1887
Volume 2025, Issue 2
  • EISSN: 2220-2749

Abstract

Artificial intelligence (AI) transforms trauma radiology by improving diagnostic speed, accuracy, and workflow efficiency. This study aims to review AI applications in trauma radiology, focusing on injury detection, emergency management planning, and disaster response systems. The results demonstrate that AI effectively identifies fractures, internal organ injuries, spinal trauma, and soft tissue damage through its high sensitivity and specificity. It also facilitates emergency care triage systems and supports clinical decision-making tools. In addition, AI enables mobile imaging, telemedicine capabilities and optimizes data management during disaster situations. AI shows substantial potential for improving outcomes in critical situations with limited resources despite facing obstacles related to diverse data sources, infrastructure issues, and ethical concerns. AI applications are changing how trauma radiology operates by providing better diagnostic capabilities. Yet, broader clinical implementation depends on resolving technical challenges and ethical and logistical issues while ensuring adequate validation.

© 2025 The Author(s), licensee HBKU Press.
Loading

Article metrics loading...

/content/journals/10.5339/avi.2025.12
2025-09-17
2025-12-07

Metrics

Loading full text...

Full text loading...

/deliver/fulltext/avi/2025/2/avi.2025.12.html?itemId=/content/journals/10.5339/avi.2025.12&mimeType=html&fmt=ahah

References

  1. Hirani R, Noruzi K, Khuram H, Hussaini AS, Aifuwa EI, Ely KE, et al. Artificial intelligence and healthcare: a journey through history, present innovations, and future possibilities. Life (Basel). 2024 May;14:(5):557. https://doi.org/10.3390/life14050557
     [Google Scholar]
  2. Rahimi SA, Légaré F, Sharma G, Archambault P, Zomahoun HTV, Chandavong S, et al. Application of artificial intelligence in community-based primary health care: systematic scoping review and critical appraisal. J Med Internet Res. 2021 Sep 3;23:(9):e29839. https://doi.org/10.2196/29839
     [Google Scholar]
  3. Varma D, Brown P, Clements W. Importance of the mechanism of injury in trauma radiology decision-making. Korean J Radiol. 2023 Jun;24:(6):522–8. https://doi.org/10.3348/kjr.2022.0966
     [Google Scholar]
  4. Hussain K, Verma D, Firoz A, Namiq KS, Raza M, Haris M, et al. Radiology and a radiologist: a keystone in the turmoil of trauma setting. Cureus. 2021 Apr 2;13:(4):e14267. https://doi.org/10.7759/cureus.14267
     [Google Scholar]
  5. O’Keeffe M, Clark S, Khosa F, Mohammed MF, McLaughlin PD, Nicolaou S. Imaging protocols for trauma patients: trauma series, extended focused assessment with sonography for trauma, and selective and whole-body computed tomography. Semin Roentgenol. 2016 Jul 1;51:(3):130–42. https://doi.org/10.1053/j.ro.2016.02.007
     [Google Scholar]
  6. Lex JR, Di Michele J, Koucheki R, Pincus D, Whyne C, Ravi B. Artificial intelligence for hip fracture detection and outcome prediction: a systematic review and meta-analysis. JAMA Netw Open. 2023 Mar 17;6:(3):e233391. https://doi.org/10.1001/jamanetworkopen.2023.3391
     [Google Scholar]
  7. AlSamhori AF, AlSamhori JF, Dib C, AlSamhori ARF, Shehadeh MW, Rihani J, et al. Implication of artificial intelligence on astrocytoma detection and treatment. Avicenna. 2025 Mar 17;2025:(1):3. https://doi.org/10.5339/avi.2025.3
     [Google Scholar]
  8. Kbaiah AT, Nashwan AJ. Beyond the image: artificial intelligence’s role in refining and transforming radiology nursing. Avicenna. 2024 Mar 21;2024:(1):3. https://doi.org/10.5339/avi.2024.3
     [Google Scholar]
