Correo Electrónico
DNINFOA - SIA
Bibliotecas
Convocatorias
Identidad U.N.
Publicado
Barriers to AI adoption and competitive strategy in medical imaging: a mixed-methods study
Barreras para la adopción de la IA y estrategia competitiva en el campo de las imágenes médicas: un estudio con métodos mixtos
DOI:
https://doi.org/10.15446/dyna.v93n241.122770Palabras clave:
adoption barriers, Artificial Intelligence, diagnostic accuracy, healthcare disparities, medical imaging (en)barreras para la adopción, Inteligencia Artificial, precisión diagnóstica, disparidades en la atención sanitaria, imágenes médicas (es)
Descargas
The study evaluates the accuracy of artificial intelligence (AI) in medical image diagnosis, analyzing obstacles such as rivalry in healthcare institutions. According to a quantitative analysis, AI outperformed radiologists in terms of sensitivity. Significant barriers to its adoption were identified through qualitative interviews, workflow disruptions, and significant disparities in adoption among hospitals. The results clearly demonstrate a dilemma between effectiveness and adoption, caused by a lack of institutional resources and a lack of trust in the technology. In order to bridge the gap between clinically fair integration and technological capabilities, the study proposes a complementary intelligence paradigm that supports interpretable AI systems, flexible regulatory standards, and equity-focused implementation strategies.
El estudio evalúa la precisión de la inteligencia artificial (IA) en el diagnóstico de imágenes médicas, analizando los obstáculos, como la y la rivalidad en instituciones sanitarias. Según un análisis cuantitativo, la IA superó a los radiólogos en términos de sensibilidad, se identificaron importantes obstáculos para su adopción a través de entrevistas cualitativas, las interrupciones en el flujo de trabajo y las importantes disparidades en la adopción entre los hospitales. Los resultados demuestran claramente un dilema entre la eficacia y la adopción, provocado por la falta de recursos institucionales y la falta de confianza en la tecnología. Con el fin de salvar la brecha entre la integración clínicamente justa y las capacidades tecnológicas, el estudio propone un paradigma de inteligencia complementaria que apoya los sistemas de IA interpretables, las normas reguladoras flexibles y las estrategias de implementación centradas en la equidad.
Referencias
[1] Esteva, A., et al., Deep learning-enabled medical computer vision. NPJ digital medicine, 4(5), art 2, 2021. DOI: https://doi.org/10.1038/s41746-020-00376-2.
[2] Wang, J., Wang, S., and Zhang, Y., Deep learning on medical image analysis. CAAI Transactions on Intelligence Technology, 10(1), pp. 1–35, 2025. DOI: https://doi.org/10.1049/cit2.12356.
[3] Khosravi, M., Zare, Z., Mojtabaeian, S.M., and Izadi, R., Artificial Intelligence and decision-making in healthcare: a thematic analysis of a systematic review of reviews. Health Services Research and Managerial Epidemiology, 11, art. 234863, 2024. DOI: https://doi.org/10.1177/23333928241234863.
[4] Silva-Atencio, G., and Umaña-Ramírez, M., The evolution and trends of hyperconvergence in the telecommunications sector: a competitive intelligence. Review- DYNA (Colombia), 90(227), pp. 126–132, 2023. DOI: https://doi.org/10.15446/dyna.v90n227.107360.
[5] Liang, N.L., Chung, T.K., and Vorp, D.A., The regulatory environment for artificial intelligence–enabled devices in the United States. Seminars in Vascular Surgery, 36(3), pp. 435–439, 2023. DOI: https://doi.org/10.1053/j.semvascsurg.2023.05.005.
[6] Vidal-Alaball, J., Panadés-Zafra, R., Escalé-Besa, A., and Martinez-Millana, A., The artificial intelligence revolution in primary care: challenges, dilemmas and opportunities. Atención Primaria, 56(2), art. 102820, 2024. DOI: https://doi.org/10.1016/j.aprim.2023.102820.
[7] Holzinger, A., and Müller, H., Toward human–ai interfaces to support explainability and causability in medical AI. Computer, 54(10), pp. 78–86, 2021. DOI: https://doi.org/10.1109/MC.2021.3092610.
