Clinical management of Kawasaki disease faces several challenges, including difficulties in early diagnosis, insufficient personalized treatment, delayed access to information, and inefficient multidisciplinary collaboration. This paper explores the application of the DeepSeek AI model in the management of Kawasaki disease: (1) Enhancing early diagnosis accuracy through the integration and analysis of multimodal data (imaging, laboratory, and clinical data); (2) Dynamically adjusting treatment plans to achieve personalized medicine; (3) Integrating the latest global guidelines and research findings in real-time to optimize clinical processes; (4) Providing personalized health education content to enhance parental involvement; (5) Establishing a platform for sharing clinical data to support intelligent decision-making and multidisciplinary collaboration.
Humans ; Mucocutaneous Lymph Node Syndrome/diagnosis* ; Child ; Artificial Intelligence ; Precision Medicine ; East Asian People

Pulmonary large cell neuroendocrine carcinoma (LCNEC) is a high-grade neuroendocrine tumor with unique characteristics, and its treatment regimens are primarily derived from those for small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). In recent years, the incidence rate has been on the rise, and the prognosis are affected by the interaction of multiple factors such as individual, clinical stage and treatment mode, and the heterogeneity is significant. In the study of molecular subtypes, multiple subgroups were divided according to key gene mutations such as RB1 and TP53, and genomic subtypes were associated with survival, chemotherapy response, and efficacy of precision therapy. Targeted therapy excavates multiple targets, and the efficacy of drugs is different. Immunotherapy has made remarkable progress, and immune checkpoint inhibitors (ICIs) have been effective in all stages of chemotherapy alone or in combination with chemotherapy or radiation therapy, but there is a risk of hyperprogressive diseases, and accurate prognostic markers need to be explored urgently. This review reviews the latest research progress in the study of molecular subtypes and precision therapies such as targeted therapy and immunotherapy of pulmonary LCNEC, and points out that pulmonary LCNEC treatment will develop in the direction of precision and individualization in the future.
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Humans ; Lung Neoplasms/drug therapy* ; Carcinoma, Neuroendocrine/drug therapy* ; Precision Medicine ; Immunotherapy ; Carcinoma, Large Cell/drug therapy*

Gastric cancer is a high-incidence malignancy that poses a serious threat to public health in China, ranking among the top three cancers in both incidence and mortality. The majority of patients are diagnosed at an advanced stage, resulting in limited treatment options and poor prognosis. To address key challenges in gastric cancer diagnosis and treatment, a research team led by Professor Jiafu Ji at Peking University Cancer Hospital has focused on the project "Development and Dissemination of Precision Medicine Approaches in Gastric Cancer Management". Through a series of high-quality multicenter clinical studies, the team established a set of new international standards in perioperative treatment, individua-lized drug selection, intelligent noninvasive diagnostics, and novel immunotherapy strategies. These advances have significantly improved treatment efficacy and reduced surgical trauma, achieving key technological breakthroughs in diagnosis, therapy, and mechanistic understanding, and systematically enhancing outcomes for gastric cancer patients. The project ' s findings had a broad international impact, including hosting China ' s first International Gastric Cancer Congress. Through nationwide dissemination, they have promoted the development of precision diagnosis and treatment of gastric cancer as a discipline, and led the formulation of the National Health Commission's guidelines for gastric cancer diagnosis and treatment. In recognition of its achievements, the project was awarded the First Prize of the 2024 Chinese Medical Science and Technology Award.
Stomach Neoplasms/genetics* ; Humans ; Precision Medicine/methods* ; China ; Immunotherapy/methods*

Prostate cancer is the second most common cancer in men, accounting for 14.1% of new cancer cases in 2020. The aggressiveness of prostate cancer is highly variable, depending on its grade and stage at the time of diagnosis. Despite recent advances in prostate cancer treatment, some patients still experience recurrence or even progression after undergoing radical treatment. Accurate initial staging and monitoring for recurrence determine patient management, which in turn affect patient prognosis and survival. Classical imaging has limitations in the diagnosis and treatment of prostate cancer, but the use of novel molecular probes has improved the detection rate, specificity, and accuracy of prostate cancer detection. Molecular probe-based imaging modalities allow the visualization and quantitative measurement of biological processes at the molecular and cellular levels in living systems. An increased understanding of tumor biology of prostate cancer and the discovery of new tumor biomarkers have allowed the exploration of additional molecular probe targets. The development of novel ligands and advances in nano-based delivery technologies have accelerated the research and development of molecular probes. Here, we summarize the use of molecular probes in positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), optical imaging, and ultrasound imaging, and provide a brief overview of important target molecules in prostate cancer.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Molecular Probes ; Molecular Imaging/methods* ; Magnetic Resonance Imaging ; Positron-Emission Tomography ; Tomography, Emission-Computed, Single-Photon ; Ultrasonography ; Optical Imaging ; Biomarkers, Tumor ; Precision Medicine/methods*

