Resumen
Objetivo: Presentar la Inteligencia Artificial (IA) aplicada a la salud, analizando su potencial para optimizar diagnósticos y tratamientos en un escenario de transformación sin precedentes. El estudio evalúa cómo la supervisión, el juicio clínico y la empatía del médico permanecerán como pilares fundamentales de la práctica médica. Revisión bibliográfica: Esta revisión narrativa explora la tesis de que, lejos de sustituir a los profesionales humanos, la IA se convertirá en una herramienta sinérgica indispensable. Utilizando la analogía del piloto automático en la aviación, el artículo postula que el médico actuará como el "comandante del cuidado", mientras que la IA funcionará como un "copiloto" de alto rendimiento, automatizando tareas analíticas y procesando grandes volúmenes de datos. Se discuten las profundas implicaciones para la formación médica, que requerirá una reforma curricular para incluir nociones de ciencia de datos y fortalecer habilidades insustituibles como la ética, la comunicación y la empatía, potenciando la relación médico-paciente. Consideraciones finales: Se puede considerar que la integración exitosa de la IA dependerá de la capacidad del sistema de salud para cultivar un modelo colaborativo. En este modelo, la tecnología no suplanta, sino que potencia la capacidad humana, permitiendo una práctica médica más precisa, eficiente y, paradójicamente, más humana.
Referencias
AHUJA AS. The Impact of Artificial Intelligence in Medicine on the Future Role of the Physician PeerJ, 2019; 7: 7702.
ARDILA D, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nature Medicine, 2019; 25(6): 954-961.
BAINBRIDGE L. Ironies of automation. Automatica, 1983; 19(6): 775-779.
BARTUSIK-AEBISHER D, et al. Artificial intelligence and ECG: a new frontier in cardiac diagnostics and prevention. Biomedicines, 2025; 13: 1685.
BRASIL. Ministério da Saúde. Programa SUS Digital: Estratégia de Saúde Digital para o Brasil 2020-2028. Brasília, DF, 2020.
CHEN RJ, et al. Algorithm fairness in artificial intelligence for medicine and healthcare. Nature Biomedical Engineering, 2023; 7(6): 719-742.
CONNOR CW. Artificial Intelligence and Machine Learning in Anesthesiology. Anesthesiology, 2019; 131(6): 1346-1359.
COOKE M, et al. Educating physicians: a call for reform of medical school and residency. Jossey-Bass, 2010; 296.
DOSHI A, et al. The role of simulation in building empathy in healthcare. The Clinical Teacher, 2019; 16(5): 429-433.
ESTEVA A, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017; 542(7639): 115-118.
GULSHAN V, et al. Development and validation of a deep learning algorithm for the detection of diabetic retinopathy in retinal fundus photographs. JAMA, 2016; 316(22): 2402-2410.
HASHIMOTO DA, et al. Artificial intelligence in surgery: promises and perils. Annals of Surgery, 2018; 268(1): 70-76.
JOHNSTON SC. Anticipating and training the physician of the future: the importance of caring in an age of artificial intelligence. Academic Medicine, 2018; 93(8): 1105-1110.
KOLACHALAMA VK e GARG PS. Machine learning and medical education. NPJ Digital Medicine, 2018; 1(1): 54.
KUMAR A. AI driven precision oncology: predictive biomarker discovery and personalized treatment optimization using genomic data. International Journal of Advance Research Publication and Reviews, 2024; 1(3): 21-39.
MASTERS K. Artificial intelligence in medical education. Medical Teacher, 2019; 41(9): 976-980.
NEMATI S, et al. An interpretable machine learning model for accurate prediction of sepsis in the ICU. Critical Care Medicine, 2018; 46(4): 547-553.
OBERMEYER Z, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science, 2019; 366(6464): 447-453.
PIRRERA A, et al. Human-machine collaboration in diagnostics: Exploring the synergy in clinical imaging with artificial intelligence. Diagnostics, 2023; 13(1): 123.
RATTI E, et al. Ethical and social considerations of applying artificial intelligence in healthcare: a two-pronged scoping review. BMC Medical Ethics, 2025; 26: 68.
SINGHAL K, et al. Large language models encode clinical knowledge. Nature, 2023; 620(7972): 172-180.
SULLENBERGER BS. Highest Duty: My Search for What Really Matters. William Morrow, 2009; 352.
TOPOL EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 2019; 25(1): 44-56.
VERGHESE A, et al. What this computer needs is a physician: humanism and artificial intelligence. JAMA, 2018; 319(1): 19-20.
VERGHESE A. A doctor's touch. TED Conferences, 2011.
WANG Y, et al. Clinical information extraction applications: a literature review. Journal of Biomedical Informatics, 2018; 77: 34-49.
WHO. Global strategy on digital health 2020-2025. World Health Organization, 2024.
WIENER EL. Human factors of advanced technology ("glass cockpit") transport aircraft. NASA Contractor Report 177528, 1989.
