(Entrevista 002) Entrevista dirigida a pacientes y personal de salud. Contamos con la colaboración de Álvaro Arbeláez Cortés, médico reumatólogo, estudiante de maestría en telesalud, colega y amigo. En caso de tener dudas o desear continuar con la discusión, quedan a disposición los foros en www.reumatologia.online y nuestras redes sociales que están adjuntas en la descripción de este podcast. Hoy el tema será inteligencia artificial en reumatología y sus posibles aplicaciones en esta área de la salud. ENLACES:Pagina web: www.reumatologia.onlineREDES:-Álvaro Arbeláez: Twitter @arbelaez_cortes. Correo: aarbelaezc@gmail.com. Consultorio 3154809316.LECTURAS RECOMENDADAS:[1] Quer G, Muse ED, Nikzad N, Topol EJ, Steinhubl SR. Augmenting diagnostic vision with AI. Lancet 2017;390:221. https://doi.org/10.1016/S0140-6736(17)31764-6.[2] Torkamani A, Andersen KG, Steinhubl SR, Topol EJ. High-Definition Medicine. Cell 2017;170:828–43. https://doi.org/10.1016/j.cell.2017.08.007.[3] Steinhubl SR, Topol EJ. Moving From Digitalization to Digitization in Cardiovascular Care: Why Is it Important, and What Could it Mean for Patients and Providers? J Am Coll Cardiol 2015;66:1489–96. https://doi.org/10.1016/J.JACC.2015.08.006.[4] Lin C, Karlson EW, Canhao H, Miller TA, Dligach D, Chen PJ, et al. Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records. PLoS One 2013;8:e69932. https://doi.org/10.1371/journal.pone.0069932.[5] Lezcano-Valverde JM, Salazar F, Leon L, Toledano E, Jover JA, Fernandez-Gutierrez B, et al. Development and validation of a multivariate predictive model for rheumatoid arthritis mortality using a machine learning approach. Sci Rep 2017;7:10189. https://doi.org/10.1038/s41598-017-10558-w.[6] Norgeot B, Glicksberg BS, Trupin L, Lituiev D, Gianfrancesco M, Oskotsky B, et al. Assessment of a Deep Learning Model Based on Electronic Health Record Data to Forecast Clinical Outcomes in Patients With Rheumatoid Arthritis. JAMA Netw Open 2019;2:e190606. https://doi.org/10.1001/jamanetworkopen.2019.0606.[7] Orange DE, Agius P, DiCarlo EF, Robine N, Geiger H, Szymonifka J, et al. Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data. Arthritis Rheumatol (Hoboken, NJ) 2018;70:690–701. https://doi.org/10.1002/art.40428.[8] Chin C-Y, Hsieh S-Y, Tseng VS. eDRAM: Effective early disease risk assessment with matrix factorization on a large-scale medical database: A case study on rheumatoid arthritis. PLoS One 2018;13:e0207579. https://doi.org/10.1371/journal.pone.0207579.[9] Parimbelli E, Wilk S, Kingwell S, Andreev P, Michalowski W. Shared Decision-Making Ontology for a Healthcare Team Executing a Workflow, an Instantiation for Metastatic Spinal Cord Compression Management. AMIA . Annu Symp Proceedings AMIA Symp 2018;2018:877–86.[10] Rozenblum R, Rodriguez-Monguio R, Volk LA, Forsythe KJ, Myers S, McGurrin M, et al. Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation. Jt Comm J Qual Patient Saf 2020;46:3–10. https://doi.org/10.1016/j.jcjq.2019.09.008.