Jinle Tang

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About

Jinle Tang is a leading researcher at the forefront of artificial intelligence in medicine, with a primary focus on developing robust, data-driven frameworks for clinical decision-making. Their most cited work, "Artificial intelligence for medicine 2025: Navigating the endless frontier" (2025, 21 citations), provides a visionary roadmap for integrating diverse clinical data sources—including pathology, medical imaging, physiological signals, and omics—into reliable AI systems. Tang’s major contribution lies in emphasizing the critical importance of data quality and rigorous standards for successful AI deployment in healthcare, addressing a fundamental bottleneck in the field. By synthesizing complex multimodal information, their research paves the way for more accurate diagnostics and personalized treatment strategies. With growing recognition for their forward-looking perspective, Tang is shaping the next generation of medical AI, ensuring that technological advances translate into tangible clinical benefits. Their work stands as an essential reference for students and researchers navigating the intersection of artificial intelligence and evidence-based medicine.