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*

Lung cancer is a leading cause of cancer-related deaths worldwide, with its high mortality rate primarily attributed to delayed diagnosis. Radiomics, by extracting abundant quantitative features from medical images, offers novel possibilities for early diagnosis and precise treatment of lung cancer. This article reviewed the latest advancements in radiomics for lung cancer management, particularly its integration with artificial intelligence (AI) to optimize diagnostic processes and personalize treatment strategies. Despite existing challenges, such as non-standardized image acquisition parameters and limitations in model reproducibility, the incorporation of AI significantly enhanced the precision and efficiency of image analysis, thereby improving the prediction of disease progression and the formulation of treatment plans. We emphasized the critical importance of standardizing image acquisition parameters and discussed the role of AI in advancing the clinical application of radiomics, alongside future research directions.
Humans ; Lung Neoplasms/diagnosis* ; Artificial Intelligence ; Early Detection of Cancer/methods* ; Precision Medicine ; Image Processing, Computer-Assisted/methods* ; Tomography, X-Ray Computed ; Radiomics

OBJECTIVE: To compare the biomechanical properties of personalized Y-shaped plates with horizontal plates, vertical plates, and traditional Y-shaped plates in the treatment of distal humeral intra-articular fractures through finite element analysis, and to evaluate their potential for clinical application. METHODS: The study selected a 38-year-old male volunteer and obtained a three-dimensional model of the humerus by scanning his upper limbs using a 64-slice spiral CT. Four types of fracture-internal fixation models were constructed using Mimics 19.0, Geomagic Wrap 2017, Creo 6.0, and other software: horizontal plates, vertical plates, traditional Y-shaped plate, and personalized Y-shaped plate. The models were then meshed using Hypermesh 14.0 software, and material properties and boundary conditions were defined in Abaqus 6.14 software. AnyBody 7.3 software was used to simulate elbow flexion and extension movements, calculate muscle strength, joint forces, and load torques, and compare the peak stress and maximum displacement of the four fixation methods at different motion angles (10°, 30°, 50°, 70°, 90°, 110°, 130°, 150°) during elbow flexion and extension. RESULTS: Under dynamic loading during elbow flexion and extension, the personalized Y-shaped plate exhibits significant biomechanical advantages. During elbow flexion, the peak internal fixation stress of the personalized Y-shaped plate was (28.8±0.9) MPa, which was significantly lower than that of the horizontal plates, vertical plates, and traditional Y-shaped plate ( P<0.05). During elbow extension, the peak internal fixation stress of the personalized Y-shaped plate was (18.1±1.6) MPa, which was lower than those of the other three models, with significant differences when compared with horizontal plates and vertical plates ( P<0.05). Regarding the peak humeral stress, the personalized Y-shaped plate model showed mean values of (10.9±0.8) and (13.1±1.4) MPa during elbow flexion and extension, respectively, which were significantly lower than those of the other three models ( P<0.05). Displacement analysis showed that the maximum displacement of the humerus with the personalized Y-shaped plate during elbow flexion was (2.03±0.08) mm, slightly higher than that of the horizontal plates, but significantly lower than that of the vertical plates, showing significant differences ( P<0.05). During elbow extension, the maximum displacement of the humerus with the personalized Y-shaped plate was (1.93±0.13) mm, which was lower than that of the other three models, with significant differences when compared with vertical plates and traditional Y-shaped plates ( P<0.05). Stress contour analysis showed that the stress of the personalized Y-shaped plate was primarily concentrated at the bifurcation of the Y-shaped structure. Displacement contour analysis showed that the personalized Y-shaped plate effectively controlled the displacement of the distal humerus during both flexion and extension, demonstrating excellent stability. CONCLUSION The personalized Y-shaped plate demonstrates excellent biomechanical performance in the treatment of distal humeral intra-articular fractures, with lower stress and displacement, providing more stable fixation effects.
Humans ; Male ; Adult ; Healthy Volunteers ; Finite Element Analysis ; Tomography, Spiral Computed ; Models, Anatomic ; Biomechanical Phenomena ; Humeral Fractures, Distal/surgery* ; Fracture Fixation, Internal/instrumentation* ; Bone Plates ; Computer Simulation ; Precision Medicine/methods* ; Elbow Joint/surgery* ; Elbow/surgery* ; Humerus/surgery* ; Torque ; Stress, Mechanical ; Intra-Articular Fractures/surgery* ; Prosthesis Design/methods* ; Imaging, Three-Dimensional ; Range of Motion, Articular

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