4th International Conference on Primary Health Care & 2nd Euro Nursing Congress
September 15-16 2025 | Virtual Event
Zahid Hasan
Charter University NCBA&E Main Campus Lahore, Pakistan
The rising global burden of chronic diseases demands innovative diagnostic solutions to ensure
early detection, effective monitoring, and personalized treatment. Traditional diagnostic
processes are often limited by errors and delays, leading to adverse patient outcomes. With
the rapid growth of digital health data including diagnoses, treatments, and medications machine
learning (ML) technologies provide new opportunities to enhance healthcare accuracy
and accessibility. This research presents an intelligent cognitive diagnostic system for telemedicine,
designed to improve the early diagnosis and monitoring of chronic illnesses. The
framework integrates Cognitive Medical Robotic Technology, the Internet of Medical Things
(IoMT), and wearable devices for real-time patient data collection, combined with advanced
ML algorithms such as Artificial Neural Networks (ANN). The system emulates human cognitive
skills, enabling perception, contextual understanding, and decision-making to deliver
accurate diagnoses and personalized treatment recommendations. Results indicate that this
approach surpasses traditional methods in precision, efficiency, and patient satisfaction, offering
a transformative step forward in telemedicine.
Zahid Hasan is Associate Professor in the Department of Computer Science and Director of Advanced Study &
Research (DASAR) at NCBA&E, Pakistan. He earned his PhD in Computer Science and has published more than
25 papers in reputed journals. He also serves on the editorial boards of several academic institutes and continues
to contribute actively to research in cognitive robotics, telemedicine, and artificial intelligence.