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*

Humans ; Pre-Eclampsia/diagnosis* ; Pregnancy ; Female ; China ; Evidence-Based Medicine ; Risk Factors

Humans ; Heart Defects, Congenital/therapy* ; Child ; Evidence-Based Medicine ; Cardiac Surgical Procedures/methods* ; Cardiac Catheterization/methods*

Basket trial, as an innovative clinical trial design concept, marks the transformation of medical research from the traditional large-scale and single-disease treatment to the precise and individualized treatment. By gradually incorporating the Bayesian method during development, the trial design becomes more scientific and reasonable and increases its efficiency. The fundamental principle of the Bayesian method is the utilization of prior knowledge in conjunction with new observational data to dynamically update the posterior probability. This flexibility enhances the basket trial's capacity to effectively adapt to variations during the research process. Consequently, it enables researchers to dynamically adjust research strategies based on accumulated data and improve the predictive accuracy regarding treatment responses. In addition, the design concept of the basket trial aligns with the traditional Chinese medicine(TCM) principle of "homotherapy for heteropathy". The principle of "homotherapy for heteropathy" emphasizes that under certain conditions, different diseases may have the same treatment. Similarly, basket trials allow using a uniform trial design across multiple diseases, offering enhanced operational and significant practical value in the realm of TCM, particularly within the context of syndrome-based disease research. By introducing basket trials, the design of TCM clinical studies will be more scientific and yield higher-quality evidence. This study systematically categorized various Bayesian methods and models utilized in basket trials, evaluated their strengths and weaknesses, and identified their appropriate application contexts, so as to offer a practical guide for designing basket trials in the realm of TCM.
Bayes Theorem ; Humans ; Medicine, Chinese Traditional/methods* ; Research Design ; Clinical Trials as Topic/methods* ; Drugs, Chinese Herbal/therapeutic use*

The guidelines for clinical research on new drugs provide unified standards for drug developers, researchers, and regulatory authorities, playing a crucial role in new drug development. This article systematically reviews the evolution of guidelines for clinical research on new traditional Chinese medicine(TCM) drugs in China, with a focus on analyzing the current status of these guidelines and the problems that exist. It also provides interpretations of three important guidelines. The article points out that with the continuous emergence of new clinical trial design methods, development concepts, and tools, and under the background of the "three combinations" evidence evaluation system for new TCM drugs, it is imperative to revise existing guidelines, formulate new ones, and develop new tools for clinical efficacy evaluation. It is hoped that relevant departments will adopt an open attitude and work together to build a technical system of clinical research guidelines for new TCM drugs that aligns with the characteristics of TCM.
Humans ; Biomedical Research/trends* ; China ; Clinical Trials as Topic ; Drugs, Chinese Herbal/therapeutic use* ; Guidelines as Topic ; Medicine, Chinese Traditional/standards*

The effective implementation of clinical practice guideline(CPG), as a crucial vehicle of evidence-based medicine, plays a vital role in improving healthcare quality and patient safety. Currently, there remains a significant gap between the actual implementation outcomes of traditional Chinese medicine(TCM) guidelines and their intended objectives, which necessitates a systematic investigation into their influencing factors to optimize implementation strategies. This study aims to comprehensively identify the factors influencing CPG implementation in China, adapt the theoretical domains framework(TDF) to the local context, and integrate TCM-specific characteristics to provide recommendations for optimizing the development and implementation processes of TCM guidelines. Systematic search was conducted across multiple databases, including CNKI, Wanfang, VIP, SinoMed, PubMed, and EMbase, covering the period from each database's inception to March 2024. Qualitative and mixed-methods studies were included to examine factors affecting the implementation of clinical practice guidelines. The methodological quality of the included studies was assessed using the critical appraisal skills programme(CASP) tool. RESULTS:: were synthesized through framework analysis and thematic synthesis, and expert consensus was achieved via a structured consensus meeting. A total of 16 studies involving 2 388 participants were included with overall good methodological quality. Based on the TDF, 43 influencing factors across 14 domains were identified. The most critical factors included the quality of guideline evidence, training and academic conferences organized by hospitals and academic institutions to promote guideline adoption among medical staff, support from professional leaders for guideline implementation, the applicability and clarity of guideline recommendations, and material resources(supplies, funding, and facilities) required for implementation. Additionally, influencing factors of TCM guideline implementation were identified, including the distinctive advantages of TCM therapies, the applicability of syndrome differentiation, and the feasibility of TCM treatments. Based on these findings, it is recommended that TCM guideline development should incorporate these unique influencing factors to formulate high-quality, clear, and actionable recommendations. Following guideline publication, healthcare and academic institutions should strengthen training and dissemination efforts and ensure the availability of necessary implementation resources to facilitate the successful adoption of guidelines in clinical practice.
China ; Humans ; Practice Guidelines as Topic ; Medicine, Chinese Traditional/standards* ; Evidence-Based Medicine

This study applied the grading of recommendations assessment, development and evaluation(GRADE) system and the integrated evidence chain-based effectiveness evaluation of traditional Chinese medicine(Eff-iEC) to evaluate the evidence for 12 commonly used Chinese patent medicines for the treatment of osteoporosis, which are frequently recommended in guidelines or expert consensuses. The results showed that Xianling Gubao Capsules/Tablets were rated as C(low-level evidence) according to the GRADE system, and as BA~+B~+(intermediate evidence) according to the Eff-iEC system. Jintiange Capsules were rated as C(low-level evidence) by the GRADE system, and as AA~+B(high-level evidence) by the Eff-iEC system. Gushukang Granules/Capsules were rated as C(low-level evidence) by GRADE system, and as BA~+B~+(intermediate evidence) by Eff-iEC system. Zuogui Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Qianggu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B~+(high-level evidence) by Eff-iEC system. Zhuanggu Zhitong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BA~+B(intermediate evidence) by Eff-iEC system. Jingui Shenqi Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~+B(high-level evidence) by Eff-iEC system. Quanduzhong Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AD~+B~+(low-level evidence) by Eff-iEC system. Epimedium Total Flavones Capsules were rated as D(extremely low-level evidence) by GRADE system, and as AAB~+(high-level evidence) by Eff-iEC system. Yougui Pills were rated as D(extremely low-level evidence) by GRADE system, and as AA~(++)B~(+ )(high-level evidence) by Eff-iEC system. Qigu Capsules were rated as D(extremely low-level evidence) by GRADE system, and as BB~+B(intermediate evidence) by Eff-iEC system. Liuwei Dihuang Pills were rated as C(low-level evidence) by GRADE system, and as AA~(++)B~+(high-level evidence) by Eff-iEC system. Overall, the Eff-iEC system provides a more comprehensive assessment of the effectiveness evidence for traditional Chinese medicine(TCM) than the GRADE system. However, it still has certain limitations that hinder its wider promotion and application. In terms of clinical evidence evaluation, both the Eff-iEC and GRADE systems reflect that the current clinical research quality on Chinese patent medicines for the treatment of osteoporosis is generally low. High-quality clinical trials are still needed in the future to further validate clinical efficacy.
Drugs, Chinese Herbal/therapeutic use* ; Osteoporosis/drug therapy* ; Humans ; Nonprescription Drugs/therapeutic use* ; Evidence-Based Medicine ; Medicine, Chinese Traditional

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