El futuro de la inteligencia artificial en enfermedades infecciosas
Contenido principal del artículo
Detalles del artículo
Citas
Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. https://doi.org/10.1038/s41591-018-0300-7
Kraemer MUG, Tsui JL, Chang SY, Lytras S, Khurana MP, Vanderslott S, et al. Artificial intelligence for modelling infectious disease epidemics. Nature. 2025;638(8051):623-635. https://doi.org/10.1038/s41586-024-08564-w
Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020;180(4):688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021
Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang B, Mehta H, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 2018 20;15(11):e1002686. https://doi.org/10.1371/journal.pmed.1002686
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395(10236):1579-1586. https://doi.org/10.1016/S0140-6736(20)30226-9
Chu WT, Reza SMS, Anibal JT, Landa A, Crozier I, Bagci U, et al. Artificial Intelligence and Infectious Disease Imaging. J Infect Dis. 2023 Oct 3;228(Suppl 4):S322-S336. https://doi.org/10.1093/infdis/jiad158
Miglietta L, Rawson TM, Galiwango R, Tasker A, Ming DK, Akogo D, et al. Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives. Lancet Infect Dis. 2025 6:S1473-3099(25)003548. https://doi.org/10.1016/S1473-3099(25)00354-8
Anzaku ET, Mohammed MA, Ozbulak U, Won J, Hong H, Krishnamoorthy J, Van Hoecke S, Magez S, Van Messem A, De Neve W. Tryp: a dataset of microscopy images of unstained thick blood smears for trypanosome detection. Sci Data. 2023 Oct 18;10(1):716. https://doi.org/10.1038/s41597-023-02608-y
Charkoftaki, G., Aalizadeh, R., Santos-Neto, A, Tan WY, Davidson EA, Nikolopoulou V. et al. An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model. Hum Genomics 2023 Aug 29;17(1):80. https://doi.org/10.1186/s40246-023-00521-4
Hun A, Bautista-Castillo A, Osuna I, Nasto K, Munoz FM, Schutze GE et al. Distinguishing Multisystem Inflammatory Syndrome in Children From Typhus Using Artificial Intelligence: MIS-C Versus Endemic Typhus (AIMET). J Infect Dis. 2025 Apr 15;231(4):931-939. https://doi.org/10.1093/infdis/jiaf004
Orjuela-Cañón AD, Jutinico AL, Awad C, Vergara E, Palencia A. Machine learning in the loop for tuberculosis diagnosis support. Front Public Health. 2022 Jul 26;10:876949. PMID:35958865; PMCID: PMC9362992. https://doi.org/10.3389/fpubh.2022.876949
Tejeda MI, Fernández J, Valledor P, Almirall C, Barberán J, Romero-Brufau S. Retrospective validation study of a machine learning-based software for empirical and organism-targeted antibiotic therapy selection. Antimicrob Agents Chemother. 2024 Oct 8;68(10):e0077724. https://doi.org/10.1128/aac.00777-24
Scarpino SV, Petri G. On the predictability of infectious disease outbreaks. Nat Commun. 2019 Feb 22;10(1):898. https://doi.org/10.1038/s41467-019-08616-0
Grantz KH, Meredith HR, Cummings DAT, Metcalf CJE, Grenfell BT, Giles JR et al. The use of mobile phone data to inform analysis of COVID-19 pandemic epidemiology. Nat Commun. 2020 Sep 30;11(1):4961. https://doi.org/10.1038/s41467-020-18190-5
Odone A, Barbati C, Amadasi S, Schultz T, Resnik DB. Artificial intelligence and infectious diseases: an evidence-driven conceptual framework for research, public health, and clinical practice. Lancet Infect Dis. 2025 16:S1473-3099(25)00412-8. https://doi.org/10.1016/S1473-3099(25)00412-8
Zhao N, Charland K, Carabali M, Nsoesie EO, Maheu-Giroux M, Rees E, Yuan M, Garcia Balaguera C, Jaramillo Ramirez G, Zinszer K. Machine learning and dengue forecasting: Comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl Trop Dis. 2020 Sep 24;14(9):e0008056. https://doi.org/10.1371/journal.pntd.0008056
Triana-Avellaneda IC, Molina-Meza JD, Pino-Villarreal LE. Vigilancia epidemiológica de enfermedades transmisibles en Colombia mediante modelos generativos basados en inteligencia artificial para el seguimiento de donantes en Colombia. Acta Médica Colomb. 2024 Nov 12;50(1):1-6. https://doi.org/10.36104/amc.2025.3814
Samuel H. King, Claudia L. Driscoll, David B. Li, Daniel Guo, Aditi T. Merchant, Garyk Brixi, Max E. Wilkinson, Brian L. Hie. Generative design of novel bacteriophages with genome language models. bioRxiv 2025.09.12.675911; https://doi.org/10.1101/2025.09.12.675911
Lin TT, Yang LY, Lin CY, Wang CT, Lai CW, Ko CF, Shih YH, Chen SH. Intelligent De Novo Design of Novel Antimicrobial Peptides against Antibiotic-Resistant Bacteria Strains. Int J Mol Sci. 2023 Apr 5;24(7):6788. https://doi.org/10.3390/ijms24076788
Gomez-Marín, Jorge E. Ciencia, salud pública y toma de decisiones. Infectio, 2021. 25(4), 205-206. https://doi.org/10.22354/in.v25i4.952