Body-Donor-Derived Data in Medical Artificial Intelligence: From Foundational Resources to Trustworthy Applications.

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Abstract

In recent years, artificial intelligence in medicine has evolved from single recognition tasks toward structural understanding, spatial reasoning, and clinical interpretability. High-quality anatomical data have become a key factor in further development. Driven by digital tomography, three-dimensional reconstruction, and multimodal technologies, body-donor-derived specimens and digital anatomical datasets, characterized by clear structural boundaries, stable spatial relationships, and fine-grained detail, are being transformed into computable, annotatable, and reusable digital anatomical resources. These resources are playing an increasingly important role in medical artificial intelligence. This narrative review summarizes the multiple roles of body-donor-derived data in medical AI. They serve as foundational resources that provide high-fidelity training data and fine-grained annotation systems. They also serve as validation references for improving algorithm credibility. In addition, they act as a substrate for AI-driven transformation in data processing, three-dimensional modeling, and intelligent applications in education, clinical practice, and forensic medicine. Their main strengths lie in anatomical authenticity, fine-grained annotatability, and structural validation utility, while their limitations include sample size, the postmortem-in vivo domain gap, annotation cost, and data governance. In the future, body-donor-derived data should become a core foundation for anatomical priors and structural gold standards, and should be deeply integrated with large-scale clinical imaging, multimodal intelligent analysis, and cross-domain learning to support the development of medical AI from high performance toward higher credibility and translational value.

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