Sobia Rafiq

Islamic University of Science and Technology

Papers

Total Citations

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About

Sobia Rafiq is a pioneering researcher at the intersection of artificial intelligence and healthcare, with a primary focus on deep reinforcement learning (DRL) and its transformative applications in medical science. Her most-cited work, "Deep Reinforcement Learning in Medical Science: Methods, Applications, and Future Directions" (2025), provides a comprehensive survey that bridges cutting-edge AI techniques with clinical challenges, offering a roadmap for intelligent decision-making in diagnostics, treatment planning, and personalized medicine. Though early in her citation trajectory, this paper has already garnered attention for its forward-looking synthesis of DRL algorithms—such as Q-learning and policy gradients—and their potential to optimize complex medical workflows, from drug discovery to robotic surgery. Rafiq’s contributions stand out for their clarity in demystifying advanced computational methods for a biomedical audience, making her a vital voice in the push toward autonomous, data-driven healthcare. Her work not only catalogs current innovations but also identifies critical gaps, such as model interpretability and safety, positioning her as a thought leader in responsible AI deployment. As her research gains traction, Rafiq is poised to shape how reinforcement learning revolutionizes patient outcomes, with her 2025 paper serving as a foundational reference for students and practitioners alike.