Large-Language-Model (LLM) functionality is rapidly becoming a cornerstone of Telemedicine-as-a-Service (PGaaS) platforms. Recent Q1 studies demonstrate that even minuscule training-set or parameter perturbations can introduce persistent back-doors, while inference pipelines leak protected health information (PHI) if left unguarded. Building on the NIST AI Risk Management Framework (AI RMF), this paper proposes and implements a zero-trust, multi-cloud security architecture that couples (i) knowledge-graph–driven data-integrity validation, (ii) containerised fine-tuning isolation, (iii) AI-RMF–centred governance and continuous risk registers, (iv) a privacy-preserving response-sanitisation gateway enhanced with one-time-password (OTP) and KYC identity binding, and (v) remote-attestation-backed zero-knowledge-proof (ZKP) integrity challenges for model weights at runtime. An extensive multi-cloud evaluation shows that the framework detects 94.6 % of tainted samples before ingestion and blocks 91.3 % of unsafe outputs, with a median latency overhead of 66 ms—well below clinical tele-consultation thresholds.

This study proposes a framework that leverages ML algorithms for the cybersecurity of telemedicine systems in the Department of Córdoba, Colombia, addressing challenges amplified by the COVID-19 pandemic. The framework emphasizes enhancing the processing capabilities and reliability in managing clinical information, particularly focusing on the RES. It integrates a PGaaS aligned with the OWASP guidelines, aiming to neutralize emerging threats using advanced IA models. The system incorporates data encryption, 2FA-based Access Control, continuous backups, code obfuscation, and watermarking, all supported by a suitable legal framework. These components are combined into a comprehensive monitoring and auditing scheme to ensure robust protection against intelligent intrusions and the compromise of medical data.

In this work, the use of virtual reality (VR) in the teaching of surgical and aseptic processes is described and analyzed. The work focuses on implementing a virtual environment as an educational resource to help students in the area of health in the understanding and assimilation of surgical and aseptic techniques. Augmented reality as a pedagogical strategy allows creating a space for autonomous and collaborative learning. Qualitative research was carried out, using a story guide to collect the experiences lived by the students when using the environment, and a closed survey was applied to assess the degree of learning. The results are promising, its implementation has shown to be a good tool for health training, it has also had a great reception by students, decreasing apathy, discouragement and lack of interest at the time of learning.