Yichen Yang, Hongru Shen, Kexin Chen, Xiangchun Li
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引用次数: 0
Abstract
Deep learning has revolutionized cancer diagnostics, shifting from pixel-based image analysis to more comprehensive, patient-centric care. This opinion article explores recent advancements in neural network architectures, highlighting their evolution in biomedical research and their impact on medical imaging interpretation and multimodal data integration. We emphasize the need for domain-specific artificial intelligence (AI) systems capable of handling complex clinical tasks, advocating for the development of multimodal large language models that can integrate diverse data sources. These models have the potential to significantly enhance the precision and efficiency of cancer diagnostics, transforming AI from a supplementary tool into a core component of clinical decision-making, ultimately improving patient outcomes and advancing cancer care.
期刊介绍：
Trends in Molecular Medicine (TMM) aims to offer concise and contextualized perspectives on the latest research advancing biomedical science toward better diagnosis, treatment, and prevention of human diseases. It focuses on research at the intersection of basic biology and clinical research, covering new concepts in human biology and pathology with clear implications for diagnostics and therapy. TMM reviews bridge the gap between bench and bedside, discussing research from preclinical studies to patient-enrolled trials. The major themes include disease mechanisms, tools and technologies, diagnostics, and therapeutics, with a preference for articles relevant to multiple themes. TMM serves as a platform for discussion, pushing traditional boundaries and fostering collaboration between scientists and clinicians. The journal seeks to publish provocative and authoritative articles that are also accessible to a broad audience, inspiring new directions in molecular medicine to enhance human health.
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