AI-driven contrast mapping enhances voxel-level T2-MRI brain analysis, revealing phenotypic variations crucial for early diagnosis and neurodevelopmental profiling 

SDS significantly reduces type I and III collagen density in decellularized bovine pericardium, whereas H2O2 better preserves extracellular matrix integrity.

Improves early cerebral palsy detection by resolving pose occlusions through enhanced skeleton generation and feature extraction from infant movement videos.

This model offers an accurate, timely SCA detection solution, improving healthcare outcomes for Nilgiri tribes and vulnerable populations.

This study explores how system usage, satisfaction, and quality factors influence the effectiveness of HMIS in improving hospital performance and patient care.

This study demonstrates the potential of mobile applications to enhance safety audits in healthcare construction sites in Bangladesh.

PA-C-AI enhances angiogenesis prediction, enabling precise cancer therapies and personalized treatments, improving patient outcomes and survival rates.

This study determined the transformative impact of EdTech innovations in revolutionizing research productivity, elevating educational engagement, and enhancing clinical precision within applied medical sciences.

This study determines the ATC-CNN’s superior performance in real-time monitoring of cardiovascular patients revolutionizes care, enhancing access, accuracy, and clinical outcomes.

This study determined a novel approach combining SOM and IRNN with EHSO to enhance early detection of ovarian cancer, improving diagnostic accuracy and efficiency.

This study demonstrated the superior potential of Web-Text Sentiment Analysis in predicting cardiovascular disease over demographic-based models.

This research demonstrates a novel Mask R-CNN with ResNet-FPN framework for improved segmentation and classification of Diabetic Foot Ulcers, enhancing diagnostic accuracy.

This study presents a novel AR-based approach for accurate, non-invasive chronic wound evaluation, enhancing clinician-patient communication and care.

This study demonstrates Radiology-specific word encodings potential in improving the performance of radiology-focused BNLP tasks using diverse word embeddings.

Digitizing supply chains with Blockchain technology improves transparency, reduces costs, and ensures secure medication distribution, crucial for pharmaceutical safety.

Combining CNN and LSTM models with feature enhancement significantly improves heart disease prediction, offering precise and timely interventions.

This study demonstrates that integrating machine learning with decision support systems significantly improves prescription error detection, reducing medication errors and enhancing patient safety.

This study demonstrated advanced deep learning models improving ovarian cancer subtype classification, defining the way for precision medicine applications.

This study determined the most effective machine learning models for predicting risky pregnancies, aiding in early clinical interventions.

This study showed optimal decellularization methods for enhancing porosity and pore size in bovine pericardium scaffolds, crucial for pediatric heart surgery.

This study enhances healthcare with WBSN-PHMS, enabling continuous, real-time monitoring and early detection while optimizing energy use.

This study determined the multifaceted benefits of blockchain in enhancing the overall efficiency, effectiveness, and integrity of digital healthcare systems.

This study demonstrated how deep learning, particularly CNNs, improves melanoma detection accuracy, supporting earlier diagnosis and better patient outcomes.

Combining RBF and DBN improves real-time cardiovascular monitoring by enhancing feature extraction and classification accuracy, addressing limitations of conventional methods.

Early seizure prediction via EEG signals might enable timely intervention, potentially improving patient outcomes and reducing clinical risks.

This study determined an advanced deep learning model optimized for early and accurate cardiomyopathy classification, addressing issues like overfitting and high dimensionality in gene expression data.

This study determined leukemia diagnostics by leveraging EfficientNetB3's transfer learning, improving accuracy and efficiency in blast cell classification.

Evaluation of water sorption in various denture base materials, highlighting significant differences and implications for clinical durability and performance.

Precision medicine, microarray analysis, ensemble learning, and deep neural networks revolutionize lung cancer diagnosis, enhancing accuracy and treatment efficacy.

Early Breast Cancer diagnosis via data mining is crucial. Proposed methods achieve high accuracy, improving treatment outcomes for patients significantly.

Closing AI's reproducibility and fairness gaps is a precondition, not an afterthought, for trusting deep-learning imaging tools at the bedside.

Explains why highly accurate AI resistance models fail clinically, and outlines concrete validation, equity, and deployment fixes.

Artificial-intelligence risk models and digital twins could shift cardiology from reactive treatment toward proactive, individualized prevention — if bias, validation, and trust gaps are honestly addressed.

Wearable digital biomarkers show strong clinical validity but require standardized validation, interoperable data systems, and equitable design before routine adoption.

Organoid-on-chip systems merge biological fidelity with engineering precision, offering AI-augmented, human-relevant platforms positioned to reshape preclinical drug development.

