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AI and Intelligent Medicine Center is a key research center established by the Hangzhou Institute of Medicine. It collaborates closely with other centers—such as Intelligent Molecular Diagnostics and the Institute-BGI Group Innovation Drug Development Center—as well as various clinical, imaging, pathology, laboratory, drug clinical trial, and informatics departments of the affiliated Cancer Hospital. Leveraging the advantages of the National Regional Medical Center for Oncology, including its large-scale oncology data and biosample resources, and its concentration of talent in both Biotechnology (BT) and Artificial Intelligence (AI), the Center addresses key common challenges in secure healthcare data sharing and deep mining. Grounded in biomedical and multi-omics big data, the Center focuses on precision diagnosis/treatment and drug discovery for major diseases such as malignant tumors. It aims to build a Trusted Intelligent Open Operating System and an innovative collaborative research platform for biomedicine. The core R&D work includes developing foundation models, advancing AI algorithms and platforms, pursuing multimodal intelligent diagnosis and therapy, and promoting end-to-end intelligent drug discovery. These efforts are dedicated to driving breakthrough progress in applying cutting-edge AI technologies to the biomedical field.

AI Algorithms and Foundation Models

We explore and apply cutting-edge artificial intelligence algorithms and trustworthy AI platform technologies. Our mission is to build foundational models and platforms – including Large Language Models (LLMs), Multimodal Generative AI, and Generative Agents – and to securely integrate distributed, large-scale biomedical data from multiple sources. This work establishes a trustworthy AI infrastructur.

Multimodal Intelligent Diagnosis and Treatment

We conduct research and development on intelligent clinical decision-support algorithms and platforms encompassing the entire lifecycle of smart medical practices. By leveraging the latest AI technologies—including deep learning, reinforcement learning, natural language processing (NLP), computer vision (CV), and knowledge graphs—we investigate core technologies for multimodal data fusion. Our work aims to enhance the accuracy of multi-dimensional data reconstruction and build integrated analysis models that synthesize imaging, pathology, clinical data, and biomolecular information (such as genomics, proteomics, and single-cell data). Furthermore, we are developing a foundational model for medicine to comprehensively support clinical intelligence. This initiative involves creating algorithms and platforms for intelligent diagnosis, proteomics, multimodal and multi-omics analysis, AI-powered early cancer screening, liquid biopsy, digital pathology, drug clinical prognosis, companion diagnostics, and clinical decision support.ce. Our current key focus areas include foundational models, deep learning, reinforcement learning, Natural Language Processing (NLP), Computer Vision (CV), Knowledge Graphs (KG), search, recommendation systems, federated learning, large-scale deep learning cloud-native platforms, and a trustworthy AI operating system.

End-to-End Intelligent Drug Discovery Leveraging cutting-edge artificial intelligence technologies—particularly deep learning, reinforcement learning, generative AI, and foundation models—we conduct innovative drug discovery research. This research encompasses target identification, structure design, intelligent generation, high-throughput and virtual screening, efficacy and toxicity analysis, patient candidate selection, and prognosis prediction. By integrating findings from animal and clinical studies, we establish an intelligent analysis and decision-making feedback loop that bridges dry and wet lab experiments. We are developing a large-scale virtual drug screening platform and a full-process, self-feedback, closed-loop intelligent system to advance the digitalization of drug discovery. This effort aims to accelerate the development of domestically produced innovative drugs for high-challenge diseases, such as highly lethal and rare cancers. Our current focus lies in designing small molecule drugs, functional nucleic acids (including aptamers, nucleic acid vaccines, and drugs), and protein antibodies.

Multimodal Intelligent Diagnosis and Treatment

We conduct research and development on intelligent clinical decision-support algorithms and platforms encompassing the entire lifecycle of smart medical practices. By leveraging the latest AI technologies—including deep learning, reinforcement learning, natural language processing (NLP), computer vision (CV), and knowledge graphs—we investigate core technologies for multimodal data fusion. Our work aims to enhance the accuracy of multi-dimensional data reconstruction and build integrated analysis models that synthesize imaging, pathology, clinical data, and biomolecular information (such as genomics, proteomics, and single-cell data). Furthermore, we are developing a foundational model for medicine to comprehensively support clinical intelligence. This initiative involves creating algorithms and platforms for intelligent diagnosis, proteomics, multimodal and multi-omics analysis, AI-powered early cancer screening, liquid biopsy, digital pathology, drug clinical prognosis, companion diagnostics, and clinical decision support.

https://aimlab.cc/zh/

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