Traditional Chinese medicine (TCM) represents a paradigmatic approach to personalized medicine, developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years, and now encompasses large-scale electronic medical records (EMR) and experimental molecular data. Artificial intelligence (AI) has demonstrated its utility in medicine through the development of various expert systems (e.g., MYCIN) since the 1970s. With the emergence of deep learning and large language models (LLMs), AI's potential in medicine shows considerable promise. Consequently, the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction. This survey provides an insightful overview of TCM AI research, summarizing related research tasks from three perspectives: systems-level biological mechanism elucidation, real-world clinical evidence inference, and personalized clinical decision support. The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice. To critically assess the current state of the field, this work identifies major challenges and opportunities that constrain the development of robust research capabilities-particularly in the mechanistic understanding of TCM syndromes and herbal formulations, novel drug discovery, and the delivery of high-quality, patient-centered clinical care. The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality, large-scale data repositories; the construction of comprehensive and domain-specific knowledge graphs (KGs); deeper insights into the biological mechanisms underpinning clinical efficacy; rigorous causal inference frameworks; and intelligent, personalized decision support systems.
Medicine, Chinese Traditional/methods* ; Artificial Intelligence ; Humans ; Precision Medicine ; Decision Support Systems, Clinical

Nasopharyngeal carcinoma（NPC） is a distinct type of head and neck cancer closely associated with Epstein-Barr virus（EBV） infection and exhibits significant geographic variations in its incidence. Despite recent advancements in radiotherapy techniques and precision medicine for NPC, the overall survival rate remains unsatisfactory due to tumor metastasis, recurrence, and drug resistance. Single-cell RNA sequencing（scRNA-seq） is an emerging technology that allows for the analysis of gene expression at single-cell resolution, providing a clearer understanding of tumor cell subpopulations, the evolutionary trajectory of tumor cells, and the functional roles and interactions of cells within the tumor microenvironment. This provides new ideas for the development of precision medicine in NPC. Here, we review the applications of scRNA-seq in exploring the mechanisms of NPC pathogenesis, tumor heterogeneity, the tumor microenvironment, drug resistance, and therapeutic response.
Humans ; Nasopharyngeal Neoplasms/genetics* ; Tumor Microenvironment ; Nasopharyngeal Carcinoma ; Single-Cell Analysis ; Sequence Analysis, RNA ; Precision Medicine ; Drug Resistance, Neoplasm ; Epstein-Barr Virus Infections ; Herpesvirus 4, Human ; Single-Cell Gene Expression Analysis

With advances of drug design and preparation technology, the development of long-acting drugs has become an important research focus in precision medicine and chronic disease management. These drugs are designed to improve the patients' compliance and quality of life by achieving prolonged maintenance of an effective drug concentration in the body with a reduced dosing frequency. Small molecule drugs, monoclonal antibodies and nucleic acid drugs all have their own difficulties in achieving long actions, which can be especially challenging for the latter two because of their structural complexity. This review provides an overview of the strategies for designing long-acting small molecule drugs, monoclonal antibodies, and nucleic acid drugs.
Humans ; Drug Design ; Antibodies, Monoclonal/chemistry* ; Nucleic Acids ; Precision Medicine ; Delayed-Action Preparations

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*

Neoantigens exhibit high immunogenic potential and confer a uniqueness to tumor cells, making them ideal targets for personalized cancer immunotherapy. Neoantigens originate from tumor-specific genetic alterations, abnormal viral infections, or other biological mechanisms, including atypical RNA splicing events and post-translational modifications (PTMs). These neoantigens are recognized as foreign by the immune system, eliciting an immune response that largely bypasses conventional mechanisms of central and peripheral tolerance. Advances in next-generation sequencing (NGS), mass spectrometry (MS), and artificial intelligence (AI) have greatly expedited the rapid detection and forecasting of neoantigens, markedly propelling the development of diverse immunotherapeutic strategies, including cancer vaccines, adoptive cell therapy, and antibody treatment. In this review, we comprehensively explore the discovery and characterization of neoantigens and their clinical use within promising immunotherapeutic frameworks. Additionally, we address the current landscape of neoantigen research, the intrinsic challenges of the field, and potential pathways for clinical application in cancer treatment.
Humans ; Neoplasms/therapy* ; Precision Medicine/methods* ; Immunotherapy/methods* ; Antigens, Neoplasm/genetics* ; Cancer Vaccines/immunology* ; High-Throughput Nucleotide Sequencing

With the rapid development of artificial intelligence, computational pathology has been seamlessly integrated into the entire clinical workflow, which encompasses diagnosis, treatment, prognosis, and biomarker discovery. This integration has significantly enhanced clinical accuracy and efficiency while reducing the workload for clinicians. Traditionally, research in this field has depended on the collection and labeling of large datasets for specific tasks, followed by the development of task-specific computational pathology models. However, this approach is labor intensive and does not scale efficiently for open-set identification or rare diseases. Given the diversity of clinical tasks, training individual models from scratch to address the whole spectrum of clinical tasks in the pathology workflow is impractical, which highlights the urgent need to transition from task-specific models to foundation models (FMs). In recent years, pathological FMs have proliferated. These FMs can be classified into three categories, namely, pathology image FMs, pathology image-text FMs, and pathology image-gene FMs, each of which results in distinct functionalities and application scenarios. This review provides an overview of the latest research advancements in pathological FMs, with a particular emphasis on their applications in oncology. The key challenges and opportunities presented by pathological FMs in precision oncology are also explored.
Humans ; Precision Medicine/methods* ; Medical Oncology/methods* ; Artificial Intelligence ; Neoplasms/pathology* ; Computational Biology/methods*