  9. Kufel J, Bargieł-Łączek K, Kocot S, Koźlik M, Bartnikowska W, Janik M, et al. What is machine learning, artificial neural networks and deep learning?-Examples of practical applications in medicine. Diagnostics (Basel). 2023 Aug 3;13:(15):2582. https://doi.org/10.3390/diagnostics13152582
     [Google Scholar]
  10. Yamashita R, Nishio M, Do RKG, Togashi K. Convolutional neural networks: an overview and application in radiology. Insights Imaging. 2018 Aug;9:(4):611–29. https://doi.org/10.1007/s13244-018-0639-9
     [Google Scholar]
  11. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May;521:(7553):436–44. https://doi.org/10.1038/nature14539
     [Google Scholar]
  12. Kraus M, Anteby R, Konen E, Eshed I, Klang E. Artificial intelligence for X-ray scaphoid fracture detection: a systematic review and diagnostic test accuracy meta-analysis. Eur Radiol. 2024 Jul 1;34:(7):4341–51. https://doi.org/10.1007/s00330-023-10473-x
     [Google Scholar]
  13. Minamoto Y, Akagi R, Maki S, Shiko Y, Tozawa R, Kimura S, et al. Automated detection of anterior cruciate ligament tears using a deep convolutional neural network. BMC Musculoskelet Disord. 2022 Jun 15;23:(1):577. https://doi.org/10.1186/ s12891-022-05524-1
     [Google Scholar]
  14. Jiang X, Luo Y, He X, Wang K, Song W, Ye Q, et al. Development and validation of the diagnostic accuracy of artificial intelligence-assisted ultrasound in the classification of splenic trauma. Ann Transl Med. 2022 Oct;10:(19):1060. https://doi.org/10.21037/atm-22-3767
     [Google Scholar]
  15. Rosenberg GS, Cina A, Schiró GR, Giorgi PD, Gueorguiev B, Alini M, et al. Artificial intelligence accurately detects traumatic thoracolumbar fractures on sagittal radiographs. Medicina (Kaunas). 2022 Jul;58:(8):998. https://doi.org/10.3390/ medicina58080998
     [Google Scholar]
  16. Emami P, Marzban A. The synergy of artificial intelligence (AI) and Geographic Information Systems (GIS) for enhanced disaster management: opportunities and challenges. Disaster Med Public Health Prep. 2023 Sep;17:e507. https://doi.org/10.1017/dmp.2023.174
     [Google Scholar]
  17. Zhao Z, Ma Y, Mushtaq A, Rajper AMA, Shehab M, Heybourne A, et al. Applications of robotics, artificial intelligence, and digital technologies during COVID-19: a review. Disaster Med Public Health Prep. 2022 Aug;16:(4):1634–44. https://doi.org/10.1017/dmp.2021.9
     [Google Scholar]
  18. Gao X, Lv Q, Hou S. Progress in the application of portable ultrasound combined with artificial intelligence in pre-hospital emergency and disaster sites. Diagnostics (Basel). 2023 Nov;13:(21):3388. https://doi.org/10.3390/ diagnostics13213388
     [Google Scholar]
  19. Gasparyan AY, Ayvazyan L, Blackmore H, Kitas GD. Writing a narrative biomedical review: considerations for authors, peer reviewers, and editors. Rheumatol Int. 2011 Nov;31:(11):1409–17. https://doi.org/10.1007/s00296-011-1999-3
     [Google Scholar]
  20. Kalmet PHS, Sanduleanu S, Primakov S, Wu G, Jochems A, Refaee T, et al. Deep learning in fracture detection: a narrative review. Acta Orthop. 2020 Mar 3;91:(2):215–20. https://doi.org/10.1080/17453674.2019.1711323
     [Google Scholar]
  21. Li M, Jiang Y, Zhang Y, Zhu H. Medical image analysis using deep learning algorithms. Front Public Health. 2023 Nov 7;11:1273253. https://doi.org/10.3389/fpubh.2023.1273253
     [Google Scholar]
  22. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018 Aug;18:(8):500–10. https://doi.org/10.1038/s41568-018-0016-5
     [Google Scholar]
  23. Currie G, Hawk KE, Rohren E, Vial A, Klein R. Machine learning and deep learning in medical imaging: intelligent imaging. J Med Imaging Radiat Sci. 2019 Dec;50:(4):477–87. https://doi.org/10.1016/j.jmir.2019.09.005
     [Google Scholar]
