Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecul...Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecular alterations.Recent advances in artificial intelligence(AI),particularly deep learning,have enabled the integration of diverse clinical data modalities to support diagnosis,treatment planning,and prognostication in bone oncology.This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging,digital pathology,multi-omics profiling,and electronic health records.We conducted a structured review of peer-reviewed literature published between 2015 and early 2025,focusing on the development,validation,and clinical applicability of AI models for BT diagnosis,subtyping,treatment response prediction,and recurrence monitoring.Although multimodal models have demonstrated advantages over unimodal approaches,especially in handling missing data and improving generalizability,most remain constrained by single-center study designs,small sample sizes,and limited prospective or external validation.Persistent technical and translational challenges include semantic misalignment across modalities,incomplete datasets,limited model interpretability,and regulatory and infrastructural barriers to clinical integration.To address these limitations,we highlight emerging directions such as contrastive representation learning,generative data augmentation,transformer-based fusion architectures,and privacy-preserving federated learning.We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability.In summary,multimodal AI represents a promising paradigm for advancing precision care in BTs.Realizing its full clinical potential will require methodologically rigorous,biologically informed,and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.展开更多
Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm,which have characteristics of high frequency and short wavelength.They have gradually and widely been used in engineering and medical fields.We h...Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm,which have characteristics of high frequency and short wavelength.They have gradually and widely been used in engineering and medical fields.We have identified studies related to millimeter waves in the biomedical field and summarized the biological effects of millimeter waves and their current status in medical applications.Finally,the shortcomings of existing studies and future developments were analyzed and discussed,with the aim of providing a reference for further research and development of millimeter waves in the medical field.展开更多
Coronavirus disease 2019(COVID-19)made a huge effect globally.With the assistance of mixed reality(MR)technology,complicated clinical works became easier to carry out and the condition had been greatly improved with h...Coronavirus disease 2019(COVID-19)made a huge effect globally.With the assistance of mixed reality(MR)technology,complicated clinical works became easier to carry out and the condition had been greatly improved with high-tech advantages such as improved convenience,better understanding and communication,higher security,and medical resource saving.This study aimed to introduce one kind of MR application in the fight against COVID-19 and anticipate more feasible smart healthcare applications to enhance our strength for the final victory.展开更多
基金supported by the National Natural Science Foundation of China[Grant No.:82172524]the Natural Science Foundation of Hubei Province[Grant No.:2025AFB240].
文摘Bone tumors(BTs)-including osteosarcoma,Ewing sarcoma,and chondrosarcoma-are rare but biologically complex malignancies characterized by pronounced heterogeneity in anatomical location,histological subtype,and molecular alterations.Recent advances in artificial intelligence(AI),particularly deep learning,have enabled the integration of diverse clinical data modalities to support diagnosis,treatment planning,and prognostication in bone oncology.This review provides a comprehensive synthesis of AI-driven multimodal fusion strategies that incorporate radiological imaging,digital pathology,multi-omics profiling,and electronic health records.We conducted a structured review of peer-reviewed literature published between 2015 and early 2025,focusing on the development,validation,and clinical applicability of AI models for BT diagnosis,subtyping,treatment response prediction,and recurrence monitoring.Although multimodal models have demonstrated advantages over unimodal approaches,especially in handling missing data and improving generalizability,most remain constrained by single-center study designs,small sample sizes,and limited prospective or external validation.Persistent technical and translational challenges include semantic misalignment across modalities,incomplete datasets,limited model interpretability,and regulatory and infrastructural barriers to clinical integration.To address these limitations,we highlight emerging directions such as contrastive representation learning,generative data augmentation,transformer-based fusion architectures,and privacy-preserving federated learning.We also discuss the evolving role of foundation models and workflow-integrated AI agents in enhancing scalability and clinical usability.In summary,multimodal AI represents a promising paradigm for advancing precision care in BTs.Realizing its full clinical potential will require methodologically rigorous,biologically informed,and system-level approaches that bridge algorithmic innovation with real-world healthcare delivery.
基金the National Natural Science Foundation of China(Grant No.81974355)Establishment of the National Intelligent Medical Clinical Research Center(Grant No.2020021105012440)Hubei Province’s New Generation of Artificial Intelligence Key Research and Development Projects(Grant No.2021BEA161).
文摘Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm,which have characteristics of high frequency and short wavelength.They have gradually and widely been used in engineering and medical fields.We have identified studies related to millimeter waves in the biomedical field and summarized the biological effects of millimeter waves and their current status in medical applications.Finally,the shortcomings of existing studies and future developments were analyzed and discussed,with the aim of providing a reference for further research and development of millimeter waves in the medical field.
基金This study was funded by the National Natural Science Foundation of China(Grant No.81974355)Major Technical Innovation Project of the Hubei Province(Grant No.2018AAA067).
文摘Coronavirus disease 2019(COVID-19)made a huge effect globally.With the assistance of mixed reality(MR)technology,complicated clinical works became easier to carry out and the condition had been greatly improved with high-tech advantages such as improved convenience,better understanding and communication,higher security,and medical resource saving.This study aimed to introduce one kind of MR application in the fight against COVID-19 and anticipate more feasible smart healthcare applications to enhance our strength for the final victory.
