Ensuring the freshness of meat is crucial for food safety and consumer trust. Traditional methods for evaluating meat freshness, such as sensory analysis and chemical assays, are time-consuming, labor-intensive, and destructive. Machine learning (ML) offers a promising alternative by providing real-time, nondestructive solutions for monitoring meat quality, rationalizing the food industry. This review examines the principles and applications of ML in meat freshness evaluation, focusing on key algorithms like Principal Component Regression (PCR), Partial Least Squares (PLS), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and neural networks. It also details the ML-based detection pipeline, covering data acquisition, preprocessing, model selection, and fine-tuning. The paper highlights recent advancements in ML approaches tailored for meat freshness assessment, such as Convolutional Neural Networks (CNN) and ensemble learning models, which have proven effective in tackling spoilage rate, safety concerns, and the complex chemical composition of meat. However, challenges remain, including the need for high-quality datasets and model interpretability. Addressing these challenges will be crucial for the widespread adoption of ML-based solutions in meat freshness detection, ultimately leading to safer and higher-quality food products.

The implementation of the Reduce, Reuse, and Recycle (3R) program in educational institutions plays a strategic role in fostering environmental awareness from an early age; however, its evaluation has often relied on descriptive approaches rather than objective data-driven analysis. This study aims to analyze the level of success of the 3R program implementation in schools and to identify the key factors influencing its success using a data mining approach with the C4.5 algorithm. A quantitative descriptive-analytic method was employed, utilizing primary data collected through observation and documentation of 3R program activities in schools. The data analysis followed the knowledge discovery in databases (KDD) process, including data selection, preprocessing, transformation, modeling, and evaluation. The results indicate that the C4.5 algorithm achieved a classification accuracy of 98.94%, demonstrating excellent model performance. The generated decision tree reveals that student participation is the most influential factor in determining the success of the 3R program, followed by parental involvement and teacher support. These findings suggest that the success of the 3R program is not solely determined by school policies, but largely depends on the active participation of key educational stakeholders. This study provides practical implications for schools and policymakers by offering a data-driven evaluation model that supports more objective decision-making and promotes the integration of environmental programs into the learning process within educational institutions.

In the era of ever-greater data production and collection, public health research is often limited by the scarcity of data. To improve this, we propose data sharing in the form of Data Spaces, which provide technical, business, and legal conditions for an easier and trustworthy data exchange for all the participants. The data must be described in a commonly understandable way, which can be assured by machine-readable ontologies. We compared the semantic interoperability technologies used in the European Data Spaces initiatives and adopted them in our use case of physical development in children and youth. We propose an ontology describing data from the Analysis of Children’s Development in Slovenia (ACDSi) study in the Resource Description Framework (RDF) format and a corresponding Next Generation Systems Interface-Linked Data (NGSI-LD) data model. For this purpose, we have developed a tool to generate an NGSI-LD data model using information from an ontology in RDF format. The tool builds on the declaration from the standard that the NGSI-LD information model follows the graph structure of RDF, so that such translation is feasible. The source RDF ontology is analyzed using the standardized SPARQL Protocol and RDF Query Language (SPARQL), specifically using Property Path queries. The NGSI-LD data model is generated from the definitions collected in the analysis. The translation has been verified on Smart Applications REFerence (SAREF) ontology SAREF4BLDG and its corresponding Smart Data Models (52 models at the time). The generated artifacts have been tested on a Context Broker reference implementation. The tool supports basic ontology structures, and for it to translate more complex structures, further development is needed.

Against the background of the deep penetration of the digital economy into the real economy, digital transformation has become a core path for enterprises to optimize resource allocation and enhance competitiveness. However, a unified understanding has not yet been formed regarding the interactive mechanism between capital structure — the core of financing decisions—and digital transformation as well as corporate performance. This paper takes Chinese A-share non-financial listed companies from 2015 to 2024 as the research sample, using data from the CSMAR database and annual report text analysis, and employs a panel data model to empirically test the interactive relationship among the three. The results show that: there is a short-term weak negative non-linear correlation between digital transformation and corporate performance, reflecting the inhibitory effect of costs in the initial stage of transformation; capital structure plays an intermediary role, but the current high debt and transformation costs form a superimposed constraint, jointly inhibiting performance; among control variables, return on equity (ROE) and revenue growth rate positively drive performance, while firm age shows an efficiency attenuation characteristic, and the model results are reliable with heterogeneous impacts of transformation; the framework of "digital transformation-capital structure-corporate performance" is initially established, but the performance feedback effect needs to be deepened, and the cyclic mechanism of the three requires verification with long-term data.

