Search Results (650)

The incorporation of artificial intelligence (AI) and digital technology in healthcare has revolutionized service delivery, improving diagnostic precision, patient outcomes, and operational efficacy. Nonetheless, despite considerable progress, numerous problems persist that impede the realization of full potential. Current reviews predominantly emphasize the advantages of AI in disease detection and health guidance, neglecting significant concerns such as social opposition, regulatory frameworks, and geographical discrepancies. This SLR, executed in accordance with PRISMA principles, examined 21 publications from 2020 to 2025 to assess the present condition of AI and digital technologies inside Saudi Arabia’s healthcare industry. Initially, 863 publications were obtained, from which 21 were chosen for comprehensive examination. Significant discoveries encompass the extensive utilization of telemedicine, data analytics, mobile health applications, Internet of Things, electronic health records, blockchain technology, online platforms, cloud computing, and encryption methods. These technologies augment diagnostic precision, boost patient outcomes, optimize administrative procedures, and foster preventative medicine, contributing to cost-effectiveness, environmental sustainability, and enduring service provision. Nonetheless, issues include data privacy concerns, elevated implementation expenses, opposition to change, interoperability challenge, and regulatory issues persist as substantial barriers. Subsequent investigations must concentrate on the development of culturally relevant AI algorithms, the enhancement of Arabic natural language processing, and the establishment of AI-driven mental health systems. By confronting these challenges and utilizing emerging technologies, Saudi Arabia has the potential to establish its status as a leading nation in medical services innovation, guaranteeing patient-centered, efficient, and accessible healthcare delivery. Recommendations must include augmenting data privacy and security, minimizing implementation expenses, surmounting resistance to change, enhancing interoperability, fortifying regulatory frameworks, addressing regional inequities, and investing in nascent technologies. Full article

Background: Cancer patients admitted to the bone marrow transplant (BMT) unit face life-threatening medical conditions. Consequently, their family members experience uncertainty, resulting in high levels of anxiety and depression (AD). Limited updates and communication from medical staff exacerbate these emotional burdens. To address these challenges, we developed a mobile health (mHealth) intervention, FamCarePlus, and evaluated its feasibility, usability, and efficacy. We hypothesized that the FamCarePlus application would demonstrate a high degree of feasibility and usability and would reduce AD compared to a control group relying solely on traditional communication through the nurses’ station. Methods: We employed a quasi-experimental pretest/posttest non-randomized, non-blinded self-report design over 3 weeks, with an experimental group (n = 10) using FamCarePlus and a control group (n = 9). We selected participants via convenience sampling using the electronic medical record to identify eligible patients and families, guided by inclusion and exclusion criteria. We used descriptive statistics and the Hospital Anxiety and Depression Scale (HADS) guidelines to analyze the data. Feasibility was defined by a retention rate > 80%, with usability testing using the System Usability Scale (SUS) and NASA Task Load Index (NASA-TLX) surveys. The HADS measured AD, comparing baseline to Week 3. Results: We met our feasibility criteria of >80%. All SUS and NASA scores were in the higher index, suggesting a significant degree of usability and low workload demand on participants. For efficacy, we compared baseline mean scores, with the experimental group reporting lower AD levels at Week 1 (41.9% and 27.8%, respectively) than the control group (55.2% and 34.2%, respectively). From Week 1 to Week 3, the percentage change showed an 8.6% decrease in anxiety in the experimental group, compared to a 12.8% decrease in anxiety in the control group. These results were consistent when analyzed according to HADS guidelines. Conclusions: The findings of this study provide preliminary evidence that the FamCarePlus intervention is feasible and usable, while also demonstrating that its use may be associated with a sustained reduction in AD levels among family members of patients admitted to the BMT unit. These outcomes underscore the potential of digital interventions to address disparities in patient health information access and psychosocial support. Full article

