JMIR Medical Informatics

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.

Shared decision-making (SDM) is critical for managing clinically ambiguous conditions such as benign breast disease (BBD), requiring reliable health information. In China, WeChat public accounts (WPAs) run by hospitals serve as a major health information channel, yet their quality remains unevaluated.

Timely access to stroke care is critical for patient prognosis. Despite the growing use of digital health strategies in stroke management, evidence supporting seamless and timely care coordination from the emergency medical services (EMS) to the emergency department (ED) remains limited. Brain Saver, a newly developed mobile app, addresses this gap by facilitating real-time data sharing between EMS personnel and ED clinicians during stroke transport.

In this Viewpoint, we highlight the principal cybersecurity measures that should be implemented to facilitate safe and effective integration of large language models (LLMs) into health care and propose a conceptual, life cycle–based framework synthesizing evidence from security and clinical informatics literature. While LLMs offer significant potential for applications in clinical documentation, triage, and medical education, their deployment creates novel vulnerabilities that can compromise patient safety and data confidentiality. We argue that these vulnerabilities must be addressed across the entire deployment life cycle, with distinct threats arising before and after a model enters clinical use. Predeployment risks include data and model poisoning, where an LLM’s training data or core parameters are maliciously corrupted to embed biases or backdoors. After deployment, LLMs are susceptible to inference attacks, such as prompt injection and adversarial inputs, which can be used to manipulate model behavior and extract sensitive information. Standard performance benchmarks are often insufficient to detect these sophisticated attacks. Therefore, we argue that a proactive, multilayered security framework combining technical safeguards, rigorous governance, and human-in-the-loop oversight is essential for the safe and trustworthy adoption of LLMs in clinical practice.

Health care organizations increasingly rely on business associates (BAs) to provide clinical, administrative, and technology services that require access to protected health information. While the Health Information Technology for Economic and Clinical Health (HITECH) Act and the Health Insurance Portability and Accountability Act (HIPAA) Omnibus Rule extended legal liability to BAs, the frequency and characteristics of data breaches involving BAs have not been systematically tracked across the entire post-HITECH reporting era. Understanding these trends is critical for health information managers and cybersecurity professionals who are directly responsible for managing third-party risk.

AI-based clinical decision support systems (CDSS) can improve diagnostics and treatment decisions, but they are rarely implemented in practice. Barriers include limited integration into clinical workflows, lack of transparency, and insufficient involvement of end users in system design. Participatory and user-centered approaches offer ways to address these challenges by aligning development processes with the needs and routines of clinical staff. However, systematic evidence on how such approaches are applied in the development of AI-based CDSS remains limited.

Access to eye care is a persistent challenge due to the high global burden of visual impairment and barriers to receiving eye care, such as transportation and cost. Conventional ophthalmic equipment is immobile and unsuitable for bedside or community use, and this limits timely diagnosis and care delivery. Portable ophthalmic devices, such as handheld slit-lamps and fundus cameras, offer the potential to extend diagnostic capabilities to nonclinical settings and improve accessibility of eye care.

Health data management during disasters enables responders to assess the needs of survivors, efficiently allocate resources, and monitor survivors’ health conditions. However, government agencies need to interpret health data in real time during disasters because the volume and complexity of such data can hinder timely decision-making.