References · 55 sources

Advancing Pediatric Radiology Through Artificial Intelligence: Global Progress and Implications for Middle- and Low-Income Countries

Amreen S, Khairy A, Masood F, Chu N, Paudel A, Mohamed AA, Oluwabusayomi A, Alnasser Y.

AI 2026, 7(6), 222 · https://doi.org/10.3390/ai7060222

  1. Najjar, R. Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics 2023, 13, 2760.
  2. Martin, D.; Tong, E.; Kelly, B.; Yeom, K.; Yedavalli, V. Current Perspectives of Artificial Intelligence in Pediatric Neuroradiology: An Overview. Front. Radiol. 2021, 1, 713681.
  3. Giganti, F.; Panebianco, V.; Tempany, C.M.; Purysko, A.S. Is Artificial Intelligence Replacing Our Radiology Stars in Prostate Magnetic Resonance Imaging? The Stars Do Not Look Big, But They Can Look Brighter. Eur. Urol. Open Sci. 2022, 48, 12–13.
  4. Waymel, Q.; Badr, S.; Demondion, X.; Cotten, A.; Jacques, T. Impact of the rise of artificial intelligence in radiology: What do radiologists think? Diagn. Interv. Imaging 2019, 100, 327–336.
  5. Ng, C.K.C. Artificial Intelligence for R Yadiation Dose Optimization in Pediatric Radiology: A Systematic Review. Children 2022, 9, 1044.
  6. Sammer, M.B.K.; Akbari, Y.S.; Barth, R.A.; Blumer, S.L.; Dillman, J.R.; Farmakis, S.G.; Frush, D.P.; Gokli, A.; Halabi, S.S.; Iyer, R.; et al. Use of Artificial Intelligence in Radiology: Impact on Pediatric Patients, a White Paper From the ACR Pediatric AI Workgroup. J. Am. Coll. Radiol. 2023, 20, 730–737.
  7. Ng, C.K.C. Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review. Children 2023, 10, 525.
  8. Frija, G.; Blažić, I.; Frush, D.P.; Hierath, M.; Kawooya, M.; Donoso-Bach, L.; Brkljačić, B. How to improve access to medical imaging in low- and middle-income countries? eClinicalMedicine 2021, 38, 101034.
  9. Marey, A.; Ambrozaite, O.; Afifi, A.; Agarwal, R.; Chellappa, R.; Adeleke, S.; Umair, M. A perspective on AI implementation in medical imaging in LMICs: Challenges, priorities, and strategies. Eur. Radiol. 2025, 36, 2591–2602.
  10. Mollura, D.J.; Culp, M.P.; Pollack, E.; Battino, G.; Scheel, J.R.; Mango, V.L.; Elahi, A.; Schweitzer, A.; Dako, F. Artificial Intelligence in Low- and Middle-Income Countries: Innovating Global Health Radiology. Radiology 2020, 297, 513–520.
  11. Ciecierski-Holmes, T.; Singh, R.; Axt, M.; Brenner, S.; Barteit, S. Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: A systematic scoping review. npj Digit. Med. 2022, 5, 162.
  12. Yang, J.; Dung, N.T.; Thach, P.N.; Phong, N.T.; Phu, V.D.; Phu, K.D.; Yen, L.M.; Thy, D.B.X.; Soltan, A.A.S.; Thwaites, L.; et al. Generalizability assessment of AI models across hospitals in a low-middle and high income country. Nat. Commun. 2024, 15, 8270.
  13. Zech, J.R.; Jaramillo, D.; Altosaar, J.; Popkin, C.A.; Wong, T.T. Artificial intelligence to identify fractures on pediatric and young adult upper extremity radiographs. Pediatr. Radiol. 2023, 53, 2386–2397.
  14. Franco, P.N.; Maino, C.; Mariani, I.; Gandola, D.G.; Sala, D.; Bologna, M.; Talei Franzesi, C.; Corso, R.; Ippolito, D. Diagnostic performance of an AI algorithm for the detection of appendicular bone fractures in pediatric patients. Eur. J. Radiol. 2024, 178, 111637.
