Tendencias y patrones de los modelos de inteligencia artificial en salud
Palabras clave:
Inteligencia artificial, aprendizaje automático, salud digital, revisión de literatura, modalidad de datos
Resumen
En los últimos años, la inteligencia artificial y el aprendizaje automático se expandieron rápidamente en el sector de la salud, no obstante, la literatura reportó los algoritmos de forma inconsistente. El objetivo de este estudio fue identificar y caracterizar las familias de algoritmos con mayor frecuencia reportadas en aplicaciones clínicas, estratificadas por modalidad de datos y tarea clínica. Para este fin, se realizó una revisión de alcance y se recuperó literatura publicada entre 2020 y 2025 en bases de datos médicas. Se incluyeron artículos de revistas que reportaron modelos de inteligencia artificial entrenados o evaluados y que presentaron métricas cuantitativas. Se extrajeron datos sobre arquitecturas de modelos, dominios clínicos, tareas, conjuntos de datos, validación y rendimiento. Se identificaron 20 estudios primarios de implementación que abarcaron tres modalidades principales: imágenes médicas (n = 9), datos tabulares de historia clínica electrónica (n = 7) y señales fisiológicas (n = 4). Las redes neuronales convolucionales dominaron en imágenes, mientras que los modelos híbridos prevalecieron en señales. Los modelos de aprendizaje automático clásico fueron comunes en predicción tabular. En conjunto, los modelos de inteligencia artificial en salud mostraron patrones claros de especialización por modalidad de datos, con arquitecturas profundas que dominaron datos no estructurados y métodos clásicos que persistieron en datos tabulares.
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Awasthi, R., Ramachandran, S. P., Mishra, S., Mahapatra, D., Arshad, H., Atreja, A., Bhattacharyya, A., Bhattad, A., Singh, N., Cywinski, J. B., Khanna, A. K., Maheshwari, K., Dave, C., Khare, A., Papay, F. A., & Mathur, P. (2025). Artificial Intelligence in Healthcare: 2024 Year in Review (p. 2025.02.26.25322978). medRxiv. https://doi.org/10.1101/2025.02.26.25322978
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Dong, F., Li, S., & Li, W. (2025). TCKAN: A novel integrated network model for predicting mortality risk in sepsis patients. Medical & Biological Engineering & Computing, 63(4), 1013–1025. https://doi.org/10.1007/s11517-024-03245-2
Duan, Y., Huo, J., Chen, M., Hou, F., Yan, G., Li, S., & Wang, H. (2023). Early prediction of sepsis using double fusion of deep features and handcrafted features. Applied Intelligence, 53(14), 17903–17919. https://doi.org/10.1007/s10489-022-04425-z
Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022). Medical deep learning—A systematic meta-review. Computer Methods and Programs in Biomedicine, 221, 106874. https://doi.org/10.1016/j.cmpb.2022.106874
Feng, C., Zhou, X., Wang, H., He, Y., Li, Z., & Tu, C. (2022). Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.949366
Gao, Q., Chen, L., & Huang, Z. (2026). Opportunities and challenges of artificial intelligence in public health: A systematic review on technological efficacy, ethical dilemmas, and governance pathways. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1748797
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Han, S. S., Moon, I. J., Lim, W., Suh, I. S., Lee, S. Y., Na, J.-I., Kim, S. H., & Chang, S. E. (2020). Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. JAMA Dermatology, 156(1), 29–37. https://doi.org/10.1001/jamadermatol.2019.3807
Hori, S., Shono, T., Gyohten, K., Ohki, H., Takami, T., & Sato, N. (2020, diciembre 30). Arrhythmia detection based on patient-specific normal ECGs using deep learning. 2020 Computing in Cardiology Conference. https://doi.org/10.22489/CinC.2020.137
Huqh, M. Z. U., Abdullah, J. Y., Wong, L. S., Jamayet, N. B., Alam, M. K., Rashid, Q. F., Husein, A., Ahmad, W. M. A. W., Eusufzai, S. Z., Prasadh, S., Subramaniyan, V., Fuloria, N. K., Fuloria, S., Sekar, M., & Selvaraj, S. (2022). Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review. International Journal of Environmental Research and Public Health, 19(17). https://doi.org/10.3390/ijerph191710860
Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020). RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.605132
Kitsios, F., Kamariotou, M., Syngelakis, A. I., & Talias, M. A. (2023). Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review. Applied Sciences, 13(13). https://doi.org/10.3390/app13137479
Kumari, J., Kumar, E., & Kumar, D. (2023). A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics. Archives of Computational Methods in Engineering, 30(6), 3673–3701. https://doi.org/10.1007/s11831-023-09915-y
Liu, Y., Liang, R., & Zhang, C. (2024). The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: Evidence from Wuhan-area hospitals. Frontiers in Digital Health, 6. https://doi.org/10.3389/fdgth.2024.1506071
Loftus, T. J., Shickel, B., Ruppert, M. M., Balch, J. A., Ozrazgat-Baslanti, T., Tighe, P. J., Efron, P. A., Hogan, W. R., Rashidi, P., Upchurch, G. R., & Bihorac, A. (2022). Uncertainty-aware deep learning in healthcare: A scoping review. PLOS Digital Health, 1(8), e0000085. https://doi.org/10.1371/journal.pdig.0000085
Malarvannan, S., V, P., Venkatraman, S., A, A., B, P., & A, K. (2025). A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks (arXiv:2411.10843). arXiv. https://doi.org/10.48550/arXiv.2411.10843
Nam, S., Kim, D., Jung, W., & Zhu, Y. (2022). Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis. Journal of Medical Internet Research, 24(4), e28114. https://doi.org/10.2196/28114
Ni, Q., Sun, Z. Y., Qi, L., Chen, W., Yang, Y., Wang, L., Zhang, X., Yang, L., Fang, Y., Xing, Z., Zhou, Z., Yu, Y., Lu, G. M., & Zhang, L. J. (2020). A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. European Radiology, 30(12), 6517–6527. https://doi.org/10.1007/s00330-020-07044-9
Ogundokun, R. O., Misra, S., Akinrotimi, A. O., & Ogul, H. (2023). MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. Sensors, 23(2). https://doi.org/10.3390/s23020656
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
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Pei, X., Zuo, K., Li, Y., & Pang, Z. (2023). A Review of the Application of Multi-modal Deep Learning in Medicine: Bibliometrics and Future Directions. International Journal of Computational Intelligence Systems, 16(1), 44. https://doi.org/10.1007/s44196-023-00225-6
Qureshi, M. A., Qureshi, K. N., Jeon, G., & Piccialli, F. (2022). Deep learning-based ambient assisted living for self-management of cardiovascular conditions. Neural Computing and Applications, 34(13), 10449–10467. https://doi.org/10.1007/s00521-020-05678-w
Sanker, V., Venkatesan, A., Salma, A., Sp, A., Li, Z., Heesen, P., Park, C., Park, D. J., & Desai, A. (2026). Artificial intelligence models in the surgical planning of low-grade gliomas: A systematic review. Frontiers in Oncology, 15(15), 1672289. https://doi.org/10.3389/fonc.2025.1672289
Santos, C., Aguiar, M., Welfer, D., & Belloni, B. (2022). A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model. Sensors, 22(17). https://doi.org/10.3390/s22176441
Shi, S., Zhang, L., Zhang, S., Shi, J., Hong, D., Wu, S., Pan, X., & Lin, W. (2025). Developing a rapid screening tool for high-risk ICU patients of sepsis: Integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization. Journal of Translational Medicine, 23(1), 97. https://doi.org/10.1186/s12967-025-06102-4
Su, L., Xu, Z., Chang, F., Ma, Y., Liu, S., Jiang, H., Wang, H., Li, D., Chen, H., Zhou, X., Hong, N., Zhu, W., & Long, Y. (2021). Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models. Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.664966
Sun, H., Depraetere, K., Meesseman, L., Silva, P. C., Szymanowsky, R., Fliegenschmidt, J., Hulde, N., Dossow, V. von, Vanbiervliet, M., Baerdemaeker, J. D., Roccaro-Waldmeyer, D. M., Stieg, J., Hidalgo, M. D., & Dahlweid, F.-M. (2022). Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance. Journal of Medical Internet Research, 24(6), e34295. https://doi.org/10.2196/34295
