Volume 05 · Issue 02 · 2025

As an emerging interdisciplinary field, digital orthopedics is hailed as the third technological wave in orthopedics, with its applications gradually expanding into various areas and continuously innovating orthopedic clinical practice. Through advanced technologies such as 3D printing, extended reality, finite-element analysis, robotic-assisted surgery, and artificial intelligence, the diagnosis, treatment, and rehabilitation of orthopedic diseases have become more convenient, precise, and personalized. This article primarily introduced the main advantages and applications of digital orthopedic technology and evaluates its clinical efficacy and development potential, providing important references for future research and clinical practice.

The tumor microenvironment (TME) is a pivotal determinant of cancer progression and therapeutic response. The advent of individualized tumor therapy, based on the in-depth analysis of the TME, represents a revolutionary transformation in oncology. Artificial intelligence (AI) provides unparalleled capabilities to analyze and decipher the complexities of the TME through multi-omics integration, spatial modeling, and predictive analytics. By combining computational innovations with clinical insights, AI is driving a new paradigm in precision medicine. This editorial explores the transformative potential of AI in individualized tumor therapy, highlighting the groundbreaking applications and strategic directions to advance this field.

Classifying motor imagery tasks via functional near-infrared spectroscopy (fNIRS) poses a significant challenge in brain-computer interface (BCI) research due to the high-dimensional nature of the signals. This study aimed to address this challenge by employing the common spatial pattern (CSP) algorithm to reduce input dimensions for support vector machine (SVM) and linear discriminant analysis (LDA) classifiers.

Data were collected from 15 healthy right-handed volunteers performing tasks involving left-hand motion, left-hand motor imagery, right-hand motion, and right-hand motor imagery. Signals from 20-channel fNIRS were utilized, with input features including statistical descriptors such as mean, variance, slope, skewness, and kurtosis. The CSP algorithm was integrated into both SVM and LDA classifiers to reduce dimensionality. The main statistical methods included classification accuracy assessment and comparison.

Mean and slope were found to be the most discriminative features. Without CSP, SVM and LDA classifiers achieved average accuracies of 59.81 % ± 0.97 % and 69 % ± 11.42 %, respectively. However, with CSP integration, accuracies significantly improved to 81.63 % ± 0.99 % and 84.19 % ± 3.18 % for SVM and LDA, respectively. This value represents an increase of 21.82 % and 15.19 % in accuracy for SVM and LDA classifiers, respectively. Dimensionality reduction from 100 to 25 dimensions was achieved for SVM, leading to reduced computational complexity and faster calculation times. Additionally, the CSP technique enhanced LDA classifier accuracy by 3.31 % for both motion and motor imagery tasks.

Integration of the CSP algorithm may demonstrate promising potential for improving BCI systems' performance.

Oral cancer is the most common head and neck malignancy and may develop from oral leukoplakia (OL) and oral lichenoid disease (OLD). Machine learning classifiers using structured (tabular) data have been employed to predict malignant transformation in OL and OLD. However, current models require improved discrimination, and their frameworks may limit feature fusion and multimodal risk prediction. Therefore, this study investigates whether tabular-to-image data conversion and deep learning (DL) based on convolutional neural networks (CNNs) can improve malignant transformation prediction compared to traditional classifiers.

This study used retrospective data of 1,010 patients with OL and OLD treated at Queen Mary Hospital, Hong Kong, from January 2003 to December 2023, to construct artificial intelligence-based models for oral cancer risk stratification in OL/OLD. Twenty-five input features and information on oral cancer development in OL/OLD were retrieved from electronic health records. Tabular-to-2D image data transformation was achieved by creating a feature matrix from encoded labels of the input variables arranged according to their correlation coefficient. Then, 2D images were used to populate five pre-trained DL models (VGG16, VGG19, MobileNetV2, ResNet50, and EfficientNet-B0). Area under the receiver operating characteristic curve (AUC), Brier scores, and net benefit of the DL models were calculated and compared to five traditional classifiers based on structured data and the binary epithelial dysplasia grading system (current method).

This study found that the DL models had better AUC values (0.893-0.955) and Brier scores (0.072-0.106) compared to the traditional classifiers (AUC: 0.887-0.941 and Brier score: 0.074-0.136) during validation. During internal testing, VGG16 and VGG19 had better AUC values and Brier scores than other CNNs (AUC: 0.998-1.00; Brier score: 0.036-0.044) and the best traditional classifier (random forest) (AUC: 0.906; Brier score: 0.153). Additionally, VGG16 and VGG19 models outperformed random forest in discrimination and calibration during external testing (AUC: 1.00 vs. 0.976; Brier score: 0.022-0.034 vs. 0.129). The best CNNs also had better discriminatory performance and calibration than binary dysplasia grading at internal and external testing. Overall, decision curve analysis showed that the optimal DL models with transformed data had a higher net benefit than random forest and binary dysplasia grading.

