生物医学领域大模型如何推动科研与临床应用
Foundation Model in Biomedicine
- 基于海量无标注数据预训练，可通用迁移至多种生物医学任务
- 在药物研发、医学影像等领域表现优于传统专用模型
- 适合关注AI+医疗融合的科研人员与开发者参考
基础模型最早于2021年提出，指通过无监督方法从大规模未标注数据中学习的大型预训练模型（如大语言模型LLMs和视觉语言模型VLMs），可在多种下游任务中表现出色。这类模型如GPT可适配问答、视觉理解等应用，因其跨领域广泛适用性而得名。生物医学基础模型的发展标志着人工智能在理解复杂生物学现象及推动医学研究与实践中的重要进展。本综述探讨了基础模型在计算生物学、药物发现与开发、临床信息学、医学影像及公共卫生等领域的潜力，旨在激发健康科学中基础模型应用的持续研究。
原文摘要 · Abstract (English)
Foundation models, first introduced in 2021, refer to large-scale pretrained models (e.g., large language models (LLMs) and vision-language models (VLMs)) that learn from extensive unlabeled datasets through unsupervised methods, enabling them to excel in diverse downstream tasks. These models, like GPT, can be adapted to various applications such as question answering and visual understanding, outperforming task-specific AI models and earning their name due to broad applicability across fields. The development of biomedical foundation models marks a significant milestone in the use of artificial intelligence (AI) to understand complex biological phenomena and advance medical research and practice. This survey explores the potential of foundation models in diverse domains within biomedical fields, including computational biology, drug discovery and development, clinical informatics, medical imaging, and public health. The purpose of this survey is to inspire ongoing research in the application of foundation models to health science.
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