ARTIFICIAL INTELLIGENCE IN PEDIATRIC RADIOLOGY: CLINICAL APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS

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Published: 2026-01-19
DOI: https://doi.org/10.55640/

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Murtazayeva Zilola Fakhriddinovna
Lecturer at the Department of Pre-Clinical Sciences Asia International University

Abstract

Artificial intelligence (AI) is rapidly reshaping pediatric radiology by improving diagnostic accuracy, optimizing workflow efficiency, and reducing radiation exposure. Pediatric imaging presents unique challenges due to developmental anatomy and heightened radiation sensitivity. This comprehensive review discusses current AI applications, clinical impact, challenges, and future perspectives in pediatric radiology.

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Vol. 5 No. 01 (2026): Journal of Multidisciplinary Sciences and Innovations

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Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.

How to Cite

ARTIFICIAL INTELLIGENCE IN PEDIATRIC RADIOLOGY: CLINICAL APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS. (2026). Journal of Multidisciplinary Sciences and Innovations, 5(01), 1269-1270. https://doi.org/10.55640/

References

1.Esteva A et al. A guide to deep learning in healthcare. Nature. 2019.

2.Litjens G et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017.

3.McBee MP et al. Deep learning in radiology. Radiology. 2018.

4.Brady AP, Neri E. Artificial intelligence in radiology. Eur Radiol. 2020.

5.Erickson BJ et al. Machine learning for medical imaging. Radiographics. 2017.

6.Hosny A et al. Artificial intelligence in radiology. Radiology. 2018.

7.Kahn CE. Ethics of AI in radiology. AJR. 2019.

8.Ranschaert ER et al. Artificial intelligence in medical imaging. Springer. 2019.

9.Langlotz CP. Will artificial intelligence replace radiologists? Radiology. 2019.

10.Giger ML. Machine learning in medical imaging. J Am Coll Radiol. 2018.

11.Murtazayeva, Z. F. (2024). THE ART OF CLINICAL CASE ANALYSIS IN PEDIATRICS: A GUIDE FOR MEDICAL PROFESSIONALS. European Journal of Modern Medicine and Practice, 4(11), 443-447.

12.Murtazayeva, Z. (2025). ALLERGY IN PRETERM INFANTS: PATHOPHYSIOLOGY, CLINICAL MANIFESTATIONS, AND MANAGEMENT STRATEGIES. Modern Science and Research, 4(2), 742-748.

13.Murtazayeva, Z. (2025). CLINICAL CHARACTERISTICS AND COURSE OF OBSTRUCTIVE BRONCHITIS IN CHILDREN. Modern Science and Research, 4(5).

14.Murtazayeva, Z. (2025). PEDIATRIC PNEUMONIA: CLINICAL FEATURES, DIAGNOSIS, MANAGEMENT, AND GLOBAL HEALTH IMPACT. Modern Science and Research, 4(4), 1092-1101.

15.Murtazayeva, Z. F. (2024). Nourishing Newborns: Feeding Strategies to Minimize Allergy Risk in Preterm Infants. American Journal of Bioscience and Clinical Integrity, 1(10), 64-71.

16.Мухамедова, Ш. Т., & Муртазаева, З. Ф. (2024). Аллергические Заболевания У Недоношенных Новорожденных И Их Связь С Типом Питания. Research Journal of Trauma and Disability Studies, 3(6), 43-47.