JMIR Medical Informatics

Regulatory frameworks such as the Belmont Report, the Common Rule, and the Declaration of Helsinki require informed consent to ensure participants understand a study’s purpose and can make voluntary decisions about their involvement. Regulations, including the General Data Protection Regulation (Regulation [EU] 2016/679), further emphasize that consent must be freely given and revocable without disadvantage. Although informed consent forms (ICFs) are intended to be clear and accessible, they have become increasingly lengthy and complex. Large language models (LLMs) offer the potential to navigate and interpret this complexity and have shown promise in biomedical information extraction tasks. However, their susceptibility to hallucinations limits their reliability in high-stakes settings. Retrieval-augmented generation (RAG) can mitigate such errors.

Pedigree mapping serves as the first step in genetic counseling. Traditional methods relying on manual pedigree drawing and information collection are inefficient and prone to errors.

Clinical temporal relation extraction (CTRE) is essential for reconstructing patient timelines from unstructured electronic health records (EHRs). However, the linguistic complexity of clinical notes and the high cost of expert annotation impede the development of large-scale training corpora. While large language models (LLMs) have transformed general natural language processing, their application to CTRE remains underexplored.

Accurate documentation of back pain is essential for effective diagnosis and treatment; however, conventional paper-based methods often lack precision and standardization, limiting their clinical utility. Digital pain drawings can improve consistency and integration; however, the literature continues to report variability in digital pain drawing methods, quantification approaches, body schemas, and implementation. Moreover, open access, anatomically labeled vector assets mapped to clinical terminology remain limited.

Clinical Quality Language (CQL) and Fast Healthcare Interoperability Resources (FHIR) support executable electronic clinical quality measures. Translating national claims–based indicators into CQL remains challenging because terminology, exclusions, and data semantics require adaptation. To our knowledge, Taiwan National Health Insurance (NHI) medical-center indicators have not previously been represented as an executable CQL library.

Pulmonary arterial hypertension (PAH) is a rare and progressive disease associated with substantial clinical heterogeneity and high health care costs. While machine learning (ML) has been increasingly applied to predict clinical outcomes, approaches that integrate clinical prediction with downstream economic modeling remain limited.

Social media offers valuable information for public health surveillance, but detecting valid symptom expressions in noisy and informal text remains challenging. Fine-tuned language models can achieve strong test accuracy while relying on spurious patterns, limiting their reliability in real-world applications.

Purpose-built AI well-being tools—designed with clinical safeguards for everyday emotional difficulties such as stress, grief, and relationship conflict—represent a growing category of digital mental health support. When such tools are removed from the market, users must navigate alternatives on their own; however, no empirical data exist on where they actually turn.

Health literacy (HL) is a major factor associated with population health. However, no visualized predictive model has been developed to identify populations with low HL in a cost-effective manner.

Clinical documentation demands a significant share of surgeons’ time and can contribute to documentation-related burden and burnout. While electronic health records (EHRs) offer accessibility and completeness, they also impose a substantial documentation burden. Ambient AI scribe solutions aim to reduce this burden by capturing patient-clinician dialogue in real time to automate clinical documentation. However, their specific impact on surgical practice remains under-studied.

Trauma registries are crucial for evaluating the prognosis of injuries and the quality of trauma care. However, the calculation of Injury Severity Score (ISS) remains a predominantly manual process, which is labor-intensive, potentially error-prone, and difficult to scale. Automating this process is a critical step toward enhancing the efficiency and quality of trauma registry data.

Small bowel capsule endoscopy (SBCE) is widely used to evaluate small bowel involvement in Crohn disease (CD); however, manual Lewis score calculation is time-consuming and susceptible to interobserver variability.