Heterogeneous electronic medical record (EMR) systems and institution-specific data structures continue to limit interoperability and large-scale utilization of healthcare data. Although Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) has been adopted as an international standard, existing conversion approaches often require extensive preprocessing, high implementation costs, or deep system-specific expertise, restricting their applicability, particularly in small and medium-sized hospitals. To address these constraints, we propose a lightweight EMR-to-HL7 FHIR conversion library optimized for small- and medium-sized healthcare providers that operate with limited system resources. Methods: The library adopts a modular architecture comprising data preprocessing, reference management, structural transformation using transform maps, terminology translation, and validation modules. The proposed approach was implemented using the HL7 Application Programming Interface (HAPI) FHIR and evaluated with anonymized EMR data extracted from multiple hospitals in South Korea, with performance and validation results compared against a conventional HAPI FHIR client-based conversion method. Results: This study proposes a standardized FHIR-based medical data conversion library that enables the efficient transformation of diverse EMR data structures into interoperable FHIR. The proposed library achieved approximately 30% lower single-request conversion latency compared to a conventional HAPI FHIR client-based conversion pipeline under identical hardware and runtime conditions. Conclusions: The proposed conversion method provides a lightweight and adaptable solution for EMR-to-FHIR transformation, improving interoperability with reduced implementation effort and supporting scalable medical data exchange across diverse healthcare environments. Full article

Background/Objectives: Cigarette smoking is a well-established risk factor for periodontal tissue damage caused by oxidative stress and increased proteolytic activity. Electronic cigarettes (e-cigarettes), marketed as less harmful alternatives, deliver nicotine and reactive compounds that may similarly disrupt periodontal health. However, their molecular effects on clinically healthy periodontal tissues remain unclear. This study aimed to compare oxidative stress-related and matrix-degradative biomarkers in the gingival crevicular fluid (GCF) of cigarette smokers (CS), e-cigarette (EC) users, and non-smokers (NS), and to examine the relationships among these markers. Methods: Sixty individuals, who were systemically and periodontally healthy (20 CS, 20 EC, and 20 NS), were examined. Clinical parameters, including probing depth (PD), clinical attachment level (CAL), plaque index (PI), and bleeding on probing (BOP), were recorded. GCF samples were analyzed for reactive oxygen species (ROS), matrix metalloproteinase-9 (MMP-9), and forkhead box protein O-1 (FOXO-1) using ELISA. Initial group comparisons were descriptive, followed by analysis of covariance (ANCOVA) to adjust for age; PI and PD were included as covariates in separate models. Correlations were assessed using Spearman’s analysis. Results: PD was significantly higher in both EC users and CS compared with NS (p = 0.022). MMP-9 levels were significantly higher in CS than in EC users and NS (p < 0.05), while FOXO-1 concentrations were significantly lower in CS compared with NS (p = 0.0227). ROS levels did not differ significantly among groups (p > 0.05). After adjustment for age, PI, or PD, group differences in MMP-9 and FOXO-1 remained statistically significant, whereas ROS levels remained comparable. FOXO-1 demonstrated positive correlations with ROS and MMP-9 within exposure groups; these associations were considered exploratory. Conclusions: In this cross-sectional study, CS and EC use were associated with altered matrix-regulatory biomarker profiles in clinically healthy periodontal tissues, independent of age and periodontal indices. Causal or temporal inferences cannot be drawn, and longitudinal studies are needed to clarify the long-term periodontal implications of these findings. Full article

The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments presents a fundamental algorithmic paradox: while generic foundation models possess vast reasoning capabilities, they often lack the precise, protocol-driven domain knowledge required for safe orthopedic decision support. This review provides a structured synthesis of the emerging algorithmic frameworks required to build modern clinical AI assistants. We deconstruct current methodologies into their core components: large-language-model adaptation, multimodal data fusion, and standardized data interoperability pipelines. Rather than proposing a single proprietary architecture, we analyze how recent literature connects specific algorithmic choices such as the trade-offs between full fine-tuning and Low-Rank Adaptation to their computational costs and factual reliability. Furthermore, we examine the theoretical architectures required for ‘agentic’ capabilities, where AI systems integrate outputs from deep convolutional neural networks and biosensors. The review concludes by outlining the unresolved challenges in algorithmic bias, security, and interoperability that must be addressed to transition these technologies from research prototypes to scalable clinical solutions. Full article

