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  • New
  • Research Article
  • 10.1007/s12553-025-01045-8
Automatic segmentation based watermarking technique for medical images in e-healthcare
  • Jan 31, 2026
  • Health and Technology
  • Solihah Gull + 2 more

  • New
  • Open Access Icon
  • Research Article
  • 10.1007/s12553-025-01046-7
Association between chronic disease, socioeconomic position and digital health literacy: a danish population-based survey
  • Jan 28, 2026
  • Health and Technology
  • Lise Lind Kristensen + 5 more

Abstract Purpose There is social inequity in the distribution of digital health literacy (DHL), and patients with chronic disease have also been shown to have lower general health literacy. The study aimed to examine the associations between low socioeconomic status (SES) [measured by self-reported educational level], chronic disease and low DHL and whether chronic disease modifies the effect between SES and DHL. Methods Cross-sectional study from the Danish HLS 19 survey, a stratified random sample of 3,644 respondents from the Danish population. A questionnaire collected information on educational level and the prevalence of 13 specific chronic diseases. The number of self-reported chronic diseases were counted. DHL was measured using the HLS 19 digital health literacy scale from the WHO-Action Network M-POHL. Linear regression models were used to estimate the association between chronic disease and DHL and between education level and DHL, adjusted for sex and age. The potential effect modification between SES, chronic disease and DHL was analyzed. Results Having two or more chronic diseases was associated with a lower level of DHL (beta (95%CI)) (-3.88 (-5.68; -2.08)) compared to having no chronic disease. Both bachelor-level education (6.49 (4.68; 8.30)) and master-level or above (9.39 (7.43;11.36)) were associated with a higher DHL than respondents with vocational education. No statistically significant interaction was found between chronic diseases and education level on the level of DHL. Conclusions SES and the presence of chronic diseases were independently associated with DHL but did not interact.

  • Research Article
  • 10.1007/s12553-025-01043-w
Mind the gap: disparities by preferred language in a novel, large-scale remote symptom monitoring program for cancer patients
  • Jan 3, 2026
  • Health and Technology
  • Allison Lipitz-Snyderman + 10 more

  • Research Article
  • 10.1007/s12553-025-01044-9
Autopilot for healthcare providers automatization methodologies to reduce workload and medical mistakes
  • Dec 27, 2025
  • Health and Technology
  • Ariel Braverman

  • Research Article
  • 10.1007/s12553-025-01029-8
Assessment of myocardial fibrosis using fusion models of echocardiographic radiomics and deep learning: Animal feasibility study
  • Dec 8, 2025
  • Health and Technology
  • Yuling Li + 8 more

  • Research Article
  • 10.1007/s12553-025-01031-0
Systematic literature review on improving healthcare through proactive maintenance and fault detection in medical equipment
  • Dec 2, 2025
  • Health and Technology
  • Nor Atikah Mohamad Radzi + 2 more

  • Research Article
  • 10.1007/s12553-025-01026-x
Enhancing healthcare for patients with multiple chronic conditions using machine learning and medical specialist data: a scoping review
  • Nov 15, 2025
  • Health and Technology
  • Hidde Dijkstra + 5 more

  • Research Article
  • 10.1007/s12553-025-01028-9
The global landscape of joint departments in medical physics and biomedical engineering
  • Nov 13, 2025
  • Health and Technology
  • Reem Ahmad + 3 more

  • Research Article
  • 10.1007/s12553-025-01027-w
Prediction of elderly acute kidney injury (AKI) in intensive care units (ICU) based on machine learning model
  • Nov 3, 2025
  • Health and Technology
  • Yanhong Du + 6 more

  • Research Article
  • 10.1007/s12553-025-01025-y
Determination of the surface dose correction factors for various detectors in proton scanning therapy
  • Nov 3, 2025
  • Health and Technology
  • Pakjira Saruang + 2 more