| Abstract |
Healthcare systems face increasing pressure as patient demand rises and incomplete records contribute to delays and repeated tests. The purpose of this project was to investigate how a structured, technology-driven platform could improve proactive personal health management, support patient and clinician communication, and improve consultation efficiency, without providing medical diagnoses.
MedSync+ integrated symptom tracking via a 3D interactive body interface, medication tracking with reminders and an allergy log area, family history, condition images with local storage allowing the system to improve over time based on user input, and curated health references into a single, cohesive framework. Users could also upload blood test results, which were analysed using OCR-based AI models to extract values, detect patterns, and generate simplified, actionable insights.
Methods included a literature review on current AI in healthcare, regulatory research on GDPR and EU AI Act compliance, interface usability testing with volunteers across diverse age groups, and validation of AI models using synthetic datasets, accuracy metrics, and confusion matrices. Pre- and post-surveys guided iterative improvements to ensure accessibility and clarity, while clinical feedback informed design and ethical compliance.
Results demonstrated that structured health data combined with AI-driven pattern recognition simplified health tracking, highlighted patterns, and supported user-controlled information sharing with clinicians. These findings show that the project is feasible and effective in simulated and controlled scenarios, supporting the potential for real-world application. By centralising data, providing simplified insights, and enabling secure communication, MedSync+ demonstrates how research-driven digital tools can empower patients and reduce systemic pressures on healthcare systems.
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