Background/Objectives: Federated learning (FL) offers a way for healthcare institutions to collaboratively train machine learning models without sharing sensitive patient data. This systematic review aims to comprehensively synthesize the ethical dimensions of FL in healthcare, integrating privacy preservation, algorithmic fairness, governance, and equitable access into a unified analytical framework. The application of FL in healthcare between January 2020 and December 2024 is examined, with a focus on ethical issues such as algorithmic fairness, privacy preservation, governance, and equitable access. Methods: Following PRISMA guidelines, six databases (PubMed, IEEE Xplore, Web of Science, Scopus, ACM Digital Library, and arXiv) were searched. The PROSPERO registration is CRD420251274110. Studies were selected if they described FL implementations in healthcare settings and explicitly discussed ethical considerations. Key data extracted included FL architectures, privacy-preserving mechanisms, such as differential privacy, secure multiparty computation, and encryption, as well as fairness metrics, governance models, and clinical application domains. Results: Out of 3047 records, 38 met the inclusion criteria. The most popular applications were found in medical imaging and electronic health records, especially in radiology and oncology. Through thematic analysis, four key ethical themes emerged: algorithmic fairness, which addresses differences between clients and attributes; privacy protection through formal guarantees and cryptographic techniques; governance models, which emphasize accountability, transparency, and stakeholder engagement; and equitable distribution of computing resources for institutions with limited resources. Considerable variation was observed in how fairness and privacy trade-offs were evaluated, and only a few studies reported real-world clinical deployment. Conclusions: FL has significant potential to promote ethical AI in healthcare, but advancement will require the development of common fairness standards, workable governance plans, and systems to guarantee fair benefit sharing. Future studies should develop standardized fairness metrics, implement multi-stakeholder governance frameworks, and prioritize real-world clinical validation beyond proof-of-concept implementations. Full article

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Diagnostic errors have been a critical concern in healthcare, leading to substantial financial burdens and serious threats to patient safety. The Improving Diagnosis in Health Care report by the National Academies of Sciences, Engineering, and Medicine (NASEM) defines diagnostic errors, focusing on accuracy, timeliness, and communication, which are influenced by clinical knowledge and the broader healthcare system. This review aims to integrate existing literature on diagnostic error from a systems-based perspective and examine the factors across various domains to present a comprehensive picture of the topic. A narrative literature review was structured upon the Systems Engineering Initiative for Patient Safety (SEIPS) model that focuses on six domains central to the diagnostic process: Diagnostic Team Members, Tasks, Technologies and Tools, Organization, Physical Environment, and External Environment. Studies on contributing factors for diagnostic error in these domains were identified and integrated. The findings reveal that the effectiveness of diagnostics is influenced by complex, interconnected factors spanning all six SEIPS domains. In particular, socio-behavioral factors, such as team communication, cognitive bias, and workload, and environmental pressures, stand out as significant but difficult-to-capture contributors in traditional and commonly used data resources like electronic health records (EHRs), which limits the scope of many studies on diagnostic errors. Factors associated with diagnostic errors are often interconnected across healthcare system stakeholders and organizations. Future research should address both technical and behavioral elements within the diagnostic ecosystem to reduce errors and enhance patient outcomes. Full article

