Analyzing key attributes of AI and technologies for biometric authentication in smart hospitals

• Autores: Gutiérrez Sandoval , Carlos Daniel, Acuña Cisneros, Cesar Alexander, Mendoza De Los Santos, Alberto Carlos
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/201
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/201; https://doi.org/10.48168/innosoft.s24.a201; https://n2t.net/ark:/42411/s24/a201
• Nivel de acceso: acceso abierto
• Materia: risk analysis; cybersecurity; intrusion detection; data privacy; health systems; análisis de riesgos; ciberseguridad; detección de intrusos; privacidad de datos; sistemas de salud

This systematic review examines the use of artificial intelligence (AI) in biometric authentication for smart hospitals, with a particular focus on identifying the most efficient and widely used technological approaches in the world to improve the security and protection of medical data from unauthorized access. The integration of artificial intelligence (AI) through neural networks and machine learning algorithms with biometric security has been demonstrated to enhance the precision of individual identification and the detection of irregular behavior that may indicate unauthorized access. The PRISMA methodology entailed a comprehensive search of scientific studies employing key terms in conjunction with Boolean operators, followed by the selection of pertinent articles in accordance with pre-established inclusion and exclusion criteria. The results demonstrate that the incorporation of AI into biometric authentication systems enhances security in terms of controlled access, protection, and data security. The analyzed studies indicate that the deployment of multimodal biometrics and advanced algorithms not only improves the reliability of the process, but also reduces false positives, which is crucial in the management of sensitive data. The combination of various biometric features, such as facial recognition and physiological signal analysis, has proven to be effective even in medical settings