  24. Rezazade Mehrizi MH, Mol F, Peter M, Ranschaert ESantos DP, Shahidi R, et al. The impact of AI suggestions on radiologists’ decisions: a pilot study of explainability and attitudinal priming interventions in mammography examination. Sci Rep. 2023 Jun 7;13:(1):9230. https://doi.org/10.1038/s41598-023-36435-3
     [Google Scholar]
  25. Bachmann R, Gunes G, Hangaard S, Nexmann A, Lisouski P, Boesen M, et al. Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study. BJR Open. 2024 Apr;6:(1):tzae011. https://doi.org/10.1093/bjro/tzae011/7658391
     [Google Scholar]
  26. Bigham-Sadegh A, Oryan A. Basic concepts regarding fracture healing and the current options and future directions in managing bone fractures. Int Wound J. 2015 Jun;12:(3):238–47. https://doi.org/10.1111/iwj.12231
     [Google Scholar]
  27. Rizzo SE, Kenan S. Pathologic fractures. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www. ncbi.nlm.nih.gov/books/NBK559077/ [Accessed 19 August 2024].
     [Google Scholar]
  28. Emmerson BR, Varacallo M, Inman D. Hip fracture overview. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK557514/ [Accessed 19 August 2024].
     [Google Scholar]
  29. Kuo K, Kim AM. Rib fracture. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK541020/ [Accessed 19 August 2024].
     [Google Scholar]
  30. Hsu H, Fahrenkopf MP, Nallamothu SV. Wrist fracture. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK499972/ [Accessed 19 August 2024].
     [Google Scholar]
  31. Wire J, Hermena S, Slane VH. Ankle fractures. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK542324/ [Accessed 19 August 2024].
     [Google Scholar]
  32. Davis DD, Tiwari V, Kane SM, Waseem M. Pelvic fracture. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK430734/ [Accessed 19 August 2024].
     [Google Scholar]
  33. Kuo RYL, Harrison C, Curran TA, Jones B, Freethy A, Cussons D, et al. Artificial intelligence in fracture detection: a systematic review and meta-analysis. Radiology. 2022 Jul;304:(1):50–62. https://doi.org/10.1148/radiol.211785
     [Google Scholar]
  34. Meetschen Mfer L, Beck N, Kroll LSchaarschmidt BM, et al. AI-assisted X-ray fracture detection in residency training: evaluation in pediatric and adult trauma patients. Diagnostics (Basel). 2024 Mar 11;14:(6):596. https://doi.org/10.3390/diagnostics14060596
     [Google Scholar]
  35. O’Rourke MC, Landis R, Burns B. Blunt abdominal trauma. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK431087/ [Accessed 20 August 2024].
     [Google Scholar]
  36. Cheng CT, Lin HS, Hsu CP, Chen HW, Huang JF, Fu CY, et al. The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study. Int J Surg. 2023 May;109:(5):1115–24. https://doi.org/10.1097/JS9.0000000000000380
     [Google Scholar]
  37. Ginsburg J, Huff JS. Closed head trauma. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK557861/ [Accessed 20 August 2024].
     [Google Scholar]
  38. Kiefer J, Kopp M, Ruettinger T, Heiss R, Wuest W, Amarteifio P, et al. Diagnostic accuracy and performance analysis of a scanner-integrated artificial intelligence model for the detection of intracranial hemorrhages in a traumatology emergency department. Bioengineering (Basel). 2023 Nov 28;10:(12):1362. https://doi.org/10.3390/bioengineering10121362
     [Google Scholar]
  39. Jnawali K, Arbabshirani MR, Rao N, Patel AA. Deep 3D convolution neural network for CT brain hemorrhage classification. In: Mori K, Petrick N, (eds.), Medical Imaging 2018: Computer-Aided Diagnosis. Houston: SPIE; 2018. p. 47. ://www.spiedigitallibrary.org/conference-proceedings-of-spie/10575/2293725/Deep-3D-convolution-neural-network-for-CT-brain-hemorrhage-classification/10.1117/12.2293725.full [Accessed 16 August 2024].