Background/Objectives: Despite high vaccination coverage, influenza remains a public health concern in South Korea, particularly in older adults. Continuous evaluation of vaccine effectiveness (VE) is essential to optimize immunization strategies. Methods: This study evaluated seasonal influenza VE for preventing laboratory-confirmed influenza using a test-negative design through a hospital-based influenza surveillance system in South Korea from 1 November 2024, to 30 April 2025. Demographic and clinical information was collected through questionnaire surveys and electronic medical records. Influenza was diagnosed using rapid antigen tests (RATs) and reverse transcription polymerase chain reaction (RT-qPCR), and vaccine effectiveness was analyzed using multivariable logistic regression. Results: In total, 3954 participants were included, with 1977 influenza-positive cases and 1977 test-negative controls. Influenza A and B accounted for 93.1% and 7.0% of cases, respectively. The adjusted overall VE was 20.4% (95% confidence interval [CI], 8.2–30.9; p = 0.002). VE was higher in adults aged 50–64 years (46.8%) than in those aged ≥65 years (18.8%). VE was 19.9% against influenza A and 45.7% against A/H3N2. VE was higher among individuals tested using RT-qPCR than among those tested using RATs (21.5% vs. 15.7%), and was also greater during the early period than during the late period (20.5% vs. 11.4%). Vaccination did not reduce influenza-associated hospitalization risk (VE, 17.3%; 95% CI, −9.3 to 37.4). A significant reduction in hospitalization risk was observed in adults aged 50–64 years (VE, 46.8%), with no significant benefit in those aged ≥65 years. Conclusions: The 2024–2025 seasonal influenza vaccine provided moderate protection against laboratory-confirmed influenza in adults, with higher effectiveness in those aged 50–64 years. Full article

Background: The persistently low 5-year survival rate for pancreatic cancer (PC) underscores the critical need for early detection. However, population-wide screening remains impractical. Artificial Intelligence (AI) models using electronic health record (EHR) data offer a promising avenue for pre-symptomatic risk stratification. Objective: To systematically review and meta-analyze the performance of AI models for PC prediction based exclusively on structured EHR data. Methods: We systematically searched PubMed, MedRxiv, BioRxiv, and Google Scholar (2010–2025). Inclusion criteria encompassed studies using EHR-derived data (excluding imaging/genomics), applying AI for PC prediction, reporting AUC, and including a non-cancer cohort. Two reviewers independently extracted data. Random-effects meta-analysis was performed for AUC, sensitivity (Se), and specificity (Sp) using R software version 4.5.1. Heterogeneity was assessed using I² statistics and publication bias was evaluated. Results: Of 946 screened records, 19 studies met the inclusion criteria. The pooled AUC across all models was 0.785 (95% CI: 0.759–0.810), indicating good overall discriminatory ability. Neural Network (NN) models demonstrated a statistically significantly higher pooled AUC (0.826) compared to Logistic Regression (LogReg, 0.799), Random Forests (RF, 0.762), and XGBoost (XGB, 0.779) (all p < 0.001). In analyses with sufficient data, models like Light Gradient Boosting (LGB) showed superior Se and Sp (99% and 98.7%, respectively) compared to NNs and LogReg, though based on limited studies. Meta-analysis of Se and Sp revealed extreme heterogeneity (I² ≥ 99.9%), and the positive predictive values (PPVs) reported across studies were consistently low (often < 1%), reflecting the challenge of screening a low-prevalence disease. Conclusions: AI models using EHR data show significant promise for early PC detection, with NNs achieving the highest pooled AUC. However, high heterogeneity and typically low PPV highlight the need for standardized methodologies and a targeted risk-stratification approach rather than general population screening. Future prospective validation and integration into clinical decision-support systems are essential. Full article

