Uzair Iqbal is an AI researcher and educator specializing in medical imaging, federated learning, and healthcare applications. He holds a PhD in Computer Science from Universiti Malaya, Malaysia, and a Master’s and Bachelor’s degree in Software Engineering from UET Taxila. As an Assistant Professor at the National University of Computer and Emerging Sciences, he designs AI courses, supervises research projects, and secures funding for AI-driven healthcare solutions. He has published over 15 research papers in top journals like IEEE Access and Diagnostics (MDPI) and has presented at international conferences, including SPIE Medical Imaging 2024. His technical expertise includes AI platforms like TensorFlow and PyTorch, with a focus on decentralized AI and health informatics. A dedicated mentor, he has guided over 20 student projects and contributed to curriculum development. As an HEC-approved PhD supervisor and a frequent reviewer for esteemed journals, he plays a key role in advancing AI research and education.
Unravelling the Future of Health: Visual Language Model Agents for Biomedical Imaging in Edged Computing Environments
The unprecedented demand for rapid, accurate, and decentralized diagnostics has created a major impetus in biomedical imaging technology development. However, traditional cloud-based AI systems have many major deficiencies, such as latency, privacy issues, and bandwidth limitations in actual healthcare environments. In this keynote, we discuss how to leverage the visual language model (VLM) agents in edge computing environments to cope with these challenges. By having multimodal AI features (image-text understanding) work with decentralized edge infrastructure, VLMs allow real-time analysis of medical images-including X-rays, MRIs, and histopathology slides-at the point of care.
This presentation takes inspiration from the ways in which lightweight, edge-optimized VLMs empower clinicians by providing on-demand diagnostic services, automated report generation, and context-aware retrieval of similar cases through natural language queries. It elaborates the technical innovations indispensable to the deployment of these systems, such as model compression techniques (pruning and quantization), federated learning for privacy preservation within institutions, and ethical frameworks to mitigate bias. A few case studies prove that it is possible, such as reducing turnaround times for radiology reports from limited resourced clinics and enabling collaborative telepathology without compromising data sovereignty across institutions.
Finally, the discussion ponders the future scenario, where VLMs at the edge will democratize high-tech diagnostics, particularly for remote or disaster-response applications, within the ambit of both ethics and regulation. Bridging AI, healthcare, and edge computing, this new paradigm promises to reinvent precision medicine, making intelligent imaging analysis ubiquitous, secure, and patient-centric.
Keywords: Visual Language Models (VLMs), Edge Computing, Biomedical Imaging, Decentralized AI, Real-Time Diagnostics, Healthcare Innovation.