Artificial intelligence capabilities in hospital clinical management: a systematic review
Capacidades de la inteligencia artificial aplicadas a la gestión clínica hospitalaria: revisión sistemáticaArticle Sidebar
Main Article Content
Introduction: Artificial intelligence (AI) is increasingly incorporated into hospital management by supporting clinical data analysis, diagnostic decisions, and resource organization. Objective: To identify and analyze AI capabilities applied to hospital clinical and administrative management, as well as the models and algorithms used. Methods: A systematic review was conducted in accordance with PRISMA 2020. Scopus, PubMed, Web of Science, SciELO, and Redalyc were searched. Original full-text studies published between 2021 and 2025, conducted in hospitals, and explicitly applying AI were included. Of 1,195 records identified, 58 studies were selected. Results: Fifty studies (86.2%) addressed clinical management and nine (15.5%) administrative management; one study contributed to both categories. Information processing was the predominant capability (54/58; 93.1%), mainly through data analysis and modeling and image analysis. Deep-learning models, particularly convolutional neural networks, and machine-learning algorithms for classification and prediction predominated. Administrative applications focused on bed and appointment management, process control, and user support through natural language processing. Conclusion: AI contributes meaningfully to diagnostic accuracy, prognosis, and care efficiency. Its administrative use remains less frequent; implementation research assessing performance, safety, equity, and organizational impact is needed.
Introducción: La inteligencia artificial (IA) se incorpora progresivamente a la gestión hospitalaria al apoyar el análisis de datos clínicos, las decisiones diagnósticas y la organización de recursos. Objetivo: Identificar y analizar las capacidades de IA aplicadas a la gestión clínica y administrativa hospitalaria, así como los modelos y algoritmos empleados. Método: Se realizó una revisión sistemática conforme a PRISMA 2020. Se consultaron Scopus, PubMed, Web of Science, SciELO y Redalyc; se incluyeron artículos originales de acceso completo, publicados entre 2021 y 2025, realizados en hospitales y con aplicación explícita de IA. De 1.195 registros identificados, se seleccionaron 58 estudios. Resultados: Cincuenta estudios (86,2 %) abordaron la gestión clínica y nueve (15,5 %) la administrativa; un estudio contribuyó a ambas categorías. El procesamiento de información fue la capacidad predominante (54/58; 93,1 %), principalmente mediante análisis y modelado de datos, y análisis de imágenes. Predominaron los modelos de aprendizaje profundo, especialmente las redes neuronales convolucionales, y los algoritmos de aprendizaje automático para clasificación y predicción. En el ámbito administrativo, las aplicaciones se concentraron en la gestión de camas y citas, el control de procesos y la atención al usuario mediante procesamiento de lenguaje natural. Conclusión: La IA muestra una contribución relevante a la precisión diagnóstica, el pronóstico y la eficiencia asistencial. Su uso administrativo sigue siendo menos frecuente; por ello, son necesarios estudios de implementación que evalúen desempeño, seguridad, equidad y efecto organizacional.
Downloads
Article Details
Ali TE, Ali FI, Dakić P, Zoltan AD. Trends, prospects, challenges, and security in the healthcare internet of things. Computing. 2025;107(1):28. https://doi.org/10.1007/s00607-024-01352-4
David-López GP. Impacto social de la cuarta revolución industrial y la responsabilidad social empresarial como respuesta. I+D Revista de Investigaciones. 2022;17(2):103–12. https://doi.org/10.33304/revinv.v17n2-2022008
Pachón A. Cognición artificial: delegación de inteligencia en la era digital. In: Zarza Núñez T, Sánchez-Moñita M, editors. Los Flujos de la imagen. 2019. p. 84-95. https://doi.org/10.5281/zenodo.7656610
Bonami B, Piazentini L, Dala-Possa A. Education, Big Data and Artificial Intelligence: Mixed methods in digital platforms. Comunicar. 2020;28(65):43-52. https://doi.org/10.3916/C65-2020-04
Pinzon-Perez H, Smith JL, Perez AD. Artificial intelligence for the training of health professionals: opportunities and challenges. Rev Fac Med Hum. 2025;25(3):133–41. https://doi.org/10.25176/rfmh.v25i3.6859
Flores Arévalo J, Barbarán Mozo HP. Gestión Hospitalaria: una mirada al desarrollo de sus procesos. Ciencia Latina Revista Científica Multidisciplinar. 2021;5(2):1527-45. https://doi.org/10.37811/cl_rcm.v5i2.368
Vásquez Sáenz OA, Montenegro Camacho LA. Modernización de la gestión hospitalaria en instituciones prestadoras de salud. Rev. Cuerpo Med. HNAAA. 2022;15(4):639–43. https://doi.org/10.35434/rcmhnaaa.2022.154.1907
Biggs D, Vargas M, Larraín T, Alvear A, Pedemonte JC. Artificial Intelligence in medicine: Methods selection, applications and considerations (Part II). Revista Chilena de Anestesia. 2022;51(5):535-42. https://doi.org/10.25237/revchilanestv5129061641
Bohr A, Memarzadeh K. El auge de la inteligencia artificial en las aplicaciones sanitarias. In: Bohr A, Memarzadeh K, editors. Inteligencia artificial en el ámbito de la salud. Barcelona: Elsevier; 2022. p. 25-60.
