JMIR Medical Informatics

There is increasing research on machine learning in predicting venous thromboembolism after joint arthroplasty, but the quality and clinical applicability of these models remain uncertain.

As health care delivery shifts toward value-based care, proactive strategies to close preventive care gaps are essential. However, patient engagement remains suboptimal due to logistical, behavioral, and socioeconomic barriers. Traditional outreach methods, such as phone calls, emails, and postal mail, have long been used, but emerging digital approaches, such as chatbot-based messaging, offer potential advantages in scalability and personalization. Their comparative effectiveness, however, remains underexplored.

The early detection of diseases is one of the tasks of general practice. AI (artificial intelligence)-based technologies could be useful to identify diseases at an early stage in general practices. As a good 90% of the population regularly consult a GP (general practitioner) during one year, this could increase the percentage of citizens who take part in meaningful screening measures.

Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospitalization, cognitive decline, and increased mortality. Early prediction of delirium is therefore critical for initiating timely interventions.

Exploring user satisfaction is crucial for enhancing and ensuring the sustainable development of mobile health (mHealth) apps, particularly in the fitness and weight management sectors. Analyzing user types and developing user profiles are valuable for understanding differences in satisfaction. However, prior research lacks a classification of user types based on self-management characteristics and an analysis of satisfaction disparities among these types.

General anesthesia comprises three essential components—hypnosis, analgesia, and immobility. Among these, maintaining an appropriate hypnotic state, or anesthetic depth, is crucial for patient safety. Excessively deep anesthesia may lead to hemodynamic instability and postoperative cognitive dysfunction, whereas inadequate anesthesia increases the risk of intraoperative awareness. Electroencephalography (EEG)–based monitoring has therefore become a cornerstone for evaluating anesthetic depth. However, processed EEG (pEEG) indices remain vulnerable to various sources of interference, including electromyographic activity, interindividual variability, and anesthetic drug effects, which can yield inaccurate numerical outputs.

AI scribes, software that can convert speech into concise clinical documents, have achieved remarkable clinical adoption at a pace rarely seen for digital technologies in healthcare. The reasons for this are understandable: the technology works well enough, it addresses a genuine pain point for clinicians, and it has largely sidestepped regulatory requirements. In many ways, clinical adoption of AI scribes has also occurred well ahead of robust evidence of their safety and efficacy. The papers in this theme issue demonstrate real progress in the technology and evidence of its benefit: documentation times are reported to decrease when using scribes, clinicians report feeling less burdened, and the notes produced are often of reasonable quality. Yet as we survey the emerging evidence base, there remains one outstanding and urgent unanswered question: Are AI scribes safe? We need to know the clinical outcomes achievable when scribes are used compared to other forms of note taking.

Harmful suicide content on the internet poses significant risks, as it can induce suicidal thoughts and behaviors, particularly among vulnerable populations. Despite global efforts, existing moderation approaches remain insufficient, especially in high-risk regions like South Korea, which has the highest suicide rate among OECD countries. Previous research has primarily focused on assessing the suicide risk of the authors who wrote the content rather than the harmfulness of content itself which potentially leads the readers to self-harm or suicide, highlighting a critical gap in current approaches. Our study addresses this gap by shifting the focus from assessing the suicide risk of content authors to evaluating the harmfulness of the content itself and its potential to induce suicide risk among readers.

Data linkage in pharmacoepidemiological research is commonly employed to ascertain exposure and outcomes, or to obtain more information about confounding variables. However, to protect patient confidentiality usually unique patient identifiers are not provided; thus, makes data linkage between various sources challenging. The Saudi Real-Evidence Researches Network (RERN) aggregates Electronic Health Records from various hospitals, which may require a robust linkage technique.

The Cox proportional hazards (CPH) model is a common choice for analyzing time to treatment interruptions in patients on antiretroviral therapy (ART), valued for its straightforward interpretability and flexibility in handling time-dependent covariates. Machine learning (ML) models have increasingly been adapted for handling temporal data, with added advantages of handling complex, non-linear relationships, large datasets, and provide clear practical interpretations.

Multiple instance learning (MIL) is widely used for slide-level classification in digital pathology without requiring expert annotations. However, even partial expert annotations offer valuable supervision; few studies have effectively leveraged this information within MIL frameworks.

This research letter summarizes early lessons from 4 enterprise implementations of artificial intelligence–enabled customer relationship management platforms in health care and describes governance practices associated with improvements in affordability, adherence, and access at program level.