Sentiment analysis has been of long-standing interest in psychotherapy research. Recently, the Transformer deep learning architecture has produced text-based sentiment analysis models that are highly accurate and context-aware. These models have been explored as proxies for emotion measurement instruments in psychotherapy, but not investigated as stand-alone psychometric tools. Using proposed utterance-level and session-level sentiment features […]
Quantum-SpinalNet: a hybrid deep learning approach for mammographic breast cancer detection
IntroductionBreast cancer diagnosis in mammograms remains challenging due to limitations in preprocessing, accurate differentiation of benign and malignant cases, and precise tumor segmentation.MethodsWe propose Quantum-SpinalNet, a hybrid deep learning model combining Swin ResUNet3+ for tumor segmentation with a Deep Quantum Neural Network (DQNN) and SpinalNet for classification. Preprocessing involves CEAMF-based denoising, Z-score normalization, and context-aware […]
Navigating ethical, regulatory, and implementation barriers to AI in healthcare: pathways toward inclusive digital health in low-resource settings—a scoping review
BackgroundArtificial intelligence (AI) has the potential to revolutionize healthcare delivery in low- and middle-income countries (LMICs), yet its rapid adoption raises complex ethical, regulatory, and implementation challenges. This review investigates these barriers and identifies emerging strategies that support equitable and inclusive AI deployment in resource-limited settings.MethodsFollowing the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines, a […]
Exploring plausible futures for artificial intelligence in rural healthcare: insights from participatory foresight methods
BackgroundArtificial intelligence (AI) has the potential to transform rural healthcare delivery through automated monitoring, personalised care, and virtual support. Yet the future pathways for AI in rural contexts remain underexplored. Most AI applications are developed in urban-centric environments with limited consideration for infrastructure constraints, workforce realities, and sociocultural dynamics that shape rural healthcare delivery.MethodsThis study […]
ChatGPT for diabetes education: potential, accuracy, and accessibility in patient support
BackgroundDiabetes mellitus is a chronic metabolic disease with rising global prevalence. Adequate patient education is essential to encourage self-management and reduce complications. Artificial intelligence applications such as ChatGPT have emerged as potential supplementary resources for patient education alongside the broader integration of technology in healthcare.MethodsA cross-sectional evaluation was conducted using ten frequently asked questions (FAQs) […]
ArcMAP – ML assisted medical concept mapping to accelerate NHS data standardization
The increasing use of electronic health records (EHRs) for real-world evidence (RWE) studies is hindered by substantial heterogeneity in data collection practices and local coding schemes across healthcare providers. Data standardization—particularly the mapping of locally defined medical concepts to standardized vocabularies—is therefore a critical but labour-intensive step, traditionally relying on extensive manual review by clinical […]
A framework for generative AI-driven extraction of clinical user needs in pediatric device development
IntroductionGenerative artificial intelligence (GenAI) is becoming an important tool in medical product development. A main component of this development includes annotating, summarizing, and extracting key insights from expert interviews to identify clinical pain points and curate device requirements. These tasks are time- and labor-intensive, resulting in increased administrative burden and reduced efficiency. As a result, […]
Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review
Medication non-adherence affects 40%–50% of chronic disease patients globally, causing preventable morbidity and substantial healthcare costs. Traditional adherence monitoring approaches are retrospective and reactive, limiting timely intervention. Artificial intelligence and machine learning offer novel approaches for prospective adherence risk prediction, enabling anticipatory, resource-efficient interventions. This narrative review synthesizes current evidence on AI-based non-adherence prediction across […]
Clustering-Enhanced Domain Adaptation for Cross-Domain Intrusion Detection in Industrial Control Systems
arXiv:2604.12183v1 Announce Type: cross Abstract: Industrial control systems operate in dynamic environments where traffic distributions vary across scenarios, labeled samples are limited, and unknown attacks frequently emerge, posing significant challenges to cross-domain intrusion detection. To address this issue, this paper proposes a clustering-enhanced domain adaptation method for industrial control traffic. The framework contains two key […]
Curvelet-Based Frequency-Aware Feature Enhancement for Deepfake Detection
arXiv:2604.12028v1 Announce Type: cross Abstract: The proliferation of sophisticated generative models has significantly advanced the realism of synthetic facial content, known as deepfakes, raising serious concerns about digital trust. Although modern deep learning-based detectors perform well, many rely on spatial-domain features that degrade under compression. This limitation has prompted a shift toward integrating frequency-domain representations […]
Narrative over Numbers: The Identifiable Victim Effect and its Amplification Under Alignment and Reasoning in Large Language Models
arXiv:2604.12076v1 Announce Type: cross Abstract: The Identifiable Victim Effect (IVE) $-$ the tendency to allocate greater resources to a specific, narratively described victim than to a statistically characterized group facing equivalent hardship $-$ is one of the most robust findings in moral psychology and behavioural economics. As large language models (LLMs) assume consequential roles in […]
ResBM: Residual Bottleneck Models for Low-Bandwidth Pipeline Parallelism
arXiv:2604.11947v1 Announce Type: cross Abstract: Unlocking large-scale low-bandwidth decentralized training has the potential to utilize otherwise untapped compute resources. In centralized settings, large-scale multi-node training is primarily enabled by data and pipeline parallelism, two techniques that require ultra-high-bandwidth communication. While efficient methods now exist for decentralized data parallelism, pipeline parallelism remains the primary challenge. Recent […]