https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Saban M, Esteban S, Rubinstein A, Cejas C, Perez-Acuna K. El impacto de la inteligencia artificial en la atención de la salud. Perspectivas y enfoques para América Latina y el Caribe, 2023. https://ai-globalhealthresearch.tghn.org/recursos-es/clias-technical-report-spanish/. https://doi.org/10.48060/tghn.125
Dwivedi YK, Hughes L, Ismagilova E, Aarts G, Coombs C, Crick T, et al. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int J Inf Manage. 2021;57:101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
Li J, Carayon P. Health Care 4.0: A vision for smart and connected health care. IISE Trans Healthc Syst Eng. 2021;11(3):171-180. https://doi.org/10.1080/24725579.2021.1884627
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. https://doi.org/10.1136/bmj.n71
Ardila R. Inteligencia. ¿Qué sabemos y qué nos falta por investigar? Rev Acad Colomb Cienc. 2011;35(134):97-103. https://doi.org/10.18257/raccefyn.35(134).2011.2491
Siemens G, Marmolejo-Ramos F, Gabriel F, Medeiros K, Marrone R, Joksimovic S, et al. Human and artificial cognition. Computers and Education: Artificial Intelligence. 2022;3:100107. https://doi.org/10.1016/j.caeai.2022.100107
Schmid T, Hildesheim W, Holoyad T, Schumacher K. The AI Methods, Capabilities and Criticality Grid: A Three-Dimensional Classification Scheme for Artificial Intelligence Applications. KI - Kunstliche Intelligenz. 2021;35(3-4):425-40. https://doi.org/10.1007/s13218-021-00736-4
Ngiam KY, Khor IW. Big data and machine learning algorithms for health-care delivery. Lancet Oncol. 2019 May;20(5):e262-e273.
https://doi.org/10.1016/S1470-2045(19)30149-4
Cabrera-Mudarra RD. Inteligencia artificial y calidad clínica: Herramientas para optimizar el desempeño hospitalario. Noesis. 2025;7(14):192–210. https://doi.org/10.35381/noesisin.v7i14.349
Valero-Tena E, Roca-Espiau M, Verdú-Díaz J, Diaz-Manera J, Andrade-Campos M, Giraldo P. Advantages of digital technology in the assessment of bone marrow involvement in Gaucher's disease. Front Med (Lausanne). 2023;10:1098472. https://doi.org/10.3389/fmed.2023.1098472
Gambarte MJ, Higa C, Novo F, Ciambrone GM, Tupayachi Villagomez OD, Ginesi A, et al. Comparación pronóstica entre scores de riesgo y la aplicación de redes neuronales para la predicción de la mortalidad a corto y mediano plazo en pacientes con insuficiencia cardíaca. Rev Argent Cardiol. 2021;89(5):416-426. https://doi.org/10.7775/rac.es.v89.i5.20434
Formica C, Bonanno L, Giambò FM, Maresca G, Latella D, Marra A, et al. Paving the Way for Predicting the Progression of Cognitive Decline: The Potential Role of Machine Learning Algorithms in the Clinical Management of Neurodegenerative Disorders. J Pers Med. 2023 Sep 15;13(9):1386. https://doi.org/10.3390/jpm13091386
Luo L, Zhang J. The Value of TruScreen (An Artificial Intelligence Cervical Cancer Screening System) in High-Risk HPV Positive Patients. Clin Exp Obstet Gynecol. 2023 Oct 1;50(10):206. https://doi.org/10.31083/j.ceog5010206
Deps PD, Yotsu R, Furriel BCRS, de Oliveira BD, de Lima SL, Loureiro RM. The potential role of artificial intelligence in the clinical management of Hansen's disease (leprosy). Front Med (Lausanne). 2024;11:1338598. https://doi.org/10.3389/fmed.2024.1338598
O'Dowd E, Berovic M, Callister M, Chalitsios CV, Chopra D, Das I, et al. Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study. BMJ Open. 2024 Jan 4;14(1):e077747. https://doi.org/10.1136/bmjopen-2023-077747
Majumdar A, Lad J, Tumanova K, Serra S, Quereshy F, Khorasani M, et al. Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging. J Biomed Opt. 2023 Nov 23;29(5):052915. https://doi.org/10.1117/1.jbo.29.5.052915
Yasar O, Long P, Harder B, Marshall H, Bhasin S, Lee S, et al. Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH). BMJ Health Care Inform. 2022 Mar 30;29(1):e100510. https://doi.org/10.1136/bmjhci-2021-100510
Beunza JJ, Lafuente JL, González S, Gómez-Tello V. Artificial intelligence and the Internet of Medical Things in the ICU: Time for implementation | Inteligencia artificial e Internet of Medical Things en UCI: momento de la implementación. Med Intensiva. 2024;48(1):56–8. https://doi.org/10.1016/j.medin.2023.10.002
Evertz R, Lange T, Backhaus SJ, Schulz A, Beuthner BE, Topci R, et al. Artificial Intelligence Enabled Fully Automated CMR Function Quantification for Optimized Risk Stratification in Patients Undergoing Transcatheter Aortic Valve Replacement. J Interv Cardiol. 2022;2022:1368878. https://doi.org/10.1155/2022/1368878
Arnaud E, Elbattah M, Ammirati C, Dequen G, Ghazali DA. Use of Artificial Intelligence to Manage Patient Flow in Emergency Department during the COVID-19 Pandemic: A Prospective, Single-Center Study. Int J Environ Res Public Health. 2022;19(15):9667. https://doi.org/10.3390/ijerph19159667
Burnette H, Pabani A, von Itzstein MS, Switzer B, Fan R, Ye F, et al. Use of artificial intelligence chatbots in clinical management of immune-related adverse events. J Immunother Cancer. 2024 May 30;12(5):e008599.
https://doi.org/10.1136/jitc-2023-008599
Schulz S, Woerl AC, Jungmann F, Glasner C, Stenzel P, Strobl S, et al. Multimodal Deep Learning for Prognosis Prediction in Renal Cancer. Front Oncol. 2021 Nov 24;11:788740. https://doi.org/10.3389/fonc.2021.788740
Diestra-Quinto NM, Cordova-Villodas AJ, Caruajulca-Montero CP, Esquivel-Cueva DL, Nina-Vera SA. La inteligencia artificial y la toma de decisiones gerenciales. Revista de Investigación Valor Agregado. 2021;8(1):52-69. https://doi.org/10.17162/riva.v8i1.1631
Yan C, Wang L, Lin J, Xu J, Zhang T, Qi J, et al. A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis. Eur Radiol. 2022;32(4):2188–99. https://doi.org/10.1007/s00330-021-08365-z
Tohyama T, Ide T, Ikeda M, Kaku H, Enzan N, Matsushima S, et al. Machine learning-based model for predicting 1 year mortality of hospitalized patients with heart failure. ESC Heart Fail. 2021;8(5):4077–85. https://doi.org/10.1002/ehf2.13556
Vargas M, Biggs D, Larraín T, Alvear A, Pedemonte JC. Artificial Intelligence in medicine: Modeling methods (Part I). Revista Chilena de Anestesia. 2022;51(5):527–34. https://doi.org/10.25237/revchilanestv5129061230
Escolar V, Lozano A, Larburu N, Kerexeta J, Álvarez R, Echebarria A, et al. Prediction of heart failure decompensations using artificial intelligence and machine learning techniques. Rev Colomb Cardiol. 2022;29(4):431-40. https://doi.org/10.24875/rccar.21000023
Sloan M, Li H, Lescay HA, Judge C, Lan L, Hajiyev P, et al. Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound. Investig Clin Urol. 2023 Nov;64(6):588-596. https://doi.org/10.4111/icu.20230170
Nisar DEM, Amin R, Shah NUH, Ghamdi MAA, Almotiri SH, Alruily M. Healthcare Techniques through Deep Learning: Issues, Challenges and Opportunities. IEEE Access. 2021;9:98523-41. https://doi.org/10.1109/ACCESS.2021.3095312
Shen X, Zhou Y, Shi X, Zhang S, Ding S, Ni L, et al. The application of deep learning in abdominal trauma diagnosis by CT imaging. World J Emerg Surg. 2024 May 6;19(1):17. https://doi.org/10.1186/s13017-024-00546-7
Fúquene Ardila HJ. Procesamiento de Lenguaje Natural, los Transformers y los Bots Conversacionales. XIKUA Boletín Científico de la Escuela Superior de Tlahuelilpan. 2024 5;12(Especial):151–60. https://doi.org/10.29057/xikua.v12iespecial.12904
Saranya A, Subhashini R. A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends. Decision Analytics Journal. 2023 1;7:100230. https://doi.org/10.1016/j.dajour.2023.100230
Lee DH, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health. 2021;18(1):271. https://doi.org/10.3390/ijerph18010271
Morone G, De Angelis L, Martino Cinnera A, Carbonetti R, Bisirri A, Ciancarelli I, et al. Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting. Front Digit Health. 2025;7:1550731. https://doi.org/10.3389/fdgth.2025.1550731