     [Google Scholar]
  40. Dydyk AM, Munakomi S, Das JM. Vertebral augmentation. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK547726/ [Accessed 20 August 2024].
     [Google Scholar]
  41. Martín-Noguerol T, Oñate Miranda M, Amrhein TJ, Paulano-Godino F, Xiberta P, Vilanova JC, et al. The role of artificial intelligence in the assessment of the spine and spinal cord. Eur J Radiol. 2023 Apr;161:110726. https://doi.org/10.1016/j.ejrad.2023.110726
     [Google Scholar]
  42. Shen L, Gao C, Hu S, Kang D, Zhang Z, Xia D, et al. Using artificial intelligence to diagnose osteoporotic vertebral fractures on plain radiographs. J Bone Miner Res. 2023 Sep;38:(9):1278–87. https://doi.org/10.1002/jbmr.4879
     [Google Scholar]
  43. Burns JE, Yao J, Muñoz H, Summers RM. Automated detection, localization, and classification of traumatic vertebral body fractures in the thoracic and lumbar spine at CT. Radiology. 2016 Jan;278:(1):64–73. https://doi.org/10.1148/radiol.2015142346
     [Google Scholar]
  44. Evans J, Mabrouk A, Nielson J. Anterior cruciate ligament knee injury. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK499848/ [Accessed 20 August 2024].
  45. May T, Garmel GM. Rotator cuff injury. In: StatPearls. Treasure Island, FL: StatPearls Publishing; 2024. http://www.ncbi.nlm.nih.gov/books/NBK547664/ [Accessed 20 August 2024].
  46. Morag Y, Jacobson JA, Miller B, De Maeseneer M, Girish G, Jamadar D. MR imaging of rotator cuff injury: what the clinician needs to know. RadioGraphics. 2006 Jul;26:(4):1045–65. https://doi.org/10.1148/rg.264055087
     [Google Scholar]
  47. Zhan H, Teng F, Liu Z, Yi Z, He J, Chen Y, et al. Artificial intelligence aids detection of rotator cuff pathology: a systematic review. Arthrosc J Arthrosc Relat Surg. 2024 Feb;40:(2):567–78. https://doi.org/10.1016/j.arthro.2023.06.018
     [Google Scholar]
  48. Lee KC, Cho Y, Ahn KS, Park HJ, Kang YS, Lee S, et al. Deep-learning-based automated rotator cuff tear screening in three planes of shoulder MRI. Diagnostics (Basel). 2023 Oct 19;13:(20):3254. https://doi.org/10.3390/diagnostics13203254
     [Google Scholar]
  49. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019 Jan;25:(1):44–56. https://doi.org/10.1038/s41591-018-0300-7
     [Google Scholar]
  50. Ventura CA, Denton E, David J. Artificial intelligence in emergency trauma care: a preliminary scoping review. Med Devices (Auckl). 2024 May17:191–211. https://doi.org/10.2147/MDER.S467146
     [Google Scholar]
  51. Jalal S, Parker W, Ferguson D, Nicolaou S. Exploring the role of artificial intelligence in an emergency and trauma radiology department. Can Assoc Radiol J. 2021 Feb;72:(1):167–74. https://doi.org/10.1177/0846537120918338
     [Google Scholar]
  52. Stonko DP, Guillamondegui OD, Fischer PE, Dennis BM. Artificial intelligence in trauma systems. Surgery. 2021 Jun;169:(6):1295–9. https://doi.org/10.1016/j.surg.2020.07.038
     [Google Scholar]
  53. Peng HT, Siddiqui MM, Rhind SG, Zhang JLuz LT, Beckett A. Artificial intelligence and machine learning for hemorrhagic trauma care. Mil Med Res. 2023 Feb 16;10:(1):6. https://doi.org/10.1186/s40779-023-00444-0
     [Google Scholar]
  54. Wang D, Wang L, Zhang Z, Wang D, Zhu H, Gao Y, et al. “Brilliant AI doctor” in rural clinics: challenges in AI-powered clinical decision support system deployment. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. Yokohama Japan: ACM; 2021. p. 1–18. https://doi.org/10.1145/3411764.3445432
