JMIR Medical Informatics

Medical discharge letters are critical for continuity of care but often lack clarity and personalization, making it difficult for health care providers to retrieve essential information. While large language models (LLMs) offer potential for automating summary generation, their effectiveness depends heavily on the quality and contextual relevance of the prompts used.

Accurate segmentation of cartilage from magnetic resonance imaging (MRI) is crucial for the diagnosis and surgical planning of knee osteoarthritis. However, manual segmentation is time-consuming, and conventional computed tomography–based surgical systems are limited by their inability to visualize cartilage.

Nursing care systems face significant challenges due to demographic changes, a workforce shortage, and rising demand for care services. Digital assistive technologies offer potential to address these challenges, but systematic and standardized nursing data are essential to evaluate both innovations and broader care processes. The Nursing Minimum Data Set (NMDS) provides a foundational framework for capturing structured information on nursing care, yet there is no international consensus on its core content, development, and practical use.

Scars and keloids impose significant physical and psychological burdens on patients, often leading to functional limitations, cosmetic concerns, and mental health issues such as anxiety or depression. Patients increasingly turn to online platforms for information; however, existing web-based resources on scars and keloids are frequently unreliable, fragmented, or difficult to understand. Large language models such as GPT-4 show promise for delivering medical information, but their accuracy, readability, and potential to generate hallucinated content require validation for patient education applications.

Adverse drug reactions pose a serious threat to health care, leading to patient harm and substantial economic burden. Dashboards for drug safety surveillance are a valuable tool to tackle it.

Electronic health records are essential for advancing research aimed at improving clinical outcomes. However, stringent data protection and privacy concerns severely limit the accessibility and use of real clinical data, particularly within Child and Adolescent Mental Health Services (CAMHS) involving vulnerable young individuals. This challenge can be effectively addressed through synthetic data generation, which safeguards individual privacy while facilitating comprehensive analyses of clinical information.

Individual-level behavioral interventions are designed to improve health behaviors and manage noncommunicable diseases. Neighborhood geo-referenced contexts (NGRCs) significantly impact the success of these interventions. Integrating NGRC data into health information systems (HISs), including electronic medical records (EMRs), electronic health records (EHRs), and personal health records (PHRs), can enhance personalized NGRC-focused behavioral interventions and improve health outcomes. Despite the potential benefits, there is a notable gap in the literature about NGRC-focused behavioral interventions using HISs.

Machine learning models are increasingly used to predict patients at risk of high health care usage for targeted interventions.

Clinical decision support systems (CDSSs) have shown promise in improving diagnosis in primary care, particularly for chronic diseases. The SATURN (Smart Physician Portal for Patients With Unclear Disease) project developed a CDSS prototype for primary care in Germany that uses artificial intelligence to reduce diagnostic uncertainty in unclear and rare diseases. It generates recommendations based on clinical data from university hospitals stored in a standardized common data model. However, integrating primary care data in Germany remains challenging due to the use of country-specific vocabularies and heterogeneous data structures. Therefore, integration of medical concepts into general practitioners’ user interfaces (UIs) and improved workflow design is needed.

Nephrolithiasis affects approximately 15% of the population and often remains undetected in asymptomatic individuals. Current diagnostic approaches rely on imaging tools, such as ultrasound or computed tomography, which are costly, operator dependent, or involve radiation, making them unsuitable for large-scale screening. A standardized, practical, and low-cost screening strategy for early identification of clinically significant kidney stones is still lacking.

Artificial intelligence (AI) is increasingly applied to healthcare, yet concerns about fairness persist, particularly in relation to sociodemographic disparities. Prior studies suggest that socioeconomic status (SES) and sex may influence AI model performance, potentially affecting groups that are historically underserved or understudied.

Coal workers’ pneumoconiosis (CWP) is the most prevalent occupational disease that causes irreversible lung damage. Early prediction of CWP is the key to blocking the irreversible process of pulmonary fibrosis. The prediction of CWP based on imaging data and biomarker detection is constrained due to high cost and poor convenience.