Ajmera P, Dillard R, Kline T, Missert A, Korfiatis P, Khandelwal A. FDA-approved artificial intelligence products in abdominal imaging: A comprehensive review. Curr Probl Diagn Radiol. 2026;55(2):258-267. https://doi.org/10.1067/j.cpradiol.2025.04.011
Qi Y, Mohamad E, Azlan AA, Zhang C. Utilization of artificial intelligence in clinical practice: A systematic review of China's experiences. Digit Health. 2025;11:20552076251343752. https://doi.org/10.1177/20552076251343752
Velekkat Suraj N. Exploring the limits of AI: Does AI mimic understanding or truly simulate intelligence? Journal of Software Engineering and Simulation. 2025;11(6):75–8. https://doi.org/10.35629/3795-11067578
Ríos Quinte RJ, Ortiz Osorio AB, Toaquiza Toapanta CD, Landi Faican EA, García Toala JA. New advances in artificial intelligence for the diagnosis and treatment of colorectal cancer: a literature review. Sapienza. 2024 15;5(1):e24012. https://doi.org/10.51798/sijis.v5i1.733
Shilpa, Kaur T, Garg R. Digital healthcare: A topical and futuristic review of technological and robotic revolution. Paladyn. 2023;14(1):20220108.
https://doi.org/10.1515/pjbr-2022-0108
Mbunge E, Muchemwa B, Jiyane S, Batani J. Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies. Glob Health J. 2021;5(4):169–177. https://doi.org/10.1016/j.glohj.2021.11.008
Brady HE. The Challenge of Big Data and Data Science. Annu Rev Polit Sci. 2019;22:297-323. https://doi.org/10.1146/annurev-polisci-090216-023229
Mi D, Li Y, Zhang K, Huang C, Shan W, Zhang J. Exploring intelligent hospital management mode based on artificial intelligence. Front Public Health. 2023;11:1182329. https://doi.org/10.3389/fpubh.2023.1182329
Lavista Ferres JM, Oviedo F, Robinson C, Chu L, Kawamoto S, Afghani E, et al. Performance of explainable artificial intelligence in guiding the management of patients with a pancreatic cyst. Pancreatology. 2024;24(7):1182–91. https://doi.org/10.1016/j.pan.2024.09.001
Junior MAFR, Smaniotto R, Gebran A, Zamudio JP, Mohseni S, da Silva Rodrigues JM, et al. The use of POTTER (Predictive Optimal Trees in Emergency Surgery Risk) calculator to predict mortality and complications in patients submitted to Emergency Surgery. Rev Col Bras Cir. 2023;50:e20233624. https://doi.org/10.1590/0100-6991e-20233624-en
Samaras A, Bekiaridou A, Papazoglou AS, Moysidis DV, Tsoumakas G, Bamidis P, et al. Artificial intelligence-based mining of electronic health record data to accelerate the digital transformation of the national cardiovascular ecosystem: design protocol of the CardioMining study. BMJ Open. 2023 Apr 3;13(4):e068698. https://doi.org/10.1136/bmjopen-2022-068698
Krzowski B, Rokicki J, Główczyńska R, Fajkis-Zajączkowska N, Barczewska K, Mąsior M, et al. The Use of Machine Learning Algorithms in the Evaluation of the Effectiveness of Resynchronization Therapy. J Cardiovasc Dev Dis. 2022;9(1):17. https://doi.org/10.3390/jcdd9010017
Ghanimi R, Chouikri K, Ghanimi I, Ghanimi F, Soulaymani A. Un enfoque basado en el aprendizaje automático para el diagnóstico urgente de envenenamiento en situaciones de emergencia. Data and Metadata. 2023;2:110. https://doi.org/10.56294/dm2023110
Gómez-Zuleta MA, Cano-Rosales DF, Bravo-Higuera DF, Ruano-Balseca JA, Romero-Castro E. Detección automática de pólipos colorrectales con técnicas de inteligencia artificial. Rev Colomb Gastroenterol. 2021;36(1):7–17. https://doi.org/10.22516/25007440.471
Lu Y, Wu J, Zhuo X, Hu M, Chen Y, Luo Y, et al. Real-Time Artificial Intelligence-Based Histologic Classifications of Colorectal Polyps Using Narrow-Band Imaging. Front Oncol. 2022 26;12:879239. https://doi.org/10.3389/fonc.2022.879239
Zand A, Stokes Z, Sharma A, van Deen WK, Hommes D. Artificial Intelligence for Inflammatory Bowel Diseases (IBD); Accurately Predicting Adverse Outcomes Using Machine Learning. Dig Dis Sci. 2022;67(10):4874-85.