The Al-Muhajirin Balongsari Mosque in Surabaya still faces obstacles in managing information and finances because administrative processes are carried out manually, which can potentially lead to recording errors, loss of archives, delays in information delivery, and limited access for worshippers to activity and financial data. This study intends to design and develop a web-based Mosque Management Information System to facilitate integrated data management. The system development employs the Rapid Application Development (RAD) methodology to ensure a swift, iterative process aligned with user requirements. The proposed system includes management of agendas/activities, income, expenses, donations/transfers, transaction categories, galleries, and reporting, with access rights based on user roles: admin, officer, and congregation. The system architecture is depicted using Data Flow Diagrams (DFDs), Entity-Relationship Diagrams (ERDs), Conceptual Data Models (CDMs), and Physical Data Models (PDMs) to ensure data connectivity and transaction fidelity. The implementation results indicate that administrators can oversee officer and category data and generate reports; officers can enter transactions, verify transfers, manage agendas and galleries, and compile reports; while congregants can access activity information, prayer schedules, financial data, and donation services online. Based on functional testing of the administrator, officer, and user modules, the main features run as expected and produce valid outputs, indicating the system is capable of improving the efficiency, transparency, accountability, and accessibility of administrative management and the delivery of mosque information to congregants.

Extracellular vesicles (EVs) are emerging as naturally bioactive nanomaterials with intrinsic biocompatibility and targeting potential. Recent integration of machine learning (ML) into EV research has accelerated advances in molecular profiling, structure-function prediction, and rational design of vesicle-based therapeutics. Yet, the inherent complexity and heterogeneity of EV populations pose major analytical challenges. Concurrently, machine learning is revolutionizing biomedical science by uncovering patterns in high dimensional, multimodal datasets. In EV research, ML has enabled major advances across automated imaging, multi omics integration, disease classification, therapeutic engineering, and standardization. This review presents a comprehensive synthesis of ML-enabled EV studies, organized by data modality (imaging, omics, cytometry), algorithmic paradigm (CNNs, random forests, autoencoders, GNNs), and translational application (diagnosis, prognosis, drug delivery, manufacturing QC). Unlike prior reviews that have typically considered EV biology and AI methods in relative isolation, we introduce a unified three-axis taxonomy that explicitly links EV data modalities, machine learning architectures, and clinical use-cases, thereby providing a structured map of the field. We discuss key technical barriers including data sparsity, batch variability, and model explainability and spotlight frontier developments such as federated learning, self-supervised models, and real-time EV analytics. At the nexus of computational intelligence and nanomedicine, ML-enhanced EV platforms are rapidly progressing from fragmented innovations to clinically actionable systems. This review offers a roadmap for advancing AI-integrated EV technologies in cancer precision medicine.

Additive manufacturing (AM) of polymers and polymer composites is changing how customized, lightweight, and complex parts are produced across various industries. However, predicting the final properties of printed parts remains challenging due to variations in material compositions, processing conditions, and microstructural characteristics. This review explores how machine learning (ML) is being used to address these challenges. It examines the application of various ML approaches in polymer and polymer composite design for AM, including supervised, unsupervised, semi-supervised, self-supervised, and reinforcement learning, for predicting key properties such as mechanical strength, thermal stability, and electrical performance. The review also highlights hybrid techniques that combine ML with physics-informed modeling, including the use of digital twins, to enhance AM process control. Challenges and future perspectives, such as data scarcity, model interpretability, and computational demands, are discussed. In summary, ML is showing strong potential to support faster, more reliable, and more sustainable development of advanced polymers and polymer composites for AM.

