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Document Type

Presentation

Publication Date

5-4-2026

Keywords

Artificial Intelligence, Biomedicine, Neuroscience, Machine Learning, Clinical AI, Biomedical Research, Shadow Learning, Computer Vision, Healthcare AI

Disciplines

Engineering | Medicine and Health Sciences | Physical Sciences and Mathematics

Abstract

This presentation explores the role of artificial intelligence in advancing biomedical research and clinical practice from the perspective of a physician-scientist. Dr. Shiqian Shen discusses current challenges in neuroscience and medicine, including limitations in human observation, data analysis, and decision-making across complex biological systems. The talk highlights how AI-driven approaches such as computer vision, neural signal processing, and large-scale data modeling can improve the understanding of disease mechanisms, enhance experimental workflows, and enable more precise, patient-centered care. Specific applications include automated classification of neural activity, behavioral analysis in animal models, and multimodal data integration. A key concept introduced is “shadow learning,” a framework in which AI systems learn from real-world expert workflows rather than only structured outputs, capturing tacit knowledge and improving transparency and auditability. The presentation also discusses future directions for AI-assisted scientific discovery and its potential to transform both laboratory research and clinical environments.

Comments

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Rights

© 2026 Shiqian Shen

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