     [Google Scholar]
  55. Wang Q, Yuan L, Ding X, Zhou Z. Prediction and diagnosis of venous thromboembolism using artificial intelligence approaches: a systematic review and meta-analysis. Clin Appl Thromb Hemost. 2021 Jan 1;27:107602962110211. https://doi.org/10.1177/10760296211021162
     [Google Scholar]
  56. Nemeth CBinks A, Burris C, Keeney N, Pinevich Y, Pickering BW, et al. Decision support for tactical combat casualty care using machine learning to detect shock. Mil Med. 2021 Jan 25;186:(Supplement_1):273–80. https://doi.org/10.1093/milmed/usaa275
     [Google Scholar]
  57. Wang X, Ezeana CF, Wang L, Puppala M, Huang Y, He Y, et al. Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia. Alzheimers Dement (N Y). 2022 Sep;8:(1):e12351. https://doi.org/10.1002/trc2.12351
     [Google Scholar]
  58. Chang MC, Kim JK, Park D, Kim JH, Kim CR, Choo YJ. The use of artificial intelligence to predict the prognosis of patients undergoing central nervous system rehabilitation: a narrative review. Healthcare (Basel). 2023 Oct 6;11:(19):2687. https://doi.org/10.3390/healthcare11192687
     [Google Scholar]
  59. Dietz N, Jaganathan V, Alkin V, Mettille J, Boakye M, Drazin D. Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): a systematic review. J Clin Orthop Trauma. 2022 Oct;35:102046. https://doi.org/10.1016/j.jcot.2022.102046
     [Google Scholar]
  60. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019 Mar;69:(2):127–57. https://doi.org/10.3322/caac.21552
     [Google Scholar]
  61. Burger B, Bernathova M, Helbich T, Singer CF, Langs G. AI-based prediction of lesion occurrence in high-risk women based on anomalies detected in follow-up examinations. In: Van Ongeval C, Marshall N, Bosmans H, (eds.), 15th International Workshop on Breast Imaging (IWBI2020). Leuven, Belgium: SPIE; 2020. p. 83. https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11513/2564313/AI-based-prediction-of-lesion-occurrence-in- high-risk-women/10.1117/12.2564313.full [Accessed 17 July 2024].
  62. Mohamed Shaluf I. An overview on disasters. Disaster Prev Manag Int J. 2007 Nov 13;16:(5):687–703. https://doi.org/10.1108/09653560710837000
     [Google Scholar]
  63. Davis JR, Wilson SMartin A, Glover S, Svendsen ER. The impact of disasters on populations with health and health care disparities. Disaster Med Public Health Prep. 2010 Mar;4:(1):30–8. https://doi.org/10.1017/s1935789300002391
     [Google Scholar]
  64. Al-Jazairi AF. Disasters and disaster medicine. In: Subhy Alsheikhly A (ed.), Essentials of Accident and Emergency Medicine. IntechOpen; 2019. https://www.intechopen.com/books/essentials-of-accident-and-emergency- medicine/disasters-and-disaster-medicine [Accessed 2 September 2024].
     [Google Scholar]
  65. Yari A, Zarezadeh Y, Fatemi F, Ardalan A, Vahedi S, Yousefi-Khoshsabeghe H, et al. Disaster safety assessment of primary healthcare facilities: a cross-sectional study in Kurdistan province of Iran. BMC Emerg Med. 2021 Feb 23;21:(1):23. https://doi.org/10.1186/s12873-021-00417-3
     [Google Scholar]
  66. Nunavath V, Goodwin M. The use of artificial intelligence in disaster management - a systematic literature review. In 2019 International Conference on Information and Communication Technologies for Disaster Management (ICT-DM). Paris, France: IEEE; 2019. p. 1–8. https://ieeexplore.ieee.org/document/9032935/ [Accessed 1 September 2024].