Innovative data integration may serve to inform rapid, local responses to community needs. We conducted a mixed methods pilot study among communities of color or low-income in the San Francisco Bay Area amid the COVID-19 pandemic to assess a hypothesized data model to inform rapid response efforts. Between 2020-2021, we collected (1) qualitative data through neighborhood reports submitted via Streetwyze, a mobile neighborhood mapping platform; (2) survey data on social and economic circumstances; and (3) geospatial data among residents of three counties. Qualitative data were coded and then integrated with survey and geospatial data. We used descriptive analyses to examine participants' experiences with food in their neighborhoods. Among 51 participants, seventy percent of participants reported food insecurity before and after the pandemic began in March 2020. Within neighborhood reports, food was the most frequently occurring sub-theme within the Goods and Resources parent themes (68% and 49% of reports, respectively). Security (88%), resource programs (88%), outdoor space (84%), and equity (83%) were more likely to be mentioned by participants who were food insecure compared to those who were not (12%, 12%, 16%, 17%, respectively). Mentions of food in neighborhood reports more often occurred in census tracts with lower socioeconomic status and more area-level food insecurity. Individuals who were food insecure reported a constellation of needs beyond food, including needs related to safety and greater social equity. Our data model illustrates the potential for rapid assessment of community residents' experiences to provide enhanced understanding of community-level needs and effective support in the face of changing circumstances.

Multimodal artificial intelligence (AI) technologies are transforming medical practices by integrating diverse data sources to enable more accurate diagnosis, disease prediction, and treatment planning. In this review, we explore state-of-the-art multimodal AI systems, focusing on their applications in clinical settings, including radiology, pathology, and clinical imaging, as well as non-image data, such as electronic health records (EHRs) and multi-omics data. We highlight how combining multiple modalities improves diagnostic accuracy and prognostic prediction compared to unimodal models. The study emphasizes the importance of robust data fusion strategies and model interpretability for real-world clinical deployment. By addressing key challenges, such as data heterogeneity and uncertainty quantification, this research offers a new paradigm for intelligent healthcare. The findings suggest that the continued advancement of multimodal AI will significantly enhance clinical decision-making, paving the way for personalized medicine and improved patient outcomes.

With the increasing volume of biomedical experimental data, standardizing, sharing, and integrating heterogeneous experimental data across domains has become a major challenge. To address this challenge, we have developed an ontology-supported Study-Experiment-Assay (SEA) common data model (CDM), which includes 10 core and 3 auxiliary classes based on object-oriented modeling. SEA CDM uses interoperable ontologies for data standardization and knowledge inference. Building on the SEA CDM, we developed the Ontology-based SEA Network (OSEAN) relational database and knowledge graph, along with a set of ETL (Extract, Transform, Load) and query tools, and further applied them to represent 1,278 immune studies with over two million samples from three resources: VIGET, ImmPort, and CELLxGENE. Using simple, robust queries and analyses, our research identified multiple scientific insights into sex-specific immune responses, such as neutrophil degranulation and TNF binding to physiological receptors, following live attenuated and trivalent inactivated influenza vaccination. The novel SEA CDM system lays a foundation for establishing an integrative biodata ecosystem across biological and biomedical domains.

Hyponatremia is a common yet potentially serious adverse event associated with antidepressants. Identifying the antidepressant class with the least risk of hyponatremia would improve patient safety. Using electronic medical records from 15 hospitals standardized into Observational Medical Outcomes Partnership Common Data Model (2003-2023), we identified patients diagnosed with depression who initiated antidepressants, including selective serotonin reuptake inhibitor (SSRI), serotonin-norepinephrine reuptake inhibitor (SNRI), tricyclic antidepressants (TCA) or others (agomelatine, bupropion, mirtazapine, moclobemide and trazodone) for at least 30 days. The index date was defined as the first antidepressant prescription, and four mutually exclusive cohorts were constructed based on the antidepressant class prescribed on index date. Each cohort was compared with all other antidepressants. The primary outcome was incident hyponatremia (serum sodium <135 mmol/L) within the first 180 days. After propensity score stratification, hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using Cox proportional hazards regression. Fixed-effect meta-analysis was used to pool the results from each site. We identified 17 895 (42.6%) patients in SSRI, 7395 (17.6%) in SNRI, 5424 (12.9%) in TCA and 11 322 (26.9%) in other group. The risk of hyponatremia increased within 180 days after SSRI initiation (HR 1.18, 95% CI 1.01-1.38) compared with all other depressants, with a higher risk in patients aged ≥60 years (1.29, 1.06-1.57). No significant association was found for SNRIs (1.05, 0.87-1.27), TCAs (1.03, 0.84-1.26) or other (0.90, 0.77-1.06). Close monitoring of serum sodium levels is essential for SSRI users, especially those aged ≥60 years.