[8] Scherer, R., Is the technology acceptance model just old wine in new wineskins? Exploring issues for further model development. Journal of University Teaching and Learning Practice, 22(8), art. 96, 2025. DOI: https://doi.org/10.53761/var55x96.
[9] Di-Maggio, P.J., and Powell, W.W., The Iron Cage Revisited: Institutional Isomorphism and Collective Rationality in Organizational Fields. American Sociological Review, 48(2), pp. 147–160, 1983. DOI: https://doi.org/10.2307/2095101.
[10] Al-Shammari, M.M., Business process reengineering for designing a knowledge-enabled customer-centric competitiveness strategy. Business Process Management Journal, 29(6), pp. 1706–1733, 2023. DOI: https://doi.org/10.1108/BPMJ-02-2023-0074.
[11] Cavallo, A., Sanasi, S., Ghezzi, A., and Rangone, A., Competitive intelligence and strategy formulation: connecting the dots. Competitiveness Review: An International Business Journal, 31(2), pp. 250–275, 2021. DOI: https://doi.org/10.1108/CR-01-2020-0009.
[12] Sharma, R., Kumar, N., and Sharma, V., Computer Vision for disease detection—an overview of how computer vision techniques can be used to detect diseases in medical images, Such as x‐rays and MRIs. AI in Disease Detection: Advancements and Applications, pp. 77–98, art. 4, 2025. DOI: https://doi.org/10.1002/9781394278695.ch4.
[13] Li, J., Wang, S., Rudinac, S., and Osseyran, A., High-performance computing in healthcare: An automatic literature analysis perspective. Journal of Big Data, 11(1), art. 61, 2024. DOI: https://doi.org/10.1186/s40537-024-00929-2.
[14] Priya, A. and Shyamala-Bharathi, P., SE-ResNeXt-50-CNN: a deep learning model for lung cancer classification. Applied Soft Computing, 171, art. 112696, 2025. DOI: https://doi.org/10.1016/j.asoc.2025.112696.
[15] Mannepalli, D., Kuan-Tak, T., Bala-Krishnan, S., and Sreenivas, V., GSC-DVIT: a vision transformer based deep learning model for lung cancer classification in CT images. Biomedical Signal Processing and Control, 103, art. 107371, 2025. DOI: https://doi.org/10.1016/j.bspc.2024.107371.
[16] Garbin, C., Rajpurkar, P., Irvin, J., Lungren, M.P., and Marques, O., Structured dataset documentation: a datasheet for CheXpert. arXiv preprint art. 03020, 2021. DOI: https://doi.org/10.48550/arXiv.2105.03020.
[17] Khan, S.U.R., Asif, S., Zhao, M., Zou, W., Li, Y., and Li, X., Optimized deep learning model for comprehensive medical image analysis across multiple modalities. Neurocomputing, 619, art. 129182, 2025. DOI: https://doi.org/10.1016/j.neucom.2024.129182.
[18] Brant, A., et al., Performance of a deep learning diabetic retinopathy algorithm in India. JAMA Network Open, 8(3), pp. art. 0984, 2025. DOI: https://doi.org/10.1001/jamanetworkopen.2025.0984.
[19] Müller, H., Holzinger, A., Plass, M., Brcic, L., Stumptner, C., and Zatloukal, K., Explainability and causability for artificial intelligence- supported medical image analysis in the context of the European In Vitro Diagnostic Regulation. New Biotechnology, 70, pp. 67–72, 2022. DOI: https://doi.org/10.1016/j.nbt.2022.05.002.
[20] Boge, F., and Mosig, A., Causality and scientific explanation of artificial intelligence systems in biomedicine. Pflügers Archiv-European Journal of Physiology, 477(4), pp. 543–554, 2025. DOI: https://doi.org/10.1007/s00424-024-03033-9.
[21] Agazu, B.G., and Kero, C.A., Innovation strategy and firm competitiveness: a systematic literature review. Journal of Innovation and Entrepreneurship, 13(24), art. 9, 2024. DOI: https://doi.org/10.1186/s13731-024-00381-9.
[22] Balasubramanian, S., Shukla, V., Islam, N., Upadhyay, A., and Duong, L., Applying artificial intelligence in healthcare: lessons from the COVID-19 pandemic. International Journal of Production Research, 63(2), pp. 594–627, 2025. DOI: https://doi.org/10.1080/00207543.2023.2263102.
