Effectiveness of Artificial Intelligence in Medical Imaging Diagnosis: A Systematic Review
DOI:
https://doi.org/10.71068/t20bpr35Keywords:
artificial intelligence, medical diagnosis, diagnostic imaging, deep learning, diagnostic accuracyAbstract
This article aimed to analyze the effectiveness of artificial intelligence (AI) in medical imaging diagnosis through a systematic review of scientific literature published between 2021 and 2025. The integration of AI algorithms into clinical practice transformed diagnostic approaches across various medical specialties by improving the accuracy, efficiency, and speed of image analysis. The review was conducted following PRISMA guidelines and using databases such as PubMed, Scopus, EMBASE, Web of Science, and Science Direct. Ten relevant studies were selected, covering fields including ophthalmology, oncology, pulmonology, rare diseases, pediatric dentistry, and plastic surgery. The findings indicated that systems based on deep learning and machine learning achieved high levels of sensitivity, specificity, and positive predictive value, in some cases surpassing the performance of human specialists. However, significant limitations were identified, such as methodological heterogeneity, lack of standardized metrics, and the absence of robust ethical and regulatory frameworks. It was concluded that AI served as a valuable complementary tool in medical practice, with the potential to optimize imaging diagnosis and improve patient care. Nevertheless, its effective implementation depended on simultaneously addressing technical, ethical, educational, and structural challenges. This review provided a critical knowledge base to guide future research, clinical decisions, and public policy toward the responsible, safe, and humanized use of artificial intelligence in healthcare.
References
Aggarwal, R., Sounderajah, V., Martin, G., Ting, D. S., Karthikesalingam, A., King, D., ... & Darzi, A. (2021). Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ digital medicine, 4(1), 65. https://www.nature.com/articles/s41746-021-00438-z
Elias, M. A., Faversani, L. A., Moreira, J. A. V., Masiero, A. V., & Cunha, N. V. D. (2024). Inteligencia artificial en salud y sus implicaciones bioéticas: una revisión sistemática. Revista Bioética, 31, e3542PT. https://www.scielo.br/j/bioet/a/d9bswmTrshnRQSN6ff9WLkD/?format=html&lang=es
Eyzaguirre, J. C. L., Torrez, W. B., Sousa, K. S., & Retamal-Valdes, B. (2023). Optimizando la atención dental infantil: El papel de la Inteligencia Artificial en odontopediatría. RECIMA21-Revista Científica Multidisciplinar-ISSN 2675-6218, 4(9), e493849-e493849. https://recima21.com.br/index.php/recima21/article/view/3849
Khalifa, M., & Albadawy, M. (2024). AI in diagnostic imaging: Revolutionising accuracy and efficiency. Computer Methods and Programs in Biomedicine Update, 100146. https://www.sciencedirect.com/science/article/pii/S2666990024000132
Londoño, C. G., Restrepo, D. F. A., Castañeda, M. C. D., Ortega, C. B., Carmona, R. J., Rodríguez, C. M., & Gómez, S. R. (2024). Inteligencia artificial en el diagnóstico y en el tratamiento de lesiones musculares. riccafd: Revista Iberoamericana de Ciencias de la Actividad Física y el Deporte, 13(3), 252-273. https://dialnet.unirioja.es/servlet/articulo?codigo=9962062
Lugo, C. A. Z., Rivarola, A. M. I., & Ferreira, A. P. A. (2024). Impacto de la IA en la Predicción y Detección Oportuna de Enfermedades: Una Revisión Sistemática. Latin American Journal of Applied Engineering, 7(1), 25-35. https://lajae.uabc.mx/index.php/home/article/view/35
Ruge, L. A., Lesmes, D. V., Rincón, E. H., & Pérez, L. A. (2025). Inteligencia artificial para el abordaje integral de las enfermedades huérfanas/raras: revisión sistemática exploratoria. Medicina de Familia. SEMERGEN, 51(5), 102434. https://www.sciencedirect.com/science/article/pii/S1138359324002442
Silva, H. E. C. D., Santos, G. N. M., Leite, A. F., Mesquita, C. R. M., Figueiredo, P. T. D. S., Stefani, C. M., & de Melo, N. S. (2023). The use of artificial intelligence tools in cancer detection compared to the traditional diagnostic imaging methods: An overview of the systematic reviews. Plos one, 18(10), e0292063. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0292063
Telich-Tarriba, J. E., Meraz-Soto, J. M., & Prieto-Vargas, V. (2023). Aplicaciones de la inteligencia artificial en la cirugía plástica y reconstructiva: una revisión exhaustiva de la literatura. Cirugía Plástica, 33(4), 152-160. https://www.medigraphic.com/cgi-bin/new/resumen.cgi?IDARTICULO=113881
Villa Feijoó, A. L., & Zapata Velasco, E. K. (2025). Aplicaciones de la inteligencia artificial en el diagnóstico médico basado en datos. Innova Science Journal, 3(1), 16-29. https://doi.org/10.63618/omd/isj/v3/n1/2
Zhan, Y., Wang, Y., Zhang, W., Ying, B., & Wang, C. (2022). Diagnostic accuracy of the artificial intelligence methods in medical imaging for pulmonary tuberculosis: a systematic review and meta-analysis. Journal of Clinical Medicine, 12(1), 303. https://www.mdpi.com/2077-0383/12/1/303
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Copyright (c) 2025 Narda Guerrero Meza, Edgar Saúl Reyes Mauricio, Joel David Bastidas Jimbo (Autor/a)
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