Modern applications in healthcare, finance and cross-institutional research increasingly require building predictive models from sensitive data that reside across multiple centers. However, multi-center data often exhibit challenges such as data imbalance, non-IID distributions, and strict privacy regulations, which limit the effectiveness of standard federated learning methods. Direct data sharing is often infeasible due to privacy, regulatory and institutional constraints. In this work we propose MAPLE-Fed, a novel multicenter federated learning framework that tightly couples adaptive differential privacy with heterogeneity-aware strategies and secure model exchange to enable high-utility, privacy-preserving modeling of sensitive distributed data. MAPLE-Fed introduces (1) an adaptive per-center privacy budgeting mechanism that dynamically allocates differential privacy (DP) noise according to each center’s data utility, sensitivity and contribution, maximizing global model performance under a fixed privacy budget; (2) a unified heterogeneity-aware clipping and fairness calibration strategy that mitigates the adverse effect of non-IID distributions and small-sample centers; (3) a hybrid secure aggregation + cross-site representation distillation pipeline, where encrypted updates protect gradients while teacher-student distillation aligns latent representations across centers without exposing raw data or labels; and (4) an analytical privacy-utility trade-off analysis with practical scheduling rules for budget decay and aggregation frequency. We validate MAPLE-Fed on multi-center benchmarks and demonstrate consistent improvements in accuracy and fairness compared to baseline DP-FedAvg and naively privatized federated approaches, while satisfying rigorous DP guarantees. MAPLE-Fed thus provides a practical, theoretically grounded path for collaborative modeling with sensitive multi-center data.

Two-dimensional (2D) materials offer an exceptional platform for exploring quantum phenomena, as their reduced dimensionality significantly enhances tunability via external parameters. Among these, superconductivity in 2D systems is of particular interest due to its fundamental significance and potential applications in quantum technologies. Despite ongoing experimental challenges in realizing novel 2D superconductors, first-principles calculations have emerged as powerful tools for guiding their prediction and design. While many prior reviews focus broadly on low-dimensional superconductivity, this article specifically surveys computationally predicted two-dimensional superconductors, with an emphasis on the underlying theoretical frameworks and their limitations. We highlight how external perturbations such as strain, doping, chemical functionalization, and intercalation, modify electron-phonon coupling and superconducting critical temperatures, and we examine cases where superconductivity competes or coexists with other quantum orders, including charge density waves and nontrivial band topology. We further discuss the growing role of machine-learning and high-throughput approaches in accelerating materials discovery, along with the challenges associated with data quality and model reliability. Overall, this review underscores the potential and current limitations of first-principles and data-driven approaches in advancing the understanding and discovery of two-dimensional superconductors.

Longitudinal mixture modeling allows for estimation of person-level patterns when there is heterogeneity in how people change over time. We demonstrate two modeling approaches: latent class growth analysis/growth mixture modeling (LCGA/GMM) and repeated measures latent profile analysis (RMLPA). The data originated from a randomized trial examining mechanisms of exercise behavior maintenance. We previously reported that average affective response remained stable during exercise training. The present study tests whether affective response over time could be best described through the estimation of latent subpopulations. Secondary analysis of women (n = 201, mean age = 37.4; baseline mean BMI = 29.3) recruited for a 16-week randomized trial of exercise intensity/duration. Affective response was measured within exercise bout (minutes 0, 10, 20, 30, and 40) over four waves (weeks 1, 4, 8, and 16). LCGA/GMM was the primary approach for average-bout affective response (4 time points; "wave-level"), where a 3-class solution emerged of "stable," "high, increasing," and "decreasing" affective response patterns over time. RMLPA was used for minute-interval analyses where a four-class solution emerged. Weighted analyses examined theoretical outcomes (e.g., change in VO2max, posttest Theory of Planned Behavior constructs) of latent class membership. Person-centered methodologies demonstrated heterogeneity in affective response over time and within specific exercise bouts. The rich longitudinal data structure facilitated illustration and comparison between methods in terms of: (1) assumptions about functional form, missing data, and random effects; (2) consideration of across wave versus within bout changes; and (3) implications of modeling choice on theory development. Supplemental materials include annotated MPlus and R code for data visualization and model estimation.