This study shows how AI can change the way drugs are made and delivered, making healthcare faster, more personalized, and cheaper.

This study demonstrated the potential of AI to revolutionize cancer care through improved diagnostics, personalized treatment, and ethical implementation.

AI improves diagnosis, treatment planning, and patient outcomes by enabling early, accurate Alzheimer's disease identification using sophisticated data analysis.

This review clarifies how AI and digital technologies realistically improve emergency response, while identifying ethical, operational, and data challenges for responsible implementation.

Machine learning transforms healthcare through accurate diagnosis, tailored therapies, and predictive analytics, promoting a future of precision medicine. 

Machine learning improves healthcare efficiency; yet issues such as data privacy and bias must be resolved for ethical, scalable, and effective application.

IoT revolutionizes healthcare by enhancing patient monitoring, treatment, and accessibility while addressing challenges in efficiency, cost, and security.

This review discusses the use of gelatin sponges for safer, more effective, and affordable wound dressings for improved clinical outcomes.

Drone-delivered AEDs significantly reduce response times for OHCAs, especially in rural areas, improving survival and offering cost-effective EMS support.

AI and predictive analytics revolutionize emergency medicine by enhancing triage, decision-making, and education, despite ethical, technical, and trust challenges.

AI-driven insights into angiogenesis and inflammation can revolutionize disease prediction, biomarker discovery, and personalized therapeutic interventions.

This review assessed the advantages, limitations, and combined efficacy of radiology and OCT in dental diagnostics, enhancing imaging precision.

AI enhances early detection and risk prediction of HAIs, improving patient outcomes and reducing healthcare costs despite integration and trust barriers.

This review discusses the AI's transformative role in healthcare, focusing on patient care, diagnostics, early detection, and cost reduction.

This review discusses the AI to improve diagnostic accuracy in breast cancer screening, potentially reducing false positives and negatives and improving patient outcomes.

This study determined advanced AI techniques for accurate CKD diagnosis, enhancing medical decision-making through efficient data analysis methods.

Neuromuscular disorders significantly impair mobility and stability; advanced orthoses like SCKAFOs improve walking efficiency and knee stability, enhancing life quality.

Biomaterials significantly enhance tissue repair and regeneration, offering innovative solutions in modern medicine and improving patient outcomes.

The review discused the Healthcare Monitoring based IoT framework (HM-IoT) and determined the security rate and classification accuracy.

Enhancing medical image contrast improves diagnosis accuracy. COA-SCSO optimizes parameters efficiently, ensuring clearer images and better patient care outcomes.

The novel ECG analysis method improves detection accuracy, is vital for early diagnosis of CVD, and potentially saves lives, especially in underserved regions.

This study showed VGG-19 model with superior training speed, accuracy, and iteration efficiency, significantly enhancing Parkinson’s disease diagnosis from MRI images.

Heart disease, responsible for millions of deaths annually, necessitates early detection. Machine Learning offers efficient, cost-effective diagnostic tools. The proposed Wild Horse Optimizer (WHO) and SVM classifier enhance heart disease forecasting, addressing critical healthcare needs.

Innovative synthetic blood vessels offer promising solutions for cardiovascular diseases, providing safer, more accessible treatments for aging populations

The review explores an inclusive ML structure, vital for efficient CVD forecasting. The proposed model showcases superior accuracy, emphasizing the need for comprehensive predictive algorithms in cardiovascular health research.

Advanced integration of deep learning (AlexNet) and SVM-RBF for breast cancer diagnosis surpasses existing models, ensuring precise and reliable early detection.

Wound features impact healing; indicators like temperature, oxygen levels, and moisture influence the process. This review shows and effective at-home wound monitoring with biosensors.

This review shows an advanced machine learning method for efficient segmentation and classification of blood vessels in medical images, revolutionizing diagnostic precision.

A review of a correlation between physical activity, life satisfaction, and aging in the senior age group with Internet of Things (IoT) based Physical Activities (IoT-PA) model

A ML-TDI model for thyroid dysfunction identification, addressing underdiagnosis challenges and emphasizing the importance of comprehensive assessment in care.

An IoT-based system enhancing elderly care, emphasizing QOL, independence, and support, aiding healthcare development for seniors' well-being.

A review of polydopamine sensor in breast cancer diagnosis.

AI improves triage, diagnostics, and resource allocation in emergency departments, reducing errors and delays while supporting clinicians through enhanced decision-making.

Targeted ultrasound appears practical for the longitudinal molecular profiling of tumor angiogenesis and for the sensitive evaluation of therapy effects in vivo.