Systematic Review on AI-Based Diagnosis and Appointment Management in Under-Digitalized Healthcare (#1052)

Authors

Meneses Ayala, Adrian Yul

Quispe Mamani, Javier Enrique

Rada Mota, Luis Carmelo

Abstract

This systematic literature review (SLR) aims to analyze the impact of the use of intelligent systems in preliminary diagnosis and appointment management in healthcare settings with low technological adoption. The PICOC method and the PRISMA protocol were applied, selecting 26 relevant studies from an initial total of 658 articles. The results show that AI significantly improves diagnostic accuracy, optimizes appointment scheduling, and promotes equity in access to care, even in settings with limited infrastructure. Six key types of solutions were identified, including predictive models, automated triage systems, and conversational assistants, which are more efficient and adaptable than traditional methods. The analyzed technologies have been shown to reduce bias, accelerate clinical processes, and expand access in rural or low-tech areas. In conclusion, this SLR validates the transformative potential of AI in decentralized healthcare, provided it is implemented with ethical criteria, contextual governance, and adequate staff training.