The growing complexity of global finance necessitates a unified framework that integrates data intelligence, risk analytics, and decision automation within a scalable architectural model. This paper proposes an Integrated Financial Intelligence Architecture (IFIA) designed to enhance the global scalability of financial systems through interoperability, advanced analytics, and real-time data governance. The model emphasizes the convergence of artificial intelligence, big data infrastructures, blockchain interoperability, and predictive analytics to strengthen decision-making across diverse regulatory and market contexts. It explores how modular system design and cross-border compliance protocols can facilitate adaptive scaling while maintaining transparency and resilience against systemic risks. The study critically reviews current limitations in fragmented financial architectures and proposes a conceptual framework that aligns financial intelligence with global digital transformation agendas. Furthermore, it examines the potential of cloud-native microservices, semantic data models, and federated learning to support secure data exchange and collaborative intelligence across institutions. The findings suggest that an integrated financial intelligence ecosystem can optimize risk management, reduce transaction inefficiencies, and enable sustainable financial innovation on a global scale. The paper concludes by highlighting policy, ethical, and governance considerations essential for implementing scalable and intelligent financial infrastructures. Keywords: Financial Intelligence Architecture, Global Scalability, Predictive Analytics, Blockchain Interoperability, Federated Learning, Digital Transformation.

The development of agricultural new productive forces (ANPFs) represents a vital pathway to overcoming the bottlenecks of agricultural modernization and reshaping agricultural competitiveness. As sustainable development and green transformation have become global priorities, the formation of ANPFs is increasingly viewed as a key engine for promoting resource-efficient agriculture, low-carbon production, ecological protection, and resilient food systems. Using panel data from 16 prefecture-level cities in Anhui Province, China, spanning the period 2010–2023, this study employs the entropy-weighted TOPSIS method to measure the levels of ANPFs and sustainable agricultural development (SAD). A panel data model is then applied to examine the impact of ANPFs on SAD, while a mediation-effect model is used to test the underlying transmission mechanisms. Finally, a spatial econometric model is employed to assess the spatial spillover effects between ANPFs and SAD. The results reveal that ANPFs exert a significant and robust positive impact on Anhui’s SAD, with the strength of this effect decreasing gradually from central to southern and northern regions. Further analysis indicates that the driving influence of ANPFs operates through three key mediating pathways: the improvement of new-type infrastructure, the enhancement of agricultural scientific and technological innovation, and the advancement of agricultural digital transformation. Moreover, ANPFs demonstrate a positive spatial spillover effect, suggesting that the development of new productive forces in one region promotes agricultural modernization in neighboring areas. These findings demonstrate that ANPFs not only enhance productivity but also contribute to sustainable agricultural development. Accordingly, strengthening ANPFs development can serve as an effective strategy for promoting long-term agricultural sustainability, indicating that central Anhui should be prioritized as a core hub for fostering ANPFs, enabling the gradient diffusion of infrastructure, innovation capacity, and digital services toward southern and northern Anhui. Strengthening regional coordination mechanisms will further amplify the spatial spillover of ANPFs, thereby advancing high-quality agricultural development across the province. This study provides new evidence for how ANPFs can support sustainable agricultural transformation, offering policy insights for green growth, food security, and rural revitalization.

Pathogen whole-genome sequencing (WGS) has significant potential for improving healthcare-associated infection (HAI) outcomes. However, methods for integrating WGS with epidemiologic data to quantify risks for pathogen spread remain underdeveloped. To identify analytic strategies for conducting WGS-based HAI surveillance in high-burden settings, we modeled patient- and facility-level transmission risks of carbapenem-resistant Klebsiella pneumoniae (CRKP) in a long-term acute care hospital (LTACH). Using rectal surveillance data collected over 1 year, we fit three pairwise regression models with three different metrics of genomic relatedness for pairs of case isolates, a proxy for transmission linkage: (i) single-nucleotide variant genomic distance, (ii) closest genomic donor, and (iii) common genomic cluster. To assess the performance of these approaches under real-world conditions defined by passive surveillance, we conducted a sensitivity study including only cases detected by admission surveillance or clinical symptoms. Genomic relatedness between pairs of isolates was associated with room sharing in two of the three models and overlapping stays on a high-acuity unit in all models, echoing previous findings from LTACH settings. In our sensitivity analysis, qualitative findings were robust to the exclusion of cases that would not have been identified with a passive surveillance strategy; however, uncertainty in all estimates also increased markedly. Taken together, our results demonstrate that pairwise regression models combining relevant genomic and epidemiologic data are useful tools for identifying HAI transmission risks.IMPORTANCEWhole-genome sequencing of healthcare-associated infections (HAIs) is becoming more common, and new methods are necessary to integrate these data with epidemiologic risk factors to quantify transmission drivers. We demonstrate how pairwise regression models, in which the outcome of a regression model represents genomic similarity between a pair of isolates, can identify known transmission risk factors of carbapenem-resistant Klebsiella pneumoniae in a long-term acute care facility. Such pairwise regression models could be used with rich epidemiologic data in other settings to identify important risk factors of endemic HAI transmission.

