Inteligencia artificial en el abordaje clínico del cáncer de pulmón en el continuum asistencial: revisión crítica
Abstract
Objetivos: analizar el impacto de la inteligencia artificial en el abordaje integral del cáncer pulmonar, evaluando sus aplicaciones diagnósticas, terapéuticas y pronósticas, así como las barreras para su implementación clínica en el periodo 2021-2025. Métodos: se realizó una revisión narrativa sobre estudios basados en el uso de algoritmos aplicados a la prevención personalizada, el cribado automatizado, el diagnóstico de precisión y la estratificación pronóstica individualizada. Resultados: se identificaron beneficios potenciales como mayor sensibilidad en detección temprana y optimización de decisiones terapéuticas, junto con limitaciones asociadas a opacidad algorítmica, sesgos en datos, falta de validación robusta e inequidad en el acceso. Conclusiones: la integración de inteligencia artificial en oncología torácica requiere combinar juicio clínico, capacidad computacional y gobernanza ética para lograr un impacto equitativo y sostenible.
Objective: to analyze the impact of artificial intelligence on the comprehensive management of lung cancer, assessing its diagnostic, therapeutic, and prognostic applications, as well as the barriers to its clinical implementation during the period 2021-2025. Methods: A narrative review was conducted of studies applying algorithms to personalized prevention, automated screening, precision diagnosis, and individualized prognostic stratification. Results: Potential benefits identified include greater sensitivity for early detection and optimization of therapeutic decision-making, along with limitations such as algorithmic opacity, data bias, lack of robust validation, and inequitable access. Conclusions: The integration of artificial intelligence into thoracic oncology requires the combination of clinical judgment, computational capacity, and ethical governance to achieve and equitable and sustainable impact.
Objective: to analyze the impact of artificial intelligence on the comprehensive management of lung cancer, assessing its diagnostic, therapeutic, and prognostic applications, as well as the barriers to its clinical implementation during the period 2021-2025. Methods: A narrative review was conducted of studies applying algorithms to personalized prevention, automated screening, precision diagnosis, and individualized prognostic stratification. Results: Potential benefits identified include greater sensitivity for early detection and optimization of therapeutic decision-making, along with limitations such as algorithmic opacity, data bias, lack of robust validation, and inequitable access. Conclusions: The integration of artificial intelligence into thoracic oncology requires the combination of clinical judgment, computational capacity, and ethical governance to achieve and equitable and sustainable impact.
Description
Vol. 48, No. 2