  67. Mollura DJ, Culp MP, Pollack E, Battino G, Scheel JR, Mango VL, et al. Artificial intelligence in low- and middle-income countries: innovating global health radiology. Radiology. 2020 Dec;297:(3):513–20. https://doi.org/10.1148/ radiol.2020201434
     [Google Scholar]
  68. Petersson L, Larsson I, Nygren JM, Nilsen P, Neher M, Reed JE, et al. Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden. BMC Health Serv Res. 2022 Jul 1;22:(1):850. https://doi.org/10.1186/s12913-022-08215-8
     [Google Scholar]
  69. Alami H, Rivard L, Lehoux P, Hoffman SJ, Cadeddu SBM, Savoldelli M, et al. Artificial intelligence in health care: laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Global Health. 2020 Jun;16:(1):52. https://doi.org/10.1186/s12992-020-00584-1
     [Google Scholar]
  70. Davenport T, Kalakota R. The potential for artificial intelligence in healthcare. Future Healthc J. 2019 Jun;6:(2):94–8. https://doi.org/10.7861/futurehosp.6-2-94
     [Google Scholar]
  71. Andersen PAB, Precht H, McEntee MF, Pedersen MRV. How to set up a mobile X-ray unit in the community - implementation initiatives for patient-centred care. Radiography (Lond). 2023 May;29:S148–51. https://doi.org/10.1016/j. radi.2023.02.027
     [Google Scholar]
  72. Wong KP, Homer SY, Wei SH, Yaghmai NPaz OA, Young TJ, et al. Integration and evaluation of chest X-ray artificial intelligence in clinical practice. J Med Imaging (Bellingham). 2023 Sep;10:(5):051805. https://doi.org/10.1117/1. JMI.10.5.051805
     [Google Scholar]
  73. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021 May;71:(3):209–49. https://doi.org/10.3322/caac.21660
     [Google Scholar]
  74. Jonas DE, Reuland DS, Reddy SM, Nagle M, Clark SD, Weber RP, et al. Screening for lung cancer with low-dose computed tomography: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. 2021 Mar 9;325:(10):971–87. https://doi.org/10.1001/jama.2021.0377
     [Google Scholar]
  75. Khosrow-Pour DBAM. (ed.) Encyclopedia of Information Science and Technology, Fourth Edition. IGI Global; 2018. http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-5225-2255-3 [Accessed 22 September 2024].
  76. Suo J, Zhang W, Gong J, Yuan X, Brady DJ, Dai Q. Computational imaging and artificial intelligence: the next revolution of mobile vision. In Proceedings of the IEEE. 2023 Dec;111:(12):1607–39. https://ieeexplore.ieee.org/document/10355958/ [Accessed 19 August 2024].