Tang, R., Zhang, S., Ding, C., Zhu, M., & Gao, Y. (2022). Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis. Journal of Medical Internet Research, 24(11), e42185. https://doi.org/10.2196/42185
Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708. https://doi.org/10.1002/(SICI)1097-0258(19991030)18:20%253C2693::AID-SIM235%253E3.0.CO;2-V
Thurzo, A., Urbanová, W., Novák, B., Czako, L., Siebert, T., Stano, P., Mareková, S., Fountoulaki, G., Kosnáčová, H., & Varga, I. (2022). Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare, 10(7). https://doi.org/10.3390/healthcare10071269
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Vaid, A., Sawant, A., Suarez-Farinas, M., Lee, J., Kaul, S., Kovatch, P., Freeman, R., Jiang, J., Jayaraman, P., Fayad, Z., Argulian, E., Lerakis, S., Charney, A. W., Wang, F., Levin, M., Glicksberg, B., Narula, J., Hofer, I., Singh, K., & Nadkarni, G. N. (2023). Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings. Annals of Internal Medicine, 176(10), 1358–1369. https://doi.org/10.7326/M23-0949
Vijayan, M., & S, V. (2023). A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet. Diagnostics, 13(4). https://doi.org/10.3390/diagnostics13040774
Xia, L., He, S., Huang, Y.-F., & Ma, H. (2024). Multiscale dilated convolutional neural network for Atrial Fibrillation detection. PLOS ONE, 19(6), e0301691. https://doi.org/10.1371/journal.pone.0301691
Xie, J., Stavrakis, S., & Yao, B. (2024). Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet. Frontiers in Physiology, 15. https://doi.org/10.3389/fphys.2024.1362185
Xu, B., Martín, D., Khishe, M., & Boostani, R. (2022). COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm. Medical & Biological Engineering & Computing, 60(10), 2931–2949. https://doi.org/10.1007/s11517-022-02637-6
Zhang, P., Lin, F., Ma, F., Chen, Y., Fang, S., Zheng, H., Xiang, Z., Yang, X., & Li, Q. (2023). Automatic screening of patients with atrial fibrillation from 24-h Holter recording using deep learning. European Heart Journal - Digital Health, 4(3), 216–224. https://doi.org/10.1093/ehjdh/ztad018
Changalidis, A., Barbitoff, Y., Nasykhova, Y., & Glotov, A. (2026). A systematic review on the generative AI applications in human medical genetics. Frontiers in Genetics, 16(1694070). https://doi.org/10.3389/fgene.2025.1694070
Dong, F., Li, S., & Li, W. (2025). TCKAN: A novel integrated network model for predicting mortality risk in sepsis patients. Medical & Biological Engineering & Computing, 63(4), 1013–1025. https://doi.org/10.1007/s11517-024-03245-2
Duan, Y., Huo, J., Chen, M., Hou, F., Yan, G., Li, S., & Wang, H. (2023). Early prediction of sepsis using double fusion of deep features and handcrafted features. Applied Intelligence, 53(14), 17903–17919. https://doi.org/10.1007/s10489-022-04425-z
Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022). Medical deep learning—A systematic meta-review. Computer Methods and Programs in Biomedicine, 221, 106874. https://doi.org/10.1016/j.cmpb.2022.106874
Feng, C., Zhou, X., Wang, H., He, Y., Li, Z., & Tu, C. (2022). Research hotspots and emerging trends of deep learning applications in orthopedics: A bibliometric and visualized study. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.949366
Gao, Q., Chen, L., & Huang, Z. (2026). Opportunities and challenges of artificial intelligence in public health: A systematic review on technological efficacy, ethical dilemmas, and governance pathways. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1748797
Gupta, A., Liu, T., & Crick, C. (2020). Utilizing time series data embedded in electronic health records to develop continuous mortality risk prediction models using hidden Markov models: A sepsis case study. Statistical Methods in Medical Research, 29(11), 3409–3423. https://doi.org/10.1177/0962280220929045
Han, S. S., Moon, I. J., Lim, W., Suh, I. S., Lee, S. Y., Na, J.-I., Kim, S. H., & Chang, S. E. (2020). Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network. JAMA Dermatology, 156(1), 29–37. https://doi.org/10.1001/jamadermatol.2019.3807
Hori, S., Shono, T., Gyohten, K., Ohki, H., Takami, T., & Sato, N. (2020, diciembre 30). Arrhythmia detection based on patient-specific normal ECGs using deep learning. 2020 Computing in Cardiology Conference. https://doi.org/10.22489/CinC.2020.137