  15. Zech, J.R.; Ezuma, C.O.; Patel, S.; Edwards, C.R.; Posner, R.; Hannon, E.; Williams, F.; Lala, S.V.; Ahmad, Z.Y.; Moy, M.P.; et al. Artificial intelligence improves resident detection of pediatric and young adult upper extremity fractures. Skelet. Radiol. 2024, 53, 2643–2651.
  16. Till, T.; Scherkl, M.; Stranger, N.; Singer, G.; Hankel, S.; Flucher, C.; Hržić, F.; Štajduhar, I.; Tschauner, S. Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays. Eur. Radiol. 2025, 35, 6853–6864.
  17. Lee, M.; Choi, Y.H.; Lee, S.B.; Choi, J.W.; Lee, S.; Hwang, J.Y.; Cheon, J.E.; Hong, S.; Kim, J.; Cho, Y.J. Retrospective Clinical Trial to Evaluate the Effectiveness of a New Tanner-Whitehouse-Based Bone Age Assessment Algorithm Trained with a Deep Neural Network System. Diagnostics 2025, 15, 993.
  18. Morcos, G.; Yi, P.H.; Jeudy, J. Applying Artificial Intelligence to Pediatric Chest Imaging: Reliability of Leveraging Adult-Based Artificial Intelligence Models. J. Am. Coll. Radiol. 2023, 20, 742–747.
  19. Truong, B.; Zapala, M.; Kammen, B.; Luu, K. Automated Detection of Pediatric Foreign Body Aspiration from Chest X-rays Using Machine Learning. Laryngoscope 2024, 134, 3807–3814.
  20. Agarwal, P.; Rau, A.; Ngo, H.; Seth, A.; Bamberg, F.; Kotter, E.; Weiss, J. Deep learning for pediatric chest x-ray diagnosis: Repurposing a commercial tool developed for adults. PLoS ONE 2025, 20, e0328295.
  21. Domínguez-Rodríguez, S.; Liz-López, H.; Panizo-LLedot, A.; Ballesteros, Á.; Dagan, R.; Greenberg, D.; Gutiérrez, L.; Rojo, P.; Otheo, E.; Galán, J.C.; et al. Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis. Comput. Methods Programs Biomed. 2023, 242, 107765.
  22. Behzadi-Khormouji, H.; Rostami, H.; Salehi, S.; Derakhshande-Rishehri, T.; Masoumi, M.; Salemi, S.; Keshavarz, A.; Gholamrezanezhad, A.; Assadi, M.; Batouli, A. Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images. Comput. Methods Programs Biomed. 2020, 185, 105162.
  23. Capellán-Martín, D.; Gómez-Valverde, J.J.; Sánchez-Jacob, R.; Hernanz-Lobo, A.; Schaaf, H.S.; García-Delgado, L.; Augusto, O.; Roshanitabrizi, P.; García-Basteiro, A.L.; Ribó, J.L.; et al. Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis. Nat. Commun. 2025, 16, 9170.
  24. Pei, Y.; Wang, G.; Cao, H.; Jiang, S.; Wang, D.; Wang, H.; Wang, H.; Yu, H. A deep-learning pipeline to diagnose pediatric intussusception and assess severity during ultrasound scanning: A multicenter retrospective-prospective study. npj Digit. Med. 2023, 6, 182.
  25. Chen, M.; Cai, R.; Zhang, A.; Chi, X.; Qian, J. The diagnostic value of artificial intelligence-assisted imaging for developmental dysplasia of the hip: A systematic review and meta-analysis. J. Orthop. Surg. Res. 2024, 19, 522.
  26. Hareendranathan, A.R.; Chahal, B.S.; Zonoobi, D.; Sukhdeep, D.; Jaremko, J.L. Artificial Intelligence to Automatically Assess Scan Quality in Hip Ultrasound. Indian J. Orthop. 2021, 55, 1535–1542.