     [Google Scholar]
  77. Sever L, Pehlivan G, Canpolat N, Saygılı S, Ağbaş A, Demirgan E, et al. Management of pediatric dialysis and kidney transplant patients after natural or man-made disasters. Pediatr Nephrol. 2023 Feb;38:(2):315–25. https://doi.org/10.1007/s00467-022-05734-8
     [Google Scholar]
  78. Peleg K, Bodas M, Hertelendy AJ, Kirsch TD. The COVID-19 pandemic challenge to the All-Hazards Approach for disaster planning. Int J Disaster Risk Reduct. 2021 Mar;55:102103. https://doi.org/10.1016/j.ijdrr.2021.102103
     [Google Scholar]
  79. Vaishya R, Javaid M, Khan IH, Haleem A. Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr. 2020 Jul–Aug;14:(4):337–9. https://doi.org/10.1016/j.dsx.2020.04.012
     [Google Scholar]
  80. Guefrechi S, Jabra MB, Ammar A, Koubaa A, Hamam H. Deep learning based detection of COVID-19 from chest X-ray images. Multimed Tools Appl. 2021;80:(21–23):31803–20. https://doi.org/10.1007/s11042-021-11192-5
     [Google Scholar]
  81. Panwar H, Gupta PK, Siddiqui MKMenendez R, Singh V. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet. Chaos Solitons Fractals. 2020 Sep;138:109944. https://doi.org/10.1016/j.chaos.2020.109944
     [Google Scholar]
  82. Awan MJ, Bilal MH, Yasin A, Nobanee H, Khan NS, Zain AM. Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach. Int J Environ Res Public Health. 2021 Sep 27;18:(19):10147. https://doi.org/10.3390/ijerph181910147
     [Google Scholar]
  83. Sahloul MZHassan J, Sankari A, Kherallah M, Atassi B, Badr S, et al. War is the enemy of health. Pulmonary, critical care, and sleep medicine in war-torn Syria. Ann Am Thorac Soc. 2016 Feb;13:(2):147–55. https://doi.org/10.1513/ AnnalsATS.201510-661PS
     [Google Scholar]
  84. Simeon JC. The use and abuse of forced migration and displacement as a weapon of war. Front Hum Dyn. 2023 Jul 6;5:1172954. https://doi.org/10.3389/fhumd.2023.1172954
     [Google Scholar]
  85. Sakula A. Robert Koch: centenary of the discovery of the tubercle bacillus, 1882. Thorax. 1982 Apr;37:(4):246–51. https://doi.org/10.1136/thx.37.4.246
     [Google Scholar]
  86. Figueroa-Munoz JIPardo P. Tuberculosis control in vulnerable groups. Bull World Health Organ. 2008 Aug;86:(9):733–5. https://doi.org/10.2471/BLT.06.038737
     [Google Scholar]
  87. Bloom BR, Atun R, Cohen T, Dye C, Fraser H, Gomez GB, et al. Tuberculosis. In: Holmes KK, Bertozzi S, Bloom BR, Jha P, (eds.), Major Infectious Diseases. 3rd ed. Washington, DC: The International Bank for Reconstruction and Development/The World Bank; 2017. http://www.ncbi.nlm.nih.gov/books/NBK525174/ [Accessed 22 September 2024].
  88. Maartens G, Wilkinson RJ. Tuberculosis. Lancet. 2007 Dec 15;370:(9604):2030–43. https://doi.org/10.1016/S0140-6736(07)61262-8
     [Google Scholar]
  89. Lange C, Mori T. Advances in the diagnosis of tuberculosis. Respirology. 2010 Feb;15:(2):220–40. https://doi.org/10.1111/j.1440- 1843.2009.01692.x
     [Google Scholar]
  90. Kulkarni S, Jha S. Artificial intelligence, radiology, and tuberculosis: a review. Acad Radiol. 2020 Jan;27:(1):71–5. https://doi.org/10.1016/j.acra.2019.10.003
     [Google Scholar]
  91. Nachiappan AC, Rahbar K, Shi X, Guy ESBarbosa EJ, Shroff GS, et al. Pulmonary tuberculosis: role of radiology in diagnosis and management. RadioGraphics. 2017 Jan;37:(1):52–72. https://doi.org/10.1148/rg.2017160032
     [Google Scholar]
  92. Du J, Su Y, Qiao J, Gao S, Dong E, Wang R, et al. Application of artificial intelligence in diagnosis of pulmonary tuberculosis. Chin Med J (Engl). 2024 Mar 5;137:(5):559–61. https://doi.org/10.1097/CM9.0000000000003018
     [Google Scholar]
  93. Khan FA, Majidulla A, Tavaziva G, Nazish A, Abidi SK, Benedetti A, et al. Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture- confirmed disease. Lancet Digit Health. 2020 Nov;2:(11):e573–81. https://doi.org/10.1016/S2589-7500(20)30221-1
     [Google Scholar]
  94. Lee S, Fox S, Smith R, Skrobarcek KA, Keyserling H, Phares CR, et al. Development and validation of a deep learning model for detecting signs of tuberculosis on chest radiographs among US-bound immigrants and refugees. medRxriv. 2024. http://medrxiv.org/lookup/doi/10.1101/2024.02.27.24303429 [Accessed 1 September 2024].