Artificial Intelligence in Snoring Sound Analysis: OSA Detection and Obstruction Site Classification, a Systematic Review.

IntroductionSalivary gland carcinomas (SGC) are rare tumours. The term SGC is not more than an umbrella for a variety of histogenetically, morphologically and biologically distinct entities. Accordingly, SGCs have not been sufficiently investigated to date. Their rarity makes it difficult to reach high patient numbers for individual entities in clinical studies, leading to pooling patients with different histological subtypes to attain sufficient participants. The different histological subtypes of SGC differ significantly in their clinicopathological features, such as their grading, their occurrence and their outcome. SGCs are usually stratified into low-grade, intermediate-grade or high-grade tumours. In most kinds of SGC, specific targetable molecular markers are lacking. The inclusion of immunotherapy (IT), however, might improve the outcome of patients suffering from high-grade SGCs. In order to integrate IT as a therapeutic option for SGC and to facilitate therapeutic decisions based on tumour (immune) biology, predictive and prognostic immunological biomarkers are indispensable.Methods and analysisIn this prospective study, 500 patients will be enrolled, who are distributed in three arms. The observational cohort includes patients with malignant salivary gland tumours, whereas patients with benign tumours of a salivary gland are grouped in the control group 1. In the control cohort, 2 patients do not have a salivary gland tumour but have a planned functional surgery of the nose or ear or a maxillofacial surgery. The local immune status from the tumour tissue and the microbiome will be sampled before treatment. In addition, the systemic immune status from peripheral blood will be analysed before and after surgery and after the adjuvant and definitive chemoradiotherapy, if applicable. Clinical baseline characteristics and outcome parameters will additionally be collected. Data mining and modelling approaches will finally be applied to identify interactions of local and systemic immune parameters and to define predictive and prognostic immune signatures based on the evaluated immune markers.Ethics and disseminationApproval from the institutional review board of the Friedrich-Alexander-Universität Erlangen-Nürnberg was granted in September 2023 (application number 23-292-B). The results will be disseminated to the scientific audience and the general public via presentations at conferences and publication in peer-reviewed journals.Trial registration numberNCT06047236.

BackgroundMultimodal and multi-omics data can revolutionize cardiovascular care by enabling artificial intelligence / machine learning (AI/ML) models to provide holistic, personalized insights. These models can enhance diagnostic precision by integrating imaging, clinical, and molecular data. Visual analytics can further aid interpretation, adoption, and trust in AI-driven tools.PurposeWe aimed to visually map the current landscape of multimodal AI/ML approaches in cardiovascular disease (CVD) and to identify translational gaps, focusing on: (a) algorithm development, (b) data readiness and interoperability, (c) validation and deployment, and (d) ethical and implementation considerations.MethodsA systematic review of peer-reviewed studies (last 10 years) was conducted using the Preferred Reporting Items for a Systematic Review and Meta-analysis (PRISMA). Articles were screened and extracted for data modality, model type, clinical application, and validation methods. We used qualitative synthesis along with visual frameworks to highlight trends and underexplored areas. Risk of bias was assessed using PROBAST.ResultsWhile integrated multimodal systems remain limited, we developed visual tools including: a matrix of modality pairings versus clinical applications, a timeline of validation stages (development to clinical deployment), and a gap map identifying underused modalities. For example, one validation stage study combining electrocardiogram (ECG) and echocardiography outperformed human experts in diagnosing left ventricular hypertrophy. Another used echocardiography and cardiac magnetic resonance imaging to guide cardiac resynchronization therapy. A third linked transcriptomics and single nucleotide polymorphisms (SNPs) to predictive CVD risk models, identifying transcriptomic features and SNPs with strong predictive performance. Our visual mappings revealed clustering around imaging-genomic pairings but sparse integration of behavioral or wearable data. Most models remained untested in real-world clinical settings.ConclusionsMultimodal AI/ML offers transformative potential for CVD care, but widespread clinical adoption will require real-world validation, integration, and interoperability. Our visual framework not only highlights technical opportunities but also uncovers policy and implementation gaps. Mapping modality usage, validation maturity, and integration readiness can guide researchers, developers, and regulators toward scalable, trustworthy AI-enabled clinical decision support tools.