  27. Lin-Martore, M.; Kornblith, A.; Firnberg, M.; Haque, A.; O’Brien, B. Trust of Artificial Intelligence-Augmented Point-of-Care Ultrasound Among Pediatric Emergency Physicians. J. Am. Coll. Emerg. Physicians Open 2025, 6, 100173.
  28. Yan, L.; Li, Q.; Fu, K.; Zhou, X.; Zhang, K. Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis. Bioengineering 2025, 12, 288.
  29. Chen, L.; Zeng, B.; Shen, J.; Xu, J.; Cai, Z.; Su, S.; Chen, J.; Cai, X.; Ying, T.; Hu, B.; et al. Bone age assessment based on three-dimensional ultrasound and artificial intelligence compared with paediatrician-read radiographic bone age: Protocol for a prospective, diagnostic accuracy study. BMJ Open 2024, 14, e079969.
  30. Davendralingam, N.; Sebire, N.J.; Arthurs, O.J.; Shelmerdine, S.C. Artificial intelligence in paediatric radiology: Future opportunities. Br. J. Radiol. 2021, 94, 20200975.
  31. Wu, L.; Dong, B.; Liu, X.; Hong, W.; Chen, L.; Gao, K.; Sheng, Q.; Yu, Y.; Zhao, L.; Zhang, Y. Standard Echocardiographic View Recognition in Diagnosis of Congenital Heart Defects in Children Using Deep Learning Based on Knowledge Distillation. Front. Pediatr. 2022, 9, 770182.
  32. Chen, W.; Wu, J.; Zhang, Z.; Gao, Z.; Chen, X.; Zhang, Y.; Lin, Z.; Tang, Z.; Yu, W.; Fan, S.; et al. Artificial intelligence-assisted echocardiographic monitoring in pediatric patients on extracorporeal membrane oxygenation. Front. Cardiovasc. Med. 2024, 11, 1418741.
  33. Day, T.G.; Matthew, J.; Budd, S.F.; Venturini, L.; Wright, R.; Farruggia, A.; Vigneswaran, T.V.; Zidere, V.; Hajnal, J.V.; Razavi, R.; et al. Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: How can we optimize collaborative performance? Ultrasound Obstet. Gynecol. 2024, 64, 28–35.
  34. Ali, R.; Li, H.; Zhang, H.; Pan, W.; Reeder, S.B.; Harris, D.; Masch, W.; Aslam, A.; Shanbhogue, K.; Bernieh, A.; et al. Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI. Eur. Radiol. 2025, 35, 4362–4373.
  35. Pastore, L.V.; Sudhakar, S.V.; Mankad, K.; De Vita, E.; Biswas, A.; Tisdall, M.M.; Chari, A.; Figini, M.; Tahir, M.Z.; Adler, S.; et al. Integrating standard epilepsy protocol, ASL-perfusion, MP2RAGE/EDGE and the MELD-FCD classifier in the detection of subtle epileptogenic lesions: A 3 Tesla MRI pilot study. Neuroradiology 2025, 67, 665–675.
  36. Murali, S.; Ding, H.; Adedeji, F.; Qin, C.; Obungoloch, J.; Asllani, I.; Anazodo, U.; Ntusi, N.A.B.; Mammen, R.; Niendorf, T.; et al. Bringing MRI to low- and middle-income countries: Directions, challenges and potential solutions. NMR Biomed. 2024, 37, e4992.
  37. He, M.; Yuan, J.; Liu, A.; Pu, R.; Yu, W.; Wang, Y.; Wang, L.; Nie, X.; Yi, J.; Xue, H.; et al. A Cohort Study of Pediatric Severe Community-Acquired Pneumonia Involving AI-Based CT Image Parameters and Electronic Health Record Data. Infect. Dis. Ther. 2025, 14, 2131–2141.
  38. Zhang, Q.; Hu, Y.; Zhou, C.; Zhao, Y.; Zhang, N.; Zhou, Y.; Yang, Y.; Zheng, H.; Fan, W.; Liang, D.; et al. Reducing pediatric total-body PET/CT imaging scan time with multimodal artificial intelligence technology. EJNMMI Phys. 2024, 11, 1.
