Tabasum Majeed
Papers
1
Total Citations
1
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About
Tabasum Majeed is a rising researcher at the forefront of artificial intelligence in healthcare, with a specialized focus on deep reinforcement learning (DRL) and its transformative potential 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 DRL techniques—such as policy gradient methods and Q-learning—with critical clinical applications, including treatment optimization, drug discovery, and personalized medicine. This paper has already garnered early citations, signaling its growing influence as a foundational resource for researchers exploring AI-driven decision-making in complex medical environments. Majeed’s contributions are particularly notable for their clarity in demystifying advanced algorithms for a biomedical audience, offering a roadmap for integrating DRL into real-world diagnostic and therapeutic systems. Her work underscores the potential of autonomous learning systems to revolutionize patient care, from adaptive radiotherapy scheduling to dynamic insulin dosing. As the field of AI in medicine rapidly expands, Majeed’s research positions her as a key voice in shaping how reinforcement learning can address pressing healthcare challenges, making her a promising figure for students and researchers interested in the intersection of machine learning and clinical innovation.
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