Tabular-to-2D image data transformation may improve the use of structured input features for developing optimal intelligent models for oral cancer risk prediction in OL and OLD using convolutional networks. This approach may have the potential to robustly handle structured data in multimodal DL frameworks for oncological outcome prediction.

Osteosarcoma is a prevalent primary malignant bone tumor in children and adolescents, accounting for approximately 5 % of childhood malignancies. Because of its rarity and biological complexity, treatment breakthroughs for osteosarcoma have been limited. To advance research in this field, we aimed to construct the first comprehensive osteosarcoma knowledge graph (OSKG) using the PubMed database.

A systematic search of PubMed (2003-2023) using the keyword "osteosarcoma" yielded 25,415 abstracts. Leveraging BioBERT, pretrained on biomedical corpora and fine-tuned with osteosarcoma-specific manual annotations, we identified 16 entity types and 17 biological relationships. The extracted elements were synthesized to create the OSKG, resulting in a deep learning-based knowledge base to explore osteosarcoma pathogenesis and molecular mechanisms. We then developed a specialized question-answering system (knowledge graph question answering (KGQA)) powered by ChatGLM3. This system employs advanced natural language processing and incorporates the OSKG to ensure optimal response quality and accuracy.

The pretrained BioBERT averaged > 92 % accuracy in entity and relationship training. Evaluation using 100 pairs of gold-standard quizzes showed that the final quiz system outperformed other large language models in accuracy and robustness.

The system is designed to provide accurate disease-related queries and answers, effectively facilitating knowledge acquisition and reasoning in medical research and clinical practice. This project offers a robust tool for osteosarcoma research and promotes the deep integration of knowledge graphs and artificial intelligence technologies in the medical field.

Medical imaging is essential for the diagnosis and treatment of liver diseases, and the objective analysis of such images is vital for precision medicine. Integration of artificial intelligence (AI), particularly computer vision, into hepatology has seen considerable growth. This study conducts a bibliometric analysis to map the evolution, principal trends, and focal points of AI in liver disease imaging research. We conducted a comprehensive literature review using the Web of Science Core Collection and PubMed databases, spanning January 1990 to July 2023, with keywords related to liver diseases and AI in medical imaging. The search resulted in 3,629 documents, with a surge in publications after 2017. The United States and China led in terms of publication volume, with the former exhibiting higher H-index scores and citation counts. However, greater number of research institutions that contribute significantly to publications in the relevant fields are based in China. Keyword analysis revealed extensive research on liver fibrosis, hepatocellular carcinoma, cirrhosis, and fatty liver disease. Techniques such as image segmentation, classification, and registration are prevalent, meeting clinical needs like lesion detection and disease prognosis. Convolutional neural networks (CNNs), particularly U-Net models, are predominantly utilized. This review synthesizes the findings to guide future advancements in AI-assisted liver disease diagnosis and management.

Skin diseases are important factors affecting health and quality of life, especially in rural areas where medical resources are limited. Early and accurate diagnosis can reduce unnecessary health and economic losses. However, traditional visual diagnosis poses a high demand on both doctors’ experience and the examination equipment, and there is a risk of missed diagnosis and misdiagnosis. Recently, advances in artificial intelligence technology, particularly deep learning, have resulted in the use of unimodal computer-aided diagnosis and treatment technologies based on skin images in dermatology. However, due to the small amount of information contained in unimodality, this technology cannot fully demonstrate the advantages of multimodal data in the real-world medical environment. Multimodal data fusion can fully integrate various types of data to help doctors make more accurate clinical decisions. This review aimed to provide a comprehensive overview of multimodal data and deep learning methods that could help dermatologists diagnose and treat skin diseases.

Large language models (LLMs) have emerged as transformative tools with significant potential across healthcare and medicine. In clinical settings, they hold promises for tasks ranging from clinical decision support to patient education. Advances in LLM agents further broaden their utility by enabling multimodal processing and multi-task handling in complex clinical workflows. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to the high-risk nature of healthcare and the complexity of medical data. This paper provides a comprehensive overview of current evaluation practices for LLMs and LLM agents in medicine. We contributed 3 main aspects: First, we summarized data sources used in evaluations, including existing medical resources and manually designed clinical questions, offering a basis for LLM evaluation in medical settings. Second, we analyzed key medical task scenarios: closed-ended tasks, open-ended tasks, image processing tasks, and real-world multitask scenarios involving LLM agents, thereby offering guidance for further research across different medical applications. Third, we compared evaluation methods and dimensions, covering both automated metrics and human expert assessments, while addressing traditional accuracy measures alongside agent-specific dimensions, such as tool usage and reasoning capabilities. Finally, we identified key challenges and opportunities in this evolving field, emphasizing the need for continued research and interdisciplinary collaboration between healthcare professionals and computer scientists to ensure safe, ethical, and effective deployment of LLMs in clinical practice.

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