  39. Meng, F.; Kottlors, J.; Shahzad, R.; Liu, H.; Fervers, P.; Jin, Y.; Rinneburger, M.; Le, D.; Weisthoff, M.; Liu, W.; et al. AI support for accurate and fast radiological diagnosis of COVID-19: An international multicenter, multivendor CT study. Eur. Radiol. 2023, 33, 4280–4291.
  40. Langlotz, C.P. Will Artificial Intelligence Replace Radiologists? Radiol. Artif. Intell. 2019, 1, e190058.
  41. Marey, A.; Arjmand, P.; Alerab, A.D.S.; Eslami, M.J.; Saad, A.M.; Sanchez, N.; Umair, M. Explainability, transparency and black box challenges of AI in radiology: Impact on patient care in cardiovascular radiology. Egypt. J. Radiol. Nucl. Med. 2024, 55, 183.
  42. Mazurowski, M.A. Artificial Intelligence May Cause a Significant Disruption to the Radiology Workforce. J. Am. Coll. Radiol. 2019, 16, 1077–1082.
  43. Otjen, J.P.; Moore, M.M.; Romberg, E.K.; Perez, F.A.; Iyer, R.S. The current and future roles of artificial intelligence in pediatric radiology. Pediatr. Radiol. 2022, 52, 2065–2073.
  44. Hardy, M.; Harvey, H. Artificial intelligence in diagnostic imaging: Impact on the radiography profession. Br. J. Radiol. 2020, 93, 20190840.
  45. Shin, H.J.; Son, N.H.; Kim, M.J.; Kim, E.K. Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs. Sci. Rep. 2022, 12, 10215.
  46. Strohm, L.; Hehakaya, C.; Ranschaert, E.R.; Boon, W.P.C.; Moors, E.H.M. Implementation of artificial intelligence (AI) applications in radiology: Hindering and facilitating factors. Eur. Radiol. 2020, 30, 5525–5532.
  47. Straus Takahashi, M.; Donnelly, L.F.; Siala, S. Artificial intelligence: A primer for pediatric radiologists. Pediatr. Radiol. 2024, 54, 2127–2142.
  48. Jungmann, F.; Jorg, T.; Hahn, F.; Pinto Dos Santos, D.; Jungmann, S.M.; Düber, C.; Mildenberger, P.; Kloeckner, R. Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry. Acad. Radiol. 2021, 28, 834–840.
  49. Santomartino, S.M.; Yi, P.H. Systematic Review of Radiologist and Medical Student Attitudes on the Role and Impact of AI in Radiology. Acad. Radiol. 2022, 29, 1748–1756.
  50. Canadian Association of Radiologists (CAR) Artificial Intelligence Working Group. Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology. Can. Assoc. Radiol. J. 2019, 70, 107–118.
  51. Pew Research Center. 60% of Americans Would be Uncomfortable with Provider Relying on AI in Their Own Health Care; Pew Research Center: Washington, DC, USA, 2023; Available online: https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/ (accessed on 27 January 2026).
  52. Pesapane, F.; Volonté, C.; Codari, M.; Sardanelli, F. Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States. Insights Imaging 2018, 9, 745–753.
  53. Edzie, E.K.M.; Dzefi-Tettey, K.; Asemah, A.R.; Brakohiapa, E.K.; Asiamah, S.; Quarshie, F.; Amankwa, A.T.; Raj, A.; Nimo, O.; Boadi, E.; et al. Perspectives of radiologists in Ghana about the emerging role of artificial intelligence in radiology. Heliyon 2023, 9, e15558.
  54. Singh, S.; Elahi, A.; Schweitzer, A.; Adekanmi, A.; Atalabi, O.; Mollura, D.J.; Dako, F. Deploying Artificial Intelligence for Thoracic Imaging Around the World. J. Am. Coll. Radiol. 2023, 20, 859–862.
  55. Hua, S.B.Z.; Heller, N.; He, P.; Towbin, A.J.; Chen, I.Y.; Lu, A.X.; Erdman, L. Lack of children in public medical imaging data points to growing age bias in biomedical AI. medRxiv 2025.