Background/Objectives: Blood thinners (anticoagulants) remain the first line pharmacotherapy for the management of cardiovascular and thromboembolic disorders. The increased utilization of polypharmacy, likely driven by the greater burden of comorbidities, elevates the risk of potential drug–drug interactions (pDDIs) and creates a significant challenge in anticoagulant management. The aim of the study was to assess the prescribing trend and impact of polypharmacy and pDDIs in patients receiving anticoagulant drug therapy in a public hospital providing secondary care. Methods: A cross-sectional observational study was undertaken between January–June 2023. Data from electronic medical records of prescriptions for anticoagulants were collected, analyzed for prescribing patterns, and checked for pDDIs using Micromedex database 2.0^®. Utilizing binary logistic regression, the relationship between polypharmacy and sociodemographic factors was assessed. Multivariate logistic regression analysis served to uncover determinants linked to pDDIs. Results: Of the total 130 patients, females were predominant (58.46%), with a higher prevalence among those aged 61–90 years. Atrial fibrillation emerged as the main clinical reason and apixaban (51.53%) ranked as the top prescribed anticoagulant in our cohort. Among the 766 pDDIs identified, the majority [401 (52.34%)] were categorized as moderate in severity. Polypharmacy was strongly linked to age (p = 0.001), the Charlson comorbidity index (CCI) (p = 0.040), and comorbidities (p = 0.005) in the binary logistic regression analysis. In the multivariable analysis, the number of medications remain a strong predictor of pDDIs (adjusted OR: 30.514, p = 0.001). Conclusions: Polypharmacy and pDDIs were exhibited in a significant segment of cohort receiving anticoagulant therapy, with strong correlations to age, CCI, comorbidities, and the number of medications. A multidimensional approach involving collaboration among healthcare providers assisted by clinical decision support systems can help optimize the management of polypharmacy, minimize the risks of pDDIs, and ultimately enhance health outcomes. Full article

Epileptic seizures arise from abnormally synchronized neural activity and remain a major global health challenge, affecting more than 50 million people worldwide. Despite advances in pharmacological interventions, a significant proportion of patients continue to experience uncontrolled seizures, underscoring the need for alternative neuromodulation strategies. Rhythmic neural entrainment has recently emerged as a promising mechanism for disrupting pathological synchrony, but most existing systems rely on complex analog electronics or high-power stimulation hardware. This study investigates a proof-of-concept digital custom-designed chip that generates a stable 6 Hz oscillation capable of imposing a stable rhythmic pattern onto digitized seizure-like EEG dynamics. Using a publicly available EEG seizure dataset, we extracted and averaged analog seizure waveforms, digitized them to emulate neural front-ends, and directly interfaced the digitized signals with digital output recordings acquired from the chip using a Saleae Logic analyser. The chip’s pulse train was resampled and low-pass-reconstructed to produce an analog 6 Hz waveform, allowing direct comparison between seizure morphology, its digitized representation, and the entrained output. Frequency-domain and time-domain analyses demonstrate that the chip imposes a narrow-band 6 Hz rhythm that overrides the broadband spectral profile of seizure activity. These results provide a proof-of-concept for low-power digital custom-designed entrainment as a potential pathway toward simplified, wearable neuromodulation device for future healthcare diagnostics. Full article

As new technologies, such as electronic medical records (EMRs), are introduced into healthcare services, we need to consider how they may be incorporated into simulated environments, so as to maintain and enhance authenticity and learning opportunities. While EMRs have revolutionised clinical practice, many education settings continue to rely on paper-based documentation in simulation, creating a widening gap between educational environments and real-world clinical workflows. This disconnect limits learners’ ability to engage authentically with the tools and resources that underpin contemporary healthcare, impeding the transfer of knowledge to the clinical environment. This practice-based commentary draws on institutional experience from a large, multi-disciplinary simulation-based education facility that explored approaches to integrating EMRs into simulation-based education. It describes the decision points and efforts made to integrate an EMR into simulation-based education and concludes that while genuine EMR systems increase fidelity, their technical rigidity and data governance constraints reduce authenticity. To overcome this, Adelaide Health Simulation adopted an academic EMR (AEMR), a purpose-built digital platform designed for education. The AEMR maintains the functional realism of clinical systems while offering the pedagogical flexibility required to control data, timelines, and learner interactions. Drawing on this experience, this commentary highlights how authenticity in simulation-based education is best achieved not through technological replication alone, but through deliberate use of technologies that align with clinical realities while supporting flexible, learner-centred design. Purpose-built AEMRs exemplify how digital tools can enhance both fidelity and authenticity, fostering higher-order thinking, clinical reasoning, and digital fluency essential for safe and effective contemporary healthcare practice. Here, we argue that advancing simulation-based education in parallel with health service innovations is required if we want to adequately prepare learners for contemporary clinical practice. Full article