https://doi.org/10.1007/s10620-022-07506-8
Vural O, Ozaydin B, Aram KY, Booth J, Lindsey BF, Ahmed A. An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study. JMIR Med Inform. 2025;13:e73960. https://doi.org/10.2196/73960
Junior AP, Homem TPD, Teixeira FO. Aplicación de inteligencia artificial para monitorear el uso de mascarillas de protección. Revista Científica General José María Córdova. 2021;19(33):205–22. https://doi.org/10.21830/19006586.725
Parham GP, Egemen D, Befano B, Mwanahamuntu MH, Rodriguez AC, Antani S, et al. Validation in Zambia of a cervical screening strategy including HPV genotyping and artificial intelligence (AI)-based automated visual evaluation. Infect Agent Cancer. 2023;18(1):61. https://doi.org/10.1186/s13027-023-00536-5
Browning L, Jesus C, Malacrino S, Guan Y, White K, Puddle A, et al. Artificial Intelligence-Based Quality Assessment of Histopathology Whole-Slide Images within a Clinical Workflow: Assessment of 'PathProfiler' in a Diagnostic Pathology Setting. Diagnostics. 2024;14(10):990. https://doi.org/10.3390/diagnostics14100990
Hirokawa M, Niioka H, Suzuki A, Abe M, Arai Y, Nagahara H, et al. Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology. Cancer Cytopathol. 2023;131(4):217-225. https://doi.org/10.1002/cncy.22669
Hagi T, Nakamura T, Yuasa H, Uchida K, Asanuma K, Sudo A, et al. Prediction of prognosis using artificial intelligence-based histopathological image analysis in patients with soft tissue sarcomas. Cancer Med. 2024;13(10):e7252. https://doi.org/10.1002/cam4.7252
Lee JH, Song GY, Lee J, Kang SR, Moon KM, Choi YD, et al. Prediction of immunochemotherapy response for diffuse large B-cell lymphoma using artificial intelligence digital pathology. J Pathol Clin Res. 2024;10(3):e12370. https://doi.org/10.1002/2056-4538.12370
Deng J, Zhao M, Li Q, Zhang Y, Ma M, Li C, et al. Implementation of artificial intelligence in the histological assessment of pulmonary subsolid nodules. Transl Lung Cancer Res. 2021 1;10(12):4574-86. https://doi.org/10.21037/tlcr-21-971
Chakrabarty S, LaMontagne P, Shimony J, Marcus DS, Sotiras A. MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network. Neurooncol Adv. 2023;5(1):vdad023. https://doi.org/10.1093/noajnl/vdad023
Ibáñez-Bruron MC, Cruzat A, Órdenes-Cavieres G, Coria M. Exactitud de tamizaje de retinopatía diabética: inteligencia artificial versus tecnólogos médicos entrenados. Rev Med Chil. 2021;149(4):493-500. https://doi.org/10.4067/s0034-98872021000400493
Yan Q, Jiang Y, Huang H, Swaroop A, Chew EY, Weeks DE, et al. Genome-wide association studies-based machine learning for prediction of age-related macular degeneration risk. Transl Vis Sci Technol. 2021;10(2):29. https://doi.org/10.1167/tvst.10.2.29
Lau CW, Catchpoole D, Simoff S, Zhang D, Nguyen QV. A Game-Theoretical Approach to Clinical Decision Making with Immersive Visualisation. Appl Sci. 2023;13(18):10178. https://doi.org/10.3390/app131810178
Tarnowska KA, Ras ZW, Jastreboff PJ. A data-driven approach to clinical decision support in tinnitus retraining therapy. Front Neuroinform. 2022;16:934433. https://doi.org/10.3389/fninf.2022.934433
