Biliary tract cancer (BTC) is a rare group of malignancies that develop from the epithelial lining of the biliary tree and have a poor prognosis. Although chemotherapy is the standard of care for patients with advanced BTC in China, its clinical benefits are moderate. In recent years, the approval of targeted therapies and immunotherapies has provided new avenues for the management of advanced BTC. Nonetheless, the increasing number of personalized medicine approaches has created a challenge for clinicians choosing individualized treatment strategies based on tumor characteristics. In this article, we discuss recent progress in implementing precision medicine approaches for advanced BTC in China and examine genomic profiling studies in Chinese patients with advanced BTC. We also discuss the challenges and opportunities of using precision medicine approaches, as well as the importance of considering population-specific factors and tailoring treatment approaches to improve outcomes for patients with BTC. In addition to providing a comprehensive overview of current and emerging precision medicine approaches for the management of advanced BTC in China, this review article will support clinicians outside of China by serving as a reference regarding the role of patient- and population-specific factors in clinical decision-making for patients with this rare malignancy.
Humans ; Precision Medicine/methods* ; Biliary Tract Neoplasms/genetics* ; China ; Molecular Targeted Therapy ; Immunotherapy/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

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*

This perspective review explores the transformative potential of personalized herbal medicine, examines the integration of ancient herbal knowledge with modern personalized medicine, delves into the principles of personalized medicine particularly in the context of herbal treatments, and investigates the principles of personalized medicine and elucidates how they are being applied to herbal medicine. It emphasizes the individualized nature of this approach and how it is facilitated through genetic analysis and health profiling. This review also highlights key advancements in herbal medicine, such as deoxyribonucleic acid (DNA) analysis and bioinformatics, and their role in the development of precise and personalized herbal remedies. The outcomes of personalized herbal medicine reveal how genetic variations are being considered to tailor treatments, create target-specific therapies, and customize dosage regimens. Furthermore, this review illustrates the evolution of herbal medicine with technological advancements, particularly DNA analysis and bioinformatics, to enhance precision and personalization. The challenge associated with implementing personalized herbal medicine more broadly includes issues of accessibility, regulation, education and ethics. It underscores the transformative potential of personalized herbal medicine. It calls for continued exploration, research and collaboration in this burgeoning field. This emerging field encourages researchers, practitioners, and stakeholders to engage in advancing healthcare practices that are increasingly personalized, evidence-based, and centered on patient's needs. Please cite this article as: Aslam MS, Kim YJ. Herbal medicine in the modern age: The era of personalized precision. J Integr Med. 2025; 23(6):591-604.
Precision Medicine ; Humans ; Herbal Medicine/methods* ; Phytotherapy

Artificial intelligence (AI) is rapidly advancing in periodontology, bringing new opportunities to clinical diagnosis, risk assessment, personalized treatment planning, and remote patient care. Leveraging core technologies such as deep learning, machine learning, and natural language processing, AI significantly enhances the sensitivity of early periodontal disease detection and provides precise quantification of alveolar bone loss and soft tissue damage. AI facilitates multimodal data integration by synthesizing medical history, lifestyle factors, and imaging data, thereby offering enhanced accurate risk prediction and personalized therapeutic recommendations. By integrating remote monitoring with tailored health counseling, AI helps patients maintain adherence to self-care protocols, significantly improving their oral health-related quality of life and treatment satisfaction. Moreover, AI demonstrates considerable potential in periodontal research and education, particularly in large-scale data mining, virtual clinical case simulations, and natural language processing-assisted literature management. Nevertheless, challenges remain concerning model generalizability, data quality, ethical concerns, and interpretability. The advancement of multi-center big-data platforms is expected to foster a profound integration of AI and periodontology, propelling precision medicine and digital healthcare, enabling holistic management from prevention to long-term care, and enhancing diagnostic efficiency and patient health outcomes.
Humans ; Artificial Intelligence ; Periodontics/methods* ; Periodontal Diseases/therapy* ; Deep Learning ; Precision Medicine ; Quality of Life

The incidence of thyroid cancer keeps rising globally,with the majority being papillary thyroid carcinoma (PTC),which has a favorable prognosis.However,some aggressive PTCs exhibit different clinical behaviors and higher mortality risks,with the growth rate surpassing that of well-differentiated PTC and undifferentiated cancers.Therefore,achieving precise diagnosis and treatment of thyroid carcinoma presents a significant challenge.Photoacoustic imaging is a molecular imaging technology that integrates optical imaging and ultrasound,providing imaging information on structure,function,and molecules.Moreover,it can utilize exogenous contrast agents to realize tumor treatment,such as photothermal therapy,serving as a promising technology for precise diagnosis and treatment of thyroid carcinoma.Researchers both domestically and internationally have explored the application of photoacoustic imaging in the precise diagnosis and treatment of thyroid tumors.This article reviews the research progress,elucidates the advantages and limitations of photoacoustic imaging in the diagnosis and treatment of thyroid carcinoma,and prospects on the precise diagnosis and treatment of this disease.
Humans ; Thyroid Neoplasms/diagnostic imaging* ; Photoacoustic Techniques/methods* ; Precision Medicine

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*