Journal of Medical Internet Research

Large language models (LLMs) have shown potential to support patient education, improve information accessibility, and assist with repetitive clinical communication tasks. However, the clinical integration of LLMs depends not only on technological capabilities but also on how these tools fit within existing health care workflows. Existing studies have largely focused on technical performance of LLMs or the experiences of physicians and patients, and little is known about how frontline nurses, who play a central role in patient education, experience LLM-supported education workflows in clinical practice settings.

Amyotrophic lateral sclerosis (ALS) clinical trials need outcome measures that complement established end points while capturing how people function in daily life. In this viewpoint, we draw on our collective experience in ALS research, clinical trials, digital-measure development, and regulatory science, together with published evidence, a preliminary patient survey, stakeholder discussions, and experience from multisite actigraphy studies. We argue that the field now has a substantial base of longitudinal data and candidate measures, but lacks the coordinated framework needed to compare, prioritize, and validate them across studies. We identify 4 linked priorities: adopt fit-for-purpose measures in stages and within defined contexts of use; harmonize data collection and analysis; use data to shortlist and validate measures across cohorts; and prepare actigraphy methods for trial adoption through early industry and regulatory engagement. Across these priorities, actigraphy should complement rather than replace established clinical outcomes, with its integration guided by patient relevance, ALS phenotypic heterogeneity, participant, and caregiver burden, and the intended context of use. Our central message is that progress now depends less on generating additional isolated datasets or measures than on coordinating the evaluation of existing assets, and aligning future evidence generation.

Mental health–related content has been increasingly visible on Chinese social media among young people, including the expression of negative affect and personal experiences of mental health issues, treatment, and recovery. Such content could reach a wide audience and influence their understanding of and responses to mental health issues. However, previous studies have mainly focused on highly sensitive topics such as self-harm and suicide, with occasional posts included, or examined discrete stages of content creation. Limited evidence can be found that treats content creation as a connected process that involves motivation, creation, and reaction to feedback from the perspective of young creators.

Transparent prediction models, such as Cox regression, remain the standard for clinical risk prediction. However, they are challenging to design optimally by hand when tens or even hundreds of features require selection, transformation, or interaction modeling. While complex machine learning models offer high performance, their “black-box” nature limits the clinical trust, transparency, and interpretability required for decision-making.

In the United States, 63.9% of adults report ≥1 adverse childhood experience (ACE), and 17.3% report ≥4. The presence and number of ACEs increases the odds of opioid misuse, overdose, and suicide. Rural children are more likely to experience ACEs. Despite ACEs’ well-documented sequela, a large proportion of providers are unaware of their detrimental effects, creating a significant need for training. There is limited research on ACEs workforce training.

Long COVID affects millions of adults, causing fatigue, dyspnea, cognitive symptoms, and mood disturbances. Effective treatments are limited, and many patients cannot access multidisciplinary rehabilitation because of symptom fluctuation, distance, or specialist shortages. Web-based mind-body interventions may offer accessible, scalable support alongside existing care.

Ambient digital scribes (ADSs) are promoted to reduce clinical documentation burden, improve efficiency, and allow clinicians to focus on patients. This viewpoint examines the sociotechnical assumptions underlying these claims and argues that safe integration of ADSs requires consideration of how documentation work and clinician oversight are transformed within the wider clinical work system. Their appeal rests on 2 reassuring assumptions: that documentation work can be cleanly automated and that residual risks can be controlled because clinicians remain responsible for reviewing and correcting the output. This viewpoint argues that both assumptions can be challenged from a sociotechnical perspective. Automating the draft note does not eliminate documentation work. It transforms and redistributes it, moving clinicians from composing notes to evaluating, correcting, and editing AI-generated accounts, potentially transferring additional verification and reconciliation work to other people and later stages of care. Clinician oversight should not be treated as an automatic safety backstop. Plausible outputs may encourage cursory review, while omissions and subtle shifts in meaning can be difficult to detect. Time pressure, perceived system reliability, and organizational expectations for productivity may further weaken scrutiny. ADSs should not be designed and implemented through simple substitution of AI for documentation tasks followed by reliance on human checking. Safe integration requires sociotechnical design at the level of the wider work system, including deliberate choices about task design, organizational arrangements, and organizational learning. Participatory and context-sensitive sociotechnical design can inform explicitly what an ADS should and should not do, how the resulting documentation work should be distributed, and how impacts will be managed across the wider care system.

Alcohol craving is associated with drinking behaviors despite negative consequences and relapse following periods of abstinence or treatment. Accurately forecasting alcohol craving is crucial for preventing alcohol consumption and relapse, thereby mitigating alcohol-related problems. Previous studies have assessed alcohol craving using diverse modalities such as psychological assessments, neurophysiological measures including electroencephalography (EEG) or heart rate variability (HRV) at a single time point, and digital phenotypes collected longitudinally via smartphone apps. However, alcohol craving is inherently heterogeneous and multidimensional across individuals and over time, making it difficult to capture using any single modality alone. In contrast, multimodal AI enables the prediction of alcohol craving by integrating complementary behavioral and neurophysiological features.

Digital technologies are increasingly incorