Application of Artificial Intelligence in Medical Diagnosis and Patient Monitoring at Gitwe Hospital, Ruhango District, Southern Province
DOI:
https://doi.org/10.65150/EP-gjetr/V2E5/2026-02Keywords:
Artificial Intelligence, Medical Diagnosis, Patient Monitoring.Abstract
Artificial Intelligence (AI) has become a transformative technology in healthcare worldwide, offering tools to improve medical diagnosis and patient monitoring. In the context of the of Gitwe Hospital , integrating AI can enhance the accuracy of diagnoses, reduce human errors, and provide continuous monitoring of patients, leading to better health outcomes, and efficient hospital activities. In this study the researcher used quantitative and qualitative descriptive research approach. Data were collected from patient records, diagnostic reports, and monitoring logs at Gitwe Hospital. Gitwe Hospital AI tools, including machine learning algorithms and predictive models were applied to analyze patterns in patients’ data. The methodology involved in our research was evaluating AI-assisted diagnostic tools against conventional methods and assessing their impact on treatment, and procedures used in Gitwe Hospital patients’ monitoring efficiency. The implementation of AI in medical diagnosis improved the accuracy and speed of identifying diseases, particularly chronic conditions and early-stage infections. Patients’ monitoring systems using AI enabled real-time tracking of vital signs, alerting medical staff to critical changes promptly. The study showed that AI integration reduced diagnostic errors by approximately 15% and improved patient response time in emergencies by 20%. The application of AI in medical diagnosis and patient monitoring at the Hospital of Gitwe demonstrated significant potential to enhance healthcare delivery. AI improved diagnostic accuracy, enabled proactive patient care, and supports clinical decision-making. Future integration of AI technologies can further optimize hospital management and patient outcomes, making the healthcare systems more responsive and efficient.
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Copyright (c) 2026 Philippe Hakizimana , Aime Fidele Ndayiragije Mvuyekure , Evariste Rukundo (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.