Saibene AM, Allevi F, Calvo-Henriquez C, Maniaci A, Mayo-Yáñez M, Paderno A, et al. Reliability of large language models in managing odontogenic sinusitis clinical scenarios: a preliminary multidisciplinary evaluation. Eur Arch Otorhinolaryngol. 2024;281(4):1835-41. https://doi.org/10.1007/s00405-023-08372-4
Asci F, Marsili L, Suppa A, Saggio G, Michetti E, Di Leo P, et al. Acoustic analysis in stuttering: a machine-learning study. Front Neurol. 2023;14:1169707. https://doi.org/10.3389/fneur.2023.1169707
Fan X, Chen L, Tang W, Sun L, Wang J, Liu S, et al. Prediction of outpatient visits for allergic rhinitis using an artificial intelligence LSTM model - a study in Eastern China. BMC Public Health. 2025;25(1):1328. https://doi.org/10.1186/s12889-025-22430-y
Lee CC, Lee WK, Wu CC, Lu CF, Yang HC, Chen YW, et al. Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery. Sci Rep. 2021;11(1):3106. https://doi.org/10.1038/s41598-021-82665-8
Wang X, Zhou Y, Deng D, Li H, Guan X, Fang L, et al. Developing a deep learning model to predict epilepsy recurrence in patients with focal cortical dysplasia type III. Quant Imaging Med Surg. 2023;13(2):999-1008. https://doi.org/10.21037/qims-22-276
Ke X, Hu W, Su X, Huang F, Lai Q. Potential of artificial intelligence based on chest computed tomography to predict the nature of part-solid nodules. Clin Respir J. 2023;17(4):320-8. https://doi.org/10.1111/crj.13597
Wang C, Shao J, Xu X, Yi L, Wang G, Bai C, et al. DeepLN: A Multi-Task AI Tool to Predict the Imaging Characteristics, Malignancy and Pathological Subtypes in CT-Detected Pulmonary Nodules. Front Oncol. 2022 11;12:683792. https://doi.org/10.3389/fonc.2022.683792
Yu L, Shi X, Liu X, Jin W, Jia X, Xi S, et al. Artificial Intelligence Systems for Diagnosis and Clinical Classification of COVID-19. Front Microbiol. 2021;12:729455. https://doi.org/10.3389/fmicb.2021.729455
Ding X, Xu J, Xu H, Zhou J, Long Q. Risk assessment using early quantitative chest CT parameters for the severity of COVID-19. Iran J Radiol. 2021;18(3):e109439. https://doi.org/10.5812/iranjradiol.109439
Drozdov I, Szubert B, Reda E, Makary P, Forbes D, Chang SL, et al. Development and prospective validation of COVID-19 chest X-ray screening model for patients attending emergency departments. Sci Rep. 2021;11(1):20384.
https://doi.org/10.1038/s41598-021-99986-3
Yuan L, Chen J, Feng H, Lv J, Lu X, Ji M. Early Identification of COVID-19 Progression to Its Severe Form Using Artificial Intelligence. Iran J Radiol. 2022;19(1):e112562. https://doi.org/10.5812/iranjradiol.112562
Dipnall JF, Page R, Du L, Costa M, Lyons RA, Cameron P, et al. Predicting fracture outcomes from clinical registry data using artificial intelligence supplemented models for evidence-informed treatment (PRAISE) study protocol. PLoS One. 2021;16(9):e0257361. https://doi.org/10.1371/journal.pone.0257361
Scherer JS, Pereira JS, Debastiani MS, Bica CG. Beyond technology: Can artificial intelligence support clinical decisions in the prediction of sepsis? Rev Bras Enferm. 2022;75(5):e20210586. https://doi.org/10.1590/0034-7167-2021-0586
Singh P, Nagori A, Lodha R, Sethi T. Early prediction of hypothermia in pediatric intensive care units using machine learning. Front Physiol. 2022 Sep 2;13:921884. https://doi.org/10.3389/fphys.2022.921884