LATIDIA · Investigación

de los profesionales de la salud

arXiv: 2610.38006v1Announce Type: new Abstract: Este artículo se centra en el sesgo algorítmico de las aplicaciones de aprendizaje automático e inteligencia artificial en los sistemas de información sanitaria. Basado en los datos cuantitativos y qu

La noticia

arXiv:2610.38006v1 Announce Type: new Abstract: This paper focuses on algorithmic bias of machine learning and artificial intelligence applications in healthcare information systems. Based on the quantitative data and qualitative comments from a survey and interviews with healthcare professionals, who have different job roles (e.g., clinical vs. administrative), this study provides findings about the relationships between algorithmic bias, perceived fairness, and the intended acceptance and adoption of ML algorithms and algorithm generated outcomes. The results suggest that the opinions of healthcare professionals toward the causes of algorithmic bias, the criteria of algorithm assessment, the perceived fairness, and bias mitigation approaches may vary depending on their job roles, perspectives, tasks, and the