[23] Islam, M.M., Exploring the impact of artificial intelligence in healthcare. Journal of Artificial Intelligence General science (JAIGS), 2(1), pp. 228–238, 2024. DOI: https://doi.org/10.60087/jaigs.v2i1.109.
[24] Murdoch, B., Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Medical Ethics, 22(122), art. 3, 2021. DOI: https://doi.org/10.1186/s12910-021-00687-3.
[25] Dell, N.A., Big Data and privacy. Encyclopedia of Social Work, art. 1666, 2025, DOI: https://doi.org/10.1093/acrefore/9780199975839.013.1666.
[26] Yuan, Z.S., and Singh, M.M., Privacy risks in health big data: a systematic literature review. arXiv preprint, art.03811, 2025. DOI: https://doi.org/10.48550/arXiv.2502.03811.
[27] Clark, S.E., et al., An umbrella review of the facilitators and barriers to implementing Artificial Intelligence (AI) solutions within hospital settings: through the lens of the NASSS framework (spread, scale-up and sustainability). medRxiv, art. 25329916, 2025. DOI:
https://doi.org/10.1101/2025.06.19.25329916.
[28] Xu, Y., Khan, T.M., Song, Y., and Meijering, E., Edge deep learning in computer vision and medical diagnostics: a comprehensive survey. Artificial Intelligence Review, 58(93), art. 5, 2025. DOI: https://doi.org/10.1007/s10462-024-11033-5.
[29] Moannaei, M., et al., Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis. BioMedical Engineering OnLine, 24(34), art. 1, 2025. DOI: https://doi.org/10.1186/s12938-025-01336-1.
[30] Suganyadevi, S., Seethalakshmi, V., and Balasamy, K., A review on deep learning in medical image analysis. International Journal of Multimedia Information Retrieval, 11(1), pp. 19–38, 2022. DOI: https://doi.org/10.1007/s13735-021-00218-1.
[31] Rezaei, K., et al., Bridging gaps with computer vision: AI in (bio)medical imaging and astronomy. Astronomy and Computing, 51, art. 100921, 2025. DOI: https://doi.org/10.1016/j.ascom.2024.100921.
[32] Ibrahim, H., et al., Reporting guidelines for clinical trials of artificial intelligence interventions: the SPIRIT-AI and CONSORT-AI guidelines. Trials, 22(11), art. 6, 2021. DOI: https://doi.org/10.1186/s13063-020-04951-6.
[33] O’Connor, C., and Joffe, H., Intercoder reliability in qualitative research: debates and practical guidelines. International Journal of Qualitative Methods, 19, art. 99220, 2020. DOI: https://doi.org/10.1177/1609406919899220.
[34] Arvisais-Anhalt S. et al., The 21st century cures act and multiuser electronic health record access: potential pitfalls of information release. Journal of medical Internet research, 24(2), art. 34085, 2022. DOI: https://doi.org/10.2196/34085.
[35] Choi, Y., Bang, J., Kim, S.Y., Seo, M., and Jang, J., Deep learning–based multimodal segmentation of oropharyngeal squamous cell carcinoma on CT and MRI using self-configuring nnU-Net. European Radiology, 34(8), pp. 5389–5400, 2024. DOI: https://doi.org/10.1007/s00330-024-10585-y.
[36] Li, X., Lv, C., Wang, W., Li, G., Yang, L., and Yang, J., Generalized focal loss: towards efficient representation learning for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3), pp. 3139–3153, 2023. DOI: https://doi.org/10.1109/TPAMI.2022.3180392.
[37] Nahm, F.S., Receiver operating characteristic curve: overview and practical use for clinicians. Korean journal of anesthesiology, 75(1), pp. 25–36, 2022. DOI: https://doi.org/10.4097/kja.21209.
[38] Rau, G., and Shih, Y.S., Evaluation of Cohen's kappa and other measures of inter-rater agreement for genre analysis and other nominal data. Journal of English for Academic Purposes, 53, art. 101026, 2021. DOI: https://doi.org/10.1016/j.jeap.2021.101026.
