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Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications.

Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high‐dimensional exposures and high‐dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein–structure–memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method. Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high‐dimensional exposures and high‐dimensional mediators to integrate data collected from multiple platforms. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein‐structure‐memory paths, which are in accordance with and also supplement existing findings of AD research.

The array of complex and evolving patient data has limited clinical decision making in the emergency department (ED). This study introduces an advanced deep learning algorithm designed to enhance real-time prediction accuracy for integration into a novel Clinical Decision Support System (CDSS). A retrospective study was conducted using data from a level 1 tertiary hospital. The algorithm’s predictive performance was evaluated based on in-hospital cardiac arrest, inotropic circulatory support, advanced airway, and intensive care unit admission. We developed an artificial intelligence (AI) algorithm for CDSS that integrates multiple data modalities, including vitals, laboratory, and imaging results from electronic health records. The AI model was trained and tested on a dataset of 237,059 ED visits. The algorithm’s predictions, based solely on triage information, significantly outperformed traditional logistic regression models, with notable improvements in the area under the precision-recall curve (AUPRC). Additionally, predictive accuracy improved with the inclusion of continuous data input at shorter intervals. This study suggests the feasibility of using AI algorithms in diverse clinical scenarios, particularly for earlier detection of clinical deterioration. Future work should focus on expanding the dataset and enhancing real-time data integration across multiple centers to further optimize its application within the novel CDSS.

The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding of the brain, unlocking new possibilities in research, diagnosis, and therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing neuroscience by enabling the analysis of complex neural datasets, from neuroimaging and electrophysiology to genomic profiling. These advancements are transforming the early detection of neurological disorders, enhancing brain–computer interfaces, and driving personalized medicine, paving the way for more precise and adaptive treatments. Beyond applications, neuroscience itself has inspired AI innovations, with neural architectures and brain-like processes shaping advances in learning algorithms and explainable models. This bidirectional exchange has fueled breakthroughs such as dynamic connectivity mapping, real-time neural decoding, and closed-loop brain–computer systems that adaptively respond to neural states. However, challenges persist, including issues of data integration, ethical considerations, and the “black-box” nature of many AI systems, underscoring the need for transparent, equitable, and interdisciplinary approaches. By synthesizing the latest breakthroughs and identifying future opportunities, this review charts a path forward for the integration of AI and neuroscience. From harnessing multimodal data to enabling cognitive augmentation, the fusion of these fields is not just transforming brain science, it is reimagining human potential. This partnership promises a future where the mysteries of the brain are unlocked, offering unprecedented advancements in healthcare, technology, and beyond.

Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis‐driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine‐learning approaches across most cancer types. Compared to traditional machine‐learning‐based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows. Synopsis netDx is a supervised patient classification algorithm based on the paradigm of patient similarity networks. It integrates multi‐omic data and uses biological pathway information to help with model interpretability. In a cancer survival prediction benchmark, netDx performs competitively or better than a diverse panel of machine‐learning algorithms. When patient similarity is defined by pathway‐level gene expression, netDx identifies biological pathways predictive of outcome, as demonstrated in diverse data sets (breast cancer and asthma). netDx is freely available as an R package and as a Docker image. Code, tutorials and worked examples are available at: http://netdx.org . Graphical Abstract netDx is a supervised patient classification algorithm based on the paradigm of patient similarity networks. It integrates multi‐omic data and uses biological pathway information to help with model interpretability.

BackgroundDiabetic foot (DF) is one of the most severe complications of type 2 diabetes mellitus (T2DM), contributing to over 85% of diabetes-related lower limb amputations and a 5-year mortality rate comparable to certain cancers. Current diagnostic approaches face challenges including over-reliance on single-indicator screening, limited multimodal data integration, and lack of model interpretability.MethodsA dataset integrating five modalities-sociodemographic characteristics, physiological indicators, traditional Chinese medicine (TCM) tongue features, plantar hardness metrics, and laboratory biomarkers-was prospectively collected from 391 patients (124 T2DM, 267 DF) at a single tertiary hospital between May 2019 and October 2022. The final model was constructed using 18 clinical features from sociodemographic, physiological, and laboratory modalities. Seven machine learning algorithms were developed and compared, and SHapley Additive exPlanations (SHAP) were used for interpretability analysis.ResultsLightGBM achieved optimal performance (accuracy: 88.61%, sensitivity: 87.76%, specificity: 90.00%, AUC: 0.9519). Key classification features included age, body mass index (BMI), creatinine (Cr), white blood cell count (WBC), and uric acid (UA).DiscussionThese features reflect general systemic inflammation, metabolic burden, and renal function rather than DF-specific pathology. The study contributes (1) an open-source multimodal DF dataset bridging TCM and Western medicine, (2) a classification tool that distinguishes DF from uncomplicated T2DM with reasonable accuracy as a potential supplementary screening instrument pending external validation, and (3) novel mechanistic insights suggesting that systemic inflammatory markers may play an important role in DF pathophysiology.

IntroductionPulmonary diseases pose significant health risks to athletes, necessitating accurate early detection and risk prediction methods. In this study, we propose a novel Multimodal Pulmonary Risk Prediction Network (MPRPN), which integrates visual data, textual data, and auxiliary physiological data through a unified deep learning framework.MethodsThe model incorporates an Adaptive Modality Weighting Strategy (AMWS) to dynamically adjust modality contributions and a Hierarchical Risk Prediction Strategy (HRPS) to capture domain-specific feature structures. Experiments were conducted on multiple multimodal datasets, including the Athlete Respiratory Health Records dataset, Multimodal Pulmonary Imaging Collection, Pulmonary Risk Profiles dataset, and Early Detection Biomarker dataset, comprising diverse clinical, imaging, and physiological samples.Results and discussionThe proposed method achieves superior performance compared to state-of-the-art models, with accuracy improvements up to 89.92%, F1-score reaching 90.23%, and AUC up to 90.47%, demonstrating strong predictive capability and robustness. These results indicate that MPRPN effectively leverages complementary multimodal information and provides a reliable tool for early detection and personalized risk assessment of pulmonary diseases in athletes. The proposed framework has significant potential for real-world applications in sports medicine and preventive healthcare.

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.