Huqh, M. Z. U., Abdullah, J. Y., Wong, L. S., Jamayet, N. B., Alam, M. K., Rashid, Q. F., Husein, A., Ahmad, W. M. A. W., Eusufzai, S. Z., Prasadh, S., Subramaniyan, V., Fuloria, N. K., Fuloria, S., Sekar, M., & Selvaraj, S. (2022). Clinical Applications of Artificial Intelligence and Machine Learning in Children with Cleft Lip and Palate—A Systematic Review. International Journal of Environmental Research and Public Health, 19(17). https://doi.org/10.3390/ijerph191710860
Jin, Q., Meng, Z., Sun, C., Cui, H., & Su, R. (2020). RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans. Frontiers in Bioengineering and Biotechnology, 8. https://doi.org/10.3389/fbioe.2020.605132
Kitsios, F., Kamariotou, M., Syngelakis, A. I., & Talias, M. A. (2023). Recent Advances of Artificial Intelligence in Healthcare: A Systematic Literature Review. Applied Sciences, 13(13). https://doi.org/10.3390/app13137479
Kumari, J., Kumar, E., & Kumar, D. (2023). A Structured Analysis to study the Role of Machine Learning and Deep Learning in The Healthcare Sector with Big Data Analytics. Archives of Computational Methods in Engineering, 30(6), 3673–3701. https://doi.org/10.1007/s11831-023-09915-y
Liu, Y., Liang, R., & Zhang, C. (2024). The application of machine learning algorithms for predicting length of stay before and during the COVID-19 pandemic: Evidence from Wuhan-area hospitals. Frontiers in Digital Health, 6. https://doi.org/10.3389/fdgth.2024.1506071
Loftus, T. J., Shickel, B., Ruppert, M. M., Balch, J. A., Ozrazgat-Baslanti, T., Tighe, P. J., Efron, P. A., Hogan, W. R., Rashidi, P., Upchurch, G. R., & Bihorac, A. (2022). Uncertainty-aware deep learning in healthcare: A scoping review. PLOS Digital Health, 1(8), e0000085. https://doi.org/10.1371/journal.pdig.0000085
Malarvannan, S., V, P., Venkatraman, S., A, A., B, P., & A, K. (2025). A Novel Adaptive Hybrid Focal-Entropy Loss for Enhancing Diabetic Retinopathy Detection Using Convolutional Neural Networks (arXiv:2411.10843). arXiv. https://doi.org/10.48550/arXiv.2411.10843
Nam, S., Kim, D., Jung, W., & Zhu, Y. (2022). Understanding the Research Landscape of Deep Learning in Biomedical Science: Scientometric Analysis. Journal of Medical Internet Research, 24(4), e28114. https://doi.org/10.2196/28114
Ni, Q., Sun, Z. Y., Qi, L., Chen, W., Yang, Y., Wang, L., Zhang, X., Yang, L., Fang, Y., Xing, Z., Zhou, Z., Yu, Y., Lu, G. M., & Zhang, L. J. (2020). A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images. European Radiology, 30(12), 6517–6527. https://doi.org/10.1007/s00330-020-07044-9
Ogundokun, R. O., Misra, S., Akinrotimi, A. O., & Ogul, H. (2023). MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors. Sensors, 23(2). https://doi.org/10.3390/s23020656
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Patel, D., Kantamneni, R., John, J. D., Patel, T., Shukla, A., Salma, A., & Anand, N. (2026). Artificial intelligence in cardiology: An updated systematic review with ethical considerations and challenges in implementing artificial intelligence models. Annals of Medicine and Surgery, 88(2), 1789. https://doi.org/10.1097/MS9.0000000000004607
Pei, X., Zuo, K., Li, Y., & Pang, Z. (2023). A Review of the Application of Multi-modal Deep Learning in Medicine: Bibliometrics and Future Directions. International Journal of Computational Intelligence Systems, 16(1), 44. https://doi.org/10.1007/s44196-023-00225-6
Qureshi, M. A., Qureshi, K. N., Jeon, G., & Piccialli, F. (2022). Deep learning-based ambient assisted living for self-management of cardiovascular conditions. Neural Computing and Applications, 34(13), 10449–10467. https://doi.org/10.1007/s00521-020-05678-w
Sanker, V., Venkatesan, A., Salma, A., Sp, A., Li, Z., Heesen, P., Park, C., Park, D. J., & Desai, A. (2026). Artificial intelligence models in the surgical planning of low-grade gliomas: A systematic review. Frontiers in Oncology, 15(15), 1672289. https://doi.org/10.3389/fonc.2025.1672289
