Hemodialysis patients are at high risk for ICU admission due to elevated mortality, cardiovascular disease, and infection rates. Traditional ICU scoring systems (e.g., APACHE-II, SOFA) demonstrate limited accuracy in this population. This study aimed to identify key risk factors and develop interpretable machine learning (ML) models for predicting ICU outcomes to enable early intervention.

Patient portals are now considered an integral part of medical care. The aim of this study was to design and evaluate a patient portal for individuals with human T-lymphotropic virus type 1 (HTLV-1) to provide information about their health status.

Epidemiological data on rare diseases (RDs) affect the accurate scientific assessment of these diseases and lead to many issues in policy-making, healthcare systems, and legislation. The coding system is crucial for accurately identifying and calculating the incidence rates of each RD. This study focuses on the effectiveness of collecting RD data via the ICD-11 and examines whether the ICD-11 can fully support RD statistics. The findings of this study should provide a foundation for replacing the ICD-10 with the ICD-11.

Chronic kidney disease (CKD) is a global health burden with low awareness among both patients and healthcare providers. Deep learning models (DLMs) have shown promise in interpreting electrocardiograms (ECGs) for various disease and may offer new opportunities for early CKD detection.

Cardiovascular magnetic resonance (CMR) offers state-of-the-art volume, function, fibrosis and oedema imaging. Quality assurance (QA) tasks, such as quantitative parameter reproducibility assessments, the evaluation of AI methods, and the assessment of trainees have become essential to CMR. However, the explainability of how qualitative differences impact quantitative differences remains underexplored. Our aim is to demonstrate a semi-automated QA tool, Lazy Luna's (LL) applicability to typical CMR QA application cases.

Explainable AI (XAI) techniques like SHAP provide valuable insights into machine learning model predictions by quantifying feature contributions. However, interpreting these quantitative outputs remains unintuitive for many clinicians, hindering their practical adoption in clinical decision-making. This exploratory feasibility study aims to propose and evaluate a prompting framework designed to guide large language models (LLMs) in generating consistent, clinically relevant explanations from SHAP values.

The early and accurate detection of eye diseases play a pivotal role in preventing vision loss and improving patients’ quality of life. It is therefore important to search for methods for improving this detection. It turns out that Artificial Intelligence (AI) has shown great promise for the detection task. However, AI models are often opaque and complex and as a result there has been a slow adoption for these models in clinical settings. In this paper Eye-XAI is introduced which provides an effective approach to the detection combining Explainable Artificial Intelligence (XAI) techniques with symptom analysis to enhance the transparency and interpretability of eye disease detection models. Our results demonstrate that Eye-XAI not only achieves high accuracy (99.11%) for eye disease detection but also provides transparent and interpretable insights into the diagnostic process. The adoption of Eye-XAI can therefore significantly enhance the early detection and management of eye diseases while empowering clinicians with a deeper understanding of its AI-based diagnostic recommendations. Furthermore, this approach promotes patient engagement by facilitating communication and trust between patients and their healthcare providers. Eye-XAI represents a major step towards the integration of XAI in ophthalmology, unlocking new possibilities for improved eye disease diagnosis and treatment.

Shared decision making (SDM) is highly relevant in oncology and cancer care, yet its application within multiple myeloma (MM) remains underexplored. This study aims to (1) investigate SDM implementation in MM clinical practice, (2) assess the role of various stakeholders next to haematologists in the SDM process, and (3) identify barriers and potential solutions to SDM implementation in MM care.

The Short Physical Performance Battery (SPPB) is a widely used assessment tool to evaluate lower extremity function in older adults. However, it requires clinical settings which may not be feasible in all circumstances. This study aimed to develop alternative methods for indirectly estimating SPPB scores using questionnaire responses related to functional abilities.

Determining the appropriate end-of-life (EOL) care model within a short time frame is challenging and requires extensive experience. To the best of our knowledge, no studies have developed automatic systems for identifying the hospice care models: hospice home (HHC), inpatient (HIC), and shared care (HSC). This study aimed to determine the optimal hospice care model for EOL patients with machine learning (ML) methods based on health assessment data.

Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small. To address this, data augmentation is one solution and is often used for imaging and time series data, but there are no evaluations on its potential benefits for tabular health data. Augmentation increases sample size and is seen as a form of regularization that increases the diversity of small datasets, leading them to perform better on unseen data.