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

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*

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

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