.org/10.1016/j.ijmedinf.2020.104179. PMID: 32663739

  43. Kim, J.H., Kim, B., Kim, M.J., Hyun, H., Kim, H.C., Chang, H.J.: Prediction of inappropriate pre-hospital transfer of patients with suspected cardiovascular emergency diseases using machine learning: a retrospective observational study. BMC Med. Inf. Decis. Mak. 23(1), 56 (2023). https://doi.org/10.1186/s12911-023-02149-9. PMID: 37024872; PMCID: PMC10080868

  44. Moyer, J.D., et al.: Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury. World J. Emerg. Surg. 17(1), 42 (2022). https://doi.org/10.1186/s13017-022-00449-5. PMID: 35922831; PMCID: PMC9351267

  45. Lachance, C.C., Ford, C.: Portable Stroke Detection Devices for Patients with Stroke Symptoms: A Review of Diagnostic Accuracy and Cost-Effectiveness [Internet]. Canadian Agency for Drugs and Technologies in Health, Ottawa (ON) (2019)

    Google Scholar 

  46. Blomberg, S.N., et al :Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation 138, 322–329 (2019). ISSN 0300-9572. https://doi.org/10.1016/j.resuscitation.2019.01.015

  47. Kwon, J.M., Lee, Y., Lee, Y., Lee, S., Park, H., Park, J.: Validation of deep-learning-based triage and acuity score using a large national dataset. PLoS One 13(10), e0205836 (2018). https://doi.org/10.1371/journal.pone.0205836. PMID: 30321231; PMCID: PMC6188844

  48. Chang, H., et al.: Clinical support system for triage based on federated learning for the Korea triage and acuity scale. Heliyon 9(8), e19210 (2023). https://doi.org/10.1016/j.heliyon.2023.e19210. PMID: 37654468; PMCID: PMC10465866

  49. Kim, D., Oh, J., Im, H., Yoon, M., Park, J., Lee, J.: Automatic classification of the Korean triage acuity scale in simulated emergency rooms using speech recognition and natural language processing: a proof of concept study. J. Korean Med. Sci. 36(27), e175 (2021). https://doi.org/10.3346/jkms.2021.36.e175. PMID: 34254471; PMCID: PMC8275459

  50. Yao, L.H., Leung, K.C., Tsai, C.L., Huang, C.H., Fu, L.C.: A novel deep learning-based system for triage in the emergency department using electronic medical records: retrospective cohort study. J. Med. Internet Res. 23(12), e27008 (2021). https://doi.org/10.2196/27008. PMID: 34958305; PMCID: PMC8749584

  51. Jiang, X., Hu, Z., Wang, S., Zhang, Y.: Deep learning for medical image-based cancer diagnosis. Cancers (Basel) 15(14), 3608 (2023). https://doi.org/10.3390/cancers15143608.PMID: 37509272; PMCID: PMC10377683

  52. Amirahmadi, A., Ohlsson, M., Etminani, K.: Deep learning prediction models based on EHR trajectories: a systematic review. J. Biomed. Inf. 144, 104430 (2023). ISSN 1532–0464. https://doi.org/10.1016/j.jbi.2023.104430

  53. Ghazal, T.M., Rehman, A.U., Saleem, M., Ahmad, M., Ahmad, S., Mehmood, F.: Intelligent model to predict early liver disease using machine learning technique. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, pp. 1–5 (2022). https://doi.org/10.1109/ICBATS54253.2022.9758929

  54. Dinesh, K.G., Arumugaraj, K., Santhosh, K.D., Mareeswari, V.: Prediction of cardiovascular disease using machine learning algorithms. In: 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT), Coimbatore, India, pp. 1–7 (2018). https://doi.org/10.1109/ICCTCT.2018.8550857

  55. Birjais, R., Mourya, A.K., Chauhan, R., et al.: Prediction and diagnosis of future diabetes risk: a machine learning approach. SN Appl. Sci. 1, 1112 (2019). https://doi.org/10.1007/s42452-019-1117-9

    Article  Google Scholar 

  56. Mammen, P.M.: Federated learning: opportunities and challenges. arXiv preprint arXiv:2101.05428 (2021)

  57. Liu, B., et al.: Recent advances on federated learning: a systematic survey. arXiv preprint arXiv:2301.01299 (2023)

  58. Whang, S.E., et al.: Data collection and quality challenges in deep learning: a data-centric AI perspective. VLDB J. 32(4), 791–813 (2023)

    Article  Google Scholar 

  59. Liu, Y., et al.: Data quantity governance for machine learning in materials science. Natl. Sci. Rev. (2023). nwad125

    Google Scholar 

  60. Dube, R.: The P versus NP Problem. arXiv e-prints: arXiv-1001 (2010)

    Google Scholar 

  61. Dong, Q., et al.: Large language model for science: a study on P vs. NP. arXiv preprint arXiv:2309.05689 (2023)

  62. Wan, C., Shi, Z.: A proof for P=? NP problem. arXiv preprint arXiv:1005.3010 (2010)

  63. Franzén, M.: The P versus NP brief. arXiv preprint arXiv:0709.1207 (2007)

  64. Yang, G., Ye, Q., Xia, J.: Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: a mini-review, two showcases and beyond. Inf. Fusion 77, 29–52 (2022)

    Article  Google Scholar 

  65. Manresa-Yee, C., Roig-Maimó, M.F., Ramis, S., Mas-Sansó, R.: Advances in XAI: explanation interfaces in healthcare. In: Lim, C.-P., Chen, Y.-W., Vaidya, A., Mahorkar, C., Jain, L.C. (eds.) Handbook of Artificial Intelligence in Healthcare. ISRL, vol. 212, pp. 357–369. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-83620-7_15

    Chapter  Google Scholar 

  66. Chen, H., et al.: Multi-agent consensus seeking via large language models. arXiv preprint arXiv:2310.20151 (2023)

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Authors and Affiliations

  1. Nanomedicine Lab, Imagery and Therapeutics, EA 466, University of Franche-Comté, Besançon, France

    Kokou Edjinedja, Oussama Barakat, Omar Elfahim & Charlotte Bredy-Maux

  2. Emergency Department, Hôpitaux Universitaires de Genève (HUG), Genève, Switzerland

    Thibaut Desmettre

  3. Emergency Department, Centre Hospitalier Universitaire (CHU), Besançon, France

    Tania Marx

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  1. Kokou Edjinedja
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  2. Oussama Barakat
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  4. Tania Marx
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  5. Omar Elfahim
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Correspondence to Kokou Edjinedja .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Edjinedja, K., Barakat, O., Desmettre, T., Marx, T., Elfahim, O., Bredy-Maux, C. (2024). Cross Approach Between Modern Artificial Intelligence and Emergency Medicine: A Review. In: Arai, K. (eds) Intelligent Computing. SAI 2024. Lecture Notes in Networks and Systems, vol 1018. Springer, Cham. https://doi.org/10.1007/978-3-031-62269-4_20

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