The role of Artificial Intelligence in Medical Image Interpretation: advances and challenges in radiology
DOI:
https://doi.org/10.59169/pentaciencias.v7i4.1578Keywords:
Artificial Intelligence; radiology; medical imaging; diagnostic imaging; technological innovation; ethical challengesAbstract
This article delves into the recent advancements in the application of artificial intelligence or AI for medical imaging interpretation within the field of radiology. It highlights significant achievements, such as improved diagnostic accuracy and workflow optimization, as well as the ethical, regulatory, and technical challenges that continue to hinder full AI integration into clinical practice. Furthermore, specific cases where AI has proven useful are examined, and the potential future implications of these developments are discussed, including emerging research areas and the impact on radiologist training.
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Al-Naser, Y. A. (2023). The impact of artificial intelligence on radiography as a profession: A narrative review. Journal of Medical Imaging and Radiation Sciences, 54(1), 162-166. https://www.sciencedirect.com/science/article/pii/S1939865422005744
Amin, J., Sharif, M., Haldorai, A., Yasmin, M., & Nayak, R. S. (2022). Brain tumor detection and classification using machine learning: a comprehensive survey. Complex & intelligent systems, 8(4), 3161-3183. https://link.springer.com/article/10.1007/s40747-021-00563-y
Archibong, E., Raji, R., Ibe, D., Udoh, A., & Chisunka, M. (2025). Ethical and Legal Dimensions of AI Diagnosis in Medicine. Ibom Medical Journal, 18(3), 440-450. http://www.ibommedicaljournal.org/index.php/imjhome/article/view/702
Cellina, M., Cè, M., Irmici, G., Ascenti, V., Caloro, E., Bianchi, L., Pellegrino, G., D’Amico, N., Papa, S., & Carrafiello, G. (2022). Artificial intelligence in emergency radiology: where are we going? Diagnostics, 12(12), 3223. https://www.mdpi.com/2075-4418/12/12/3223
Dembrower, K., Crippa, A., Colón, E., Eklund, M., & Strand, F. (2023). Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study. The Lancet Digital Health, 5(10), e703-e711. https://www.thelancet.com/journals/landig/article/PIIS2589-7500(23)00153-X/fulltext
Ferreira, A. L. C., de Carvalho Feitoza, L. P. G., Benitez, M. E., Aziri, B., Begic, E., de Souza, L. V. F., Bulhões, E., Monteiro, S. O., Defante, M. L., & Vieira, R. A. M. S. (2025). Diagnostic accuracy of artificial-intelligence-based electrocardiogram algorithm to estimate heart failure with reduced ejection fraction: A systematic review and meta-analysis. Current problems in cardiology, 103004. https://www.sciencedirect.com/science/article/pii/S0146280625000271
González, E. R., Cornelio, O. M., García, A. L. G., & Fonseca, B. B. (2025). Modelo computacional para el apoyo al diagnóstico de pacientes con la enfermedad de Parkinson. Revista Cubana de Ciencias Informáticas, 19(2). https://rcci.uci.cu/index.php/RCCI/article/view/13090
Langlotz, C. P. (2019). Will artificial intelligence replace radiologists? , 1(3), e190058. https://pubs.rsna.org/doi/abs/10.1148/ryai.2019190058
Lastrucci, A., Iosca, N., Wandael, Y., Barra, A., Lepri, G., Forini, N., Ricci, R., Miele, V., & Giansanti, D. (2025). Ai and interventional radiology: A narrative review of reviews on opportunities, challenges, and future directions. Diagnostics, 15(7), 893. https://www.mdpi.com/2075-4418/15/7/893
Li, X., Zhao, L., Zhang, L., Wu, Z., Liu, Z., Jiang, H., Cao, C., Xu, S., Li, Y., & Dai, H. (2024). Artificial general intelligence for medical imaging analysis. IEEE Reviews in Biomedical Engineering. https://ieeexplore.ieee.org/abstract/document/10746601/
Liu, M., Wu, J., Wang, N., Zhang, X., Bai, Y., Guo, J., Zhang, L., Liu, S., & Tao, K. (2023). The value of artificial intelligence in the diagnosis of lung cancer: A systematic review and meta-analysis. PloS one, 18(3), e0273445. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0273445
Martín-Noguerol, T., Paulano-Godino, F., López-Ortega, R., Górriz, J., Riascos, R., & Luna, A. (2021). Artificial intelligence in radiology: relevance of collaborative work between radiologists and engineers for building a multidisciplinary team. Clinical Radiology, 76(5), 317-324. https://www.sciencedirect.com/science/article/pii/S0009926020306024
Ranschaert, E., Topff, L., & Pianykh, O. (2021). Optimization of radiology workflow with artificial intelligence. Radiologic Clinics, 59(6), 955-966. https://www.radiologic.theclinics.com/article/S0033-8389(21)00080-4/abstract
Romero Ibarra, J. L. (2025). Análisis integral de algoritmos de clasificación en aprendizaje automático: perspectivas, comparaciones y aplicaciones. Serie Científica de la Universidad de las Ciencias Informáticas, 18(1), 283-304. http://scielo.sld.cu/scielo.php?pid=S2306-24952025000100283&script=sci_abstract
Saw, S. N., Yan, Y. Y., & Ng, K. H. (2025). Current status and future directions of explainable artificial intelligence in medical imaging. European Journal of Radiology, 183, 111884. https://www.sciencedirect.com/science/article/pii/S0720048X24006004
Thanoon, M. A., Zulkifley, M. A., Mohd Zainuri, M. A. A., & Abdani, S. R. (2023). A review of deep learning techniques for lung cancer screening and diagnosis based on CT images. Diagnostics, 13(16), 2617. https://www.mdpi.com/2075-4418/13/16/2617
Yadav, N., Pandey, S., Gupta, A., Dudani, P., Gupta, S., & Rangarajan, K. (2023). Data privacy in healthcare: in the era of artificial intelligence. Indian Dermatology Online Journal, 14(6), 788-792. https://journals.lww.com/idoj/fulltext/2023/14060/data_privacy_in_healthcare__in_the_era_of.5.asp
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