[39] Ahmad, M., and Wilkins, S., Purposive sampling in qualitative research: a framework for the entire journey. Quality and Quantity, 59(2), pp. 1461–1479, 2025. DOI: https://doi.org/10.1007/s11135-024-02022-5.
[40] Mortelmans, D., Thematic Coding. Doing Qualitative Data Analysis with NVivo, pp. 57–87, art. 8, 2025. DOI: https://doi.org/10.1007/978-3-031-66014-6_8.
[41] Kawar, L.N., Dunbar, G.B., Aquino-Maneja E.M., Flores, S.L., Squier, V.R., and Failla, K.R., Quantitative, qualitative, mixed methods, and triangulation research simplified. The Journal of Continuing Education in Nursing, 55(7), pp. 338–344, 2024. DOI: https://doi.org/10.3928/00220124-20240328-03.
[42] Hedges, L.V., Tipton, E., Zejnullahi, R., and Diaz, K.G., Effect sizes in ANCOVA and difference‐in‐differences designs. British Journal of Mathematical and Statistical Psychology, 76(2), pp. 259–282, 2023. DOI: https://doi.org/10.1111/bmsp.12296.
[43] Min, C., Liao, G., Wen, G., Li, Y., and Guo, X., Ensemble interpretation: a unified method for interpretable machine learning. arXiv preprint, art. 06255, 2023. DOI: https://doi.org/10.48550/arXiv.2312.06255.
[44] Radestad, M., Kanfjäll, T., and Lindström, V., Real-Time Triage, Position, and Documentation (TriPoD) during medical response to major incidents: protocol for an action research study. JMIR Res Protoc, 13, art. 57819, 2024. DOI: https://doi.org/10.2196/57819.
[45] Dossett, L.A., Kaji, A.H., and Cochran, A., SRQR and COREQ reporting guidelines for qualitative studies. JAMA Surgery, 156(9), pp. 875–876, 2021. DOI: https://doi.org/10.1001/jamasurg.2021.0525.
[46] Alexander, R. et al., Mandating limits on workload, duty, and speed in radiology," Radiology, 304(2), pp. 274–282, 2022. DOI: https://doi.org/10.1148/radiol.212631.
[47] Arshad, K., Ardalan, S., Schreiweis, B., and Bergh, B., Integrating an AI platform into clinical IT: BPMN processes for clinical AI model development. BMC Medical Informatics and Decision Making, 25(243), art. 4, 2025. DOI: https://doi.org/10.1186/s12911-025-03087-4.
[48] Sweller, J., Roussel, S., and Tricot, A., Cognitive load theory and instructional design for language learning. The Cambridge handbook of working memory and language, 1, pp. 859–880, 2022. DOI: https://doi.org/10.1017/9781108955638.045.
[49] Egala, R., and Sairam, M.V.S., A review on medical image analysis using deep learning. Engineering Proceedings, 66(7), art. 7, 2024. DOI: https://doi.org/10.3390/engproc2024066007.
[50] Sharma, N., and Kaushik, P., Integration of AI in Healthcare Systems—A Discussion of the Challenges and Opportunities of Integrating AI in Healthcare Systems for Disease Detection and Diagnosis. AI in Disease Detection: Advancements and Applications, pp. 239–263, art. 11, 2025. DOI: https://doi.org/10.1002/9781394278695.ch11.
[51] Bitomsky, L., Nißen, M., and Kowatsch, T., Equity by Design Principles for Digital Health Interventions. art. 1, 2025, DOI: https://doi.org/10.21203/rs.3.rs-6705871/v1.
Cómo citar
IEEE
ACM
ACS
APA
ABNT
Chicago
Harvard
MLA
Turabian
Vancouver
Descargar cita
Licencia
Derechos de autor 2026 DYNA
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
El autor o autores de un artículo aceptado para publicación en cualquiera de las revistas editadas por la facultad de Minas cederán la totalidad de los derechos patrimoniales a la Universidad Nacional de Colombia de manera gratuita, dentro de los cuáles se incluyen: el derecho a editar, publicar, reproducir y distribuir tanto en medios impresos como digitales, además de incluir en artículo en índices internacionales y/o bases de datos, de igual manera, se faculta a la editorial para utilizar las imágenes, tablas y/o cualquier material gráfico presentado en el artículo para el diseño de carátulas o posters de la misma revista.