Santos, C., Aguiar, M., Welfer, D., & Belloni, B. (2022). A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model. Sensors, 22(17). https://doi.org/10.3390/s22176441
Shi, S., Zhang, L., Zhang, S., Shi, J., Hong, D., Wu, S., Pan, X., & Lin, W. (2025). Developing a rapid screening tool for high-risk ICU patients of sepsis: Integrating electronic medical records with machine learning methods for mortality prediction in hospitalized patients—model establishment, internal and external validation, and visualization. Journal of Translational Medicine, 23(1), 97. https://doi.org/10.1186/s12967-025-06102-4
Su, L., Xu, Z., Chang, F., Ma, Y., Liu, S., Jiang, H., Wang, H., Li, D., Chen, H., Zhou, X., Hong, N., Zhu, W., & Long, Y. (2021). Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models. Frontiers in Medicine, 8. https://doi.org/10.3389/fmed.2021.664966
Sun, H., Depraetere, K., Meesseman, L., Silva, P. C., Szymanowsky, R., Fliegenschmidt, J., Hulde, N., Dossow, V. von, Vanbiervliet, M., Baerdemaeker, J. D., Roccaro-Waldmeyer, D. M., Stieg, J., Hidalgo, M. D., & Dahlweid, F.-M. (2022). Machine Learning–Based Prediction Models for Different Clinical Risks in Different Hospitals: Evaluation of Live Performance. Journal of Medical Internet Research, 24(6), e34295. https://doi.org/10.2196/34295
Tang, R., Zhang, S., Ding, C., Zhu, M., & Gao, Y. (2022). Artificial Intelligence in Intensive Care Medicine: Bibliometric Analysis. Journal of Medical Internet Research, 24(11), e42185. https://doi.org/10.2196/42185
Thompson, S. G., & Sharp, S. J. (1999). Explaining heterogeneity in meta-analysis: A comparison of methods. Statistics in Medicine, 18(20), 2693–2708. https://doi.org/10.1002/(SICI)1097-0258(19991030)18:20%253C2693::AID-SIM235%253E3.0.CO;2-V
Thurzo, A., Urbanová, W., Novák, B., Czako, L., Siebert, T., Stano, P., Mareková, S., Fountoulaki, G., Kosnáčová, H., & Varga, I. (2022). Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare, 10(7). https://doi.org/10.3390/healthcare10071269
Tricco, A. C., Lillie, E., Zarin, W., O’Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., Hempel, S., Akl, E. A., Chang, C., McGowan, J., Stewart, L., Hartling, L., Aldcroft, A., Wilson, M. G., Garritty, C., … Straus, S. E. (2018). PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Annals of Internal Medicine, 169(7), 467–473. https://doi.org/10.7326/M18-0850
Vaid, A., Sawant, A., Suarez-Farinas, M., Lee, J., Kaul, S., Kovatch, P., Freeman, R., Jiang, J., Jayaraman, P., Fayad, Z., Argulian, E., Lerakis, S., Charney, A. W., Wang, F., Levin, M., Glicksberg, B., Narula, J., Hofer, I., Singh, K., & Nadkarni, G. N. (2023). Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings. Annals of Internal Medicine, 176(10), 1358–1369. https://doi.org/10.7326/M23-0949
Vijayan, M., & S, V. (2023). A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet. Diagnostics, 13(4). https://doi.org/10.3390/diagnostics13040774
Xia, L., He, S., Huang, Y.-F., & Ma, H. (2024). Multiscale dilated convolutional neural network for Atrial Fibrillation detection. PLOS ONE, 19(6), e0301691. https://doi.org/10.1371/journal.pone.0301691
Xie, J., Stavrakis, S., & Yao, B. (2024). Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet. Frontiers in Physiology, 15. https://doi.org/10.3389/fphys.2024.1362185
Xu, B., Martín, D., Khishe, M., & Boostani, R. (2022). COVID-19 diagnosis using chest CT scans and deep convolutional neural networks evolved by IP-based sine-cosine algorithm. Medical & Biological Engineering & Computing, 60(10), 2931–2949. https://doi.org/10.1007/s11517-022-02637-6
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Publicado
2026-03-18
Cómo citar
Zepeda Lugo , C., & Ramírez Castro, J. R. (2026). Tendencias y patrones de los modelos de inteligencia artificial en salud. Ciencia Latina Revista Científica Multidisciplinar, 10(1), 8080-8106. https://doi.org/10.37811/cl_rcm.v10i1.22887
Número
Sección
Ciencias de la Salud
Derechos de autor 2026 Carlos Zepeda Lugo , Jonathan Ramón Ramírez Castro
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
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