JMIR Formative Research

Large language models (LLMs) are increasingly used to generate differential diagnoses from clinical narratives. However, LLM-based diagnostic clinical decision support systems still lack a quantitative measure of how strongly a diagnosis is supported by the available case description. Conditional perplexity score quantifies how predictable a target text is given in a preceding context, with lower scores indicating greater predictability. We hypothesized that this concept can be adapted to diagnostic reasoning by treating the prediagnostic case description as the context and a diagnosis as the target text.

Long-term remote patient monitoring of weight, pulse, and blood pressure has been shown to significantly reduce mortality and hospitalization rates among patients with heart failure. Despite its proven effectiveness, maintaining patient engagement in remote monitoring programs remains challenging.

The integration of AI into intraoperative surgical imaging represents an emerging frontier in digital health. Despite advances in preoperative computed tomography (CT)–based surgical planning, real-time translation of imaging data into actionable intraoperative guidance remains limited by CT-to-body divergence—a fundamental information gap between preoperative digital models and the dynamic surgical field. This divergence, driven by lung deflation under anesthesia and positional changes, represents a critical digital-to-physical registration challenge that current preoperative imaging workflows fail to address in real time.

Clinical decision support systems (CDSSs) are widely implemented in hospitals, yet their uptake in nursing practice remains inconsistent. While electronic health records continuously capture nursing decision data, these data are rarely used to support structured quality improvement. Decision mining offers a novel approach by reconstructing and visualizing decision logic from clinical decision logs to support retrospective reflection and protocol evaluation rather than real-time alerting. Before such tools can be integrated into quality improvement programs, their acceptance by clinical users must be empirically examined.

As digital technologies become increasingly integrated into psychological practice, the demand for competencies in digital clinical psychology is growing. Although competency frameworks for digital clinical practice exist, validated instruments to assess these competencies remain scarce. In Sweden, psychology master’s students are now being offered digital psychology courses, increasing the need for instruments to measure intended improvements in knowledge and abilities. Using AI to assist with translation procedures can facilitate the adaptation of existing instruments to new national and cultural contexts.

Digital technologies in emergency medical services (EMS) have become crucial for patient outcomes and survival, and the integration of novel technologies drives rapid transformation within EMS. Understanding the acceptance and use of digital technology among EMS health care providers is paramount for implementation and effective use of novel digital tools. Currently, instruments assessing acceptance of technology are lacking within the Swedish EMS context.

Gamification, defined as the application of game design principles in nongame contexts, has garnered increasing attention for its potential to enhance motivation and engagement across various fields, including medical education and training. However, there is a lack of virtual reality (VR)–based simulations on pneumatic reduction of intussusception (PRI) in children in Brunei, thereby limiting practical training opportunities for medical students and surgical trainees.

In the digital era, smartphone technology has significantly advanced ocular imaging, allowing for high-resolution anterior segment photography (ASP) to enhance referrals, decision-making, and disease monitoring within clinical workflows.

Commercially available wearable devices, capable of measuring cardiac rhythm, are gaining popularity for convenient heart health monitoring. Accurate measurement of key intervals, specifically RR intervals for rhythm and QT and corrected QT (QTc) intervals for arrhythmia risk, is crucial for assessing potential cardiac morbidity.

Screening is important for early detection of cervical cancer in low- and middle-income countries. Visual inspection with acetic acid (VIA) is usually the method of choice in these settings. However, interpretation of VIA results is subject to interobserver and intraobserver variability. AI decision support systems (AI-DSSs) could contribute to better decisions by health workers.

Basic life support (BLS) significantly improves survival and neurological outcomes after out-of-hospital cardiac arrest (OHCA). However, bystander cardiopulmonary resuscitation (CPR) rates vary widely worldwide, reaching only 40% in Geneva, Switzerland. The European Resuscitation Council’s “Kids Save Lives” statement advocates for integrating BLS education into mandatory school curricula to improve bystander CPR rates, and thus OHCA outcomes. Training medical students as BLS instructors could help address the shortage of qualified instructors needed to implement school-based BLS programs in primary schools.

Remote patient management (RPM) is a strategy to track daily health data in patients with heart failure (HF) to enable early detection and treatment of decompensation to prevent hospital readmissions. The implementation of RPM alters standard care and redistributes provider responsibilities. Although randomized controlled trials demonstrate positive clinical outcomes, the impact of implementing RPM on patients’ health care usage, as well as the care time and workload of health care providers in a real-world setting, remains unclear.