Background: Glaucoma is an age-related optic neuropathy frequently accompanied by systemic comorbidities. Vitamin D deficiency (VDD) has been associated with cardiovascular and renal diseases in the general population, yet its relationship with long-term systemic outcomes in glaucoma remains unclear. This study evaluated the association between baseline vitamin D status and subsequent mortality and cardiorenal events in patients with primary glaucoma. Methods: We conducted a retrospective cohort study using deidentified electronic health records from the TriNetX U.S. Collaborative Network, a federated network of participating healthcare organizations. Adults (≥18 years) with incident primary glaucoma (2005–2020) and a serum 25-hydroxyvitamin D (25(OH)D) test within 12 months prior to diagnosis were categorized as VDD (<30 ng/mL) or vitamin D adequacy (VDA; ≥30 ng/mL). After 1:1 propensity score matching across 47 demographic, clinical, medication, and laboratory variables, 11,855 patients per group were followed for up to 5 years. Outcomes included all-cause mortality, major adverse cardiovascular events (MACE), acute kidney injury (AKI), and renal function decline (eGFR < 60 mL/min/1.73 m²). Analyses incorporated Kaplan–Meier curves, Cox models, landmark tests, sensitivity analyses, and competing risk methods. Results: Among the 35,100 eligible patients, the matched cohorts demonstrated higher 5-year risks associated with VDD for all-cause mortality (HR 1.104; 95% CI 1.001–1.217), MACE (HR 1.151; 95% CI 1.078–1.229), and AKI (HR 1.154; 95% CI 1.056–1.261), whereas the risks of renal function decline did not differ (HR 0.972; 95% CI 0.907–1.042). Risk divergence emerged within the first year of follow-up and persisted through the 5-year observation period. Conclusions: In patients with primary glaucoma, vitamin D deficiency was associated with higher long-term risks of mortality and cardiorenal complications, but not renal function decline. Taken together, the results are consistent with vitamin D status serving as a marker of broader systemic vulnerability in glaucoma and highlight the need for prospective studies to further clarify its prognostic significance. Full article

A sharing framework based on Zero-Knowledge Proof (ZKP) and Proxy Re-encryption (PRE) technologies offers a promising solution for sharing Student Electronic Academic Records (SEARs). As core credentials in the education sector, student records are characterized by strong identity binding, the need for long-term retention, frequent cross-institutional verification, and sensitive information. Compared with electronic health records and government archives, they face more complex security, privacy protection, and storage scalability challenges during sharing. These records not only contain sensitive data such as personal identity and academic performance but also serve as crucial evidence in key scenarios such as further education, employment, and professional title evaluation. Leakage or tampering could have irreversible impacts on a student’s career development. Furthermore, traditional blockchain technology faces storage capacity limitations when storing massive academic records, and existing general electronic record sharing solutions struggle to meet the high-frequency verification demands of educational authorities, universities, and employers for academic data. This study proposes a dedicated sharing framework for students’ electronic academic records, leveraging PRE technology and the distributed ledger characteristics of blockchain to ensure transparency and immutability during sharing. By integrating the InterPlanetary File System (IPFS) with Ethereum Smart Contract (SC), it addresses blockchain storage bottlenecks, enabling secure storage and efficient sharing of academic records. Relying on optimized ZKP technology, it supports verifying the authenticity and integrity of records without revealing sensitive content. Furthermore, the introduction of gate circuit merging, constant folding techniques, Field-Programmable Gate Array (FPGA) hardware acceleration, and the efficient Bulletproofs algorithm alleviates the high computational complexity of ZKP, significantly reducing proof generation time. The experimental results demonstrate that the framework, while ensuring strong privacy protection, can meet the cross-scenario sharing needs of student records and significantly improve sharing efficiency and security. Therefore, this method exhibits superior security and performance in privacy-preserving scenarios. This framework can be applied to scenarios such as cross-institutional academic certification, employer background checks, and long-term management of academic records by educational authorities, providing secure and efficient technical support for the sharing of electronic academic credentials in the digital education ecosystem. Full article