  95. Zuhair V, Babar A, Ali R, Oduoye MO, Noor Z, Chris K, et al. Exploring the impact of artificial intelligence on global health and enhancing healthcare in developing nations. J Prim Care Community Health. 2024 Jan-Dec;15:21501319241245847. https://doi.org/10.1177/21501319241245847
     [Google Scholar]
  96. Farhud DD, Zokaei S. Ethical issues of artificial intelligence in medicine and healthcare. Iran J Public Health. 2021 Nov;50:(11):i–v. https://doi.org/10.18502/ijph.v50i11.7600
     [Google Scholar]
  97. Sharma M, Savage C, Nair M, Larsson I, Svedberg P, Nygren JM. Artificial intelligence applications in health care practice: scoping review. J Med Internet Res. 2022 Oct 5;24:(10):e40238. https://doi.org/10.2196/40238
     [Google Scholar]
  98. Elendu C, Amaechi DC, Elendu TC, Jingwa KA, Okoye OK, John Okah M, et al. Ethical implications of AI and robotics in healthcare: a review. Medicine (Baltimore). 2023 Dec 15;102:(50):e36671. https://doi.org/10.1097/MD.0000000000036671
     [Google Scholar]
  99. European Society of Radiology (ESR). What the radiologist should know about artificial intelligence - an ESR white paper. Insights Imaging. 2019 Apr;10:(1):44. https://doi.org/10.1186/s13244-019-0738-2
     [Google Scholar]
  100. European Society of Radiology (ESR). The new EU General Data Protection Regulation: what the radiologist should know. Insights Imaging. 2017 Jun;8:(3):295–9. https://doi.org/10.1007/s13244-017-0552-7
     [Google Scholar]
  101. U.S. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/ Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD). 2019 Apr.
  102. Brady AP, Neri E. Artificial intelligence in radiology—ethical considerations. Diagnostics (Basel). 2020 Apr 17;10:(4):231. https://doi.org/10.3390/diagnostics10040231
     [Google Scholar]
  103. Ebers M, Hoch VRS, Rosenkranz F, Ruschemeier Htter B. The European Commission’s Proposal for an Artificial Intelligence Act—A Critical Assessment by Members of the Robotics and AI Law Society (RAILS). J. 2021 Oct 8;4:(4):589–603. https://doi.org/10.3390/j4040043
     [Google Scholar]
  104. Brown NA, Carey CH, Gerry EI. FDA releases action plan for artificial intelligence/machine learning-enabled software as a medical device. J Robot Artif Intell Law. 2021;4:255–60.
     [Google Scholar]
  105. U.S. Food and Drug Administration (FDA). Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. U.S. Food and Drug Administration (FDA); 2021. https://www.fda.gov/media/145022/download?attachment [Accessed 19 July 2025].
     [Google Scholar]
  106. U.S. Food and Drug Administration. Digital Health Technologies for Remote Data Acquisition in Clinical Investigations. U.S. Food and Drug Administration; 2023. https://www.fda.gov/media/177030/download [Accessed 19 July 2025].
     [Google Scholar]
  107. U.S. Food and Drug Administration. Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations. 2025. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/artificial-intelligence-enabled-device-software-functions-lifecycle-management-and-marketing
     [Google Scholar]
/content/journals/10.5339/avi.2025.12
Loading
The implication of artificial intelligence in radiographic evaluation of trauma and acute care
Avicenna 2025, 12 (2025); https://doi.org/10.5339/avi.2025.12
/content/journals/10.5339/avi.2025.12
/content/journals/10.5339/avi.2025.12
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Artificial intelligence, emergency radiology AI applications, radiology, telemedicine in trauma imaging and trauma settings

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error