Digital first primary care in NHS England: evaluating alignment with patient-centered care and implications for future practice

The Digital First Primary Care (DFPC) model, introduced by NHS England, aims to enhance healthcare accessibility and efficiency by leveraging digital tools such as telemedicine,

The economics of digitally integrated wellness services in heritage regions

Wellness tourism is among the fastest-growing segments of the global health economy, yet its development in Central Asian heritage regions remains constrained by fragmented service

Screening anxiety via contrastive autobiographical recall

IntroductionLanguage offers a low-burden and scalable pathway for digital anxiety screening, particularly in telehealth or repeated-monitoring settings where spontaneous speech may already be available. This

DFU-GCNet: a global context-enhanced inception network for robust and interpretable diabetic foot ulcer classification

IntroductionDiabetic foot ulcers (DFUs) are severe complications that cause frequent lower extremity amputations. Timely diagnosis is crucial for effective clinical management. Although deep learning approaches

Resources consumption and environmental impacts of the DYNAMIC digital health intervention aimed at improving quality of care for sick children in Tanzania: a life cycle assessment

BackgroundHealth systems contribute to an important part of planetary boundaries overshoot, the effect of its rapid digitalization being however not well known. DYNAMIC is a

Text-guided few-shot liver and tumor segmentation

June 1, 2026

IntroductionHigh-precision liver and tumor segmentation is a cornerstone of digital oncology, yet its clinical deployment remains constrained by two persistent challenges: the scarcity of pixel-level annotations and severe performance degradation under cross-center domain shift. Although few shot learning offers a promising direction for data-efficient modeling, existing approaches relying solely on visual similarity often fail to generalize across heterogeneous clinical environments.MethodIn this study, we propose a text-guided few-shot segmentation framework that integrates clinical semantic information as an explicit inductive bias to bridge the gap between low-level pixels and high-level diagnostic reasoning. The framework consists of three synergistic modules: an Automated Semantic Generator that employs a large-scale vision-language model to encode clinically structured semantic descriptions into structured linguistic priors. A Text-Guided Gating (TGG) mechanism that adaptively modulates visual representations to filter scanner-dependent artifacts, and a Decoupled Prototype Learner that constructs unbiased class prototypes via per-image averaging and gradient detachment to address extreme class imbalance. Together, these components transform segmentation from a pure visual matching task into a knowledge guided reasoning process, ensuring both data efficiency and improved cross-dataset robustness across heterogeneous datasets.ResultsExperiments on LiTS and 3DIRCADb show that the method consistently outperforms state-of-the-art supervised, foundation-model-based, and few-shot baselines. On 3DIRCADb, compared to PANet, the strongest few-shot baseline, our method improves external liver Dice by 8.7 percentage points and external tumor Dice by 26.3 percentage points, effectively mitigating performance degradation in conventional supervised models.DiscussionThese results demonstrate that cross-modal semantic guidance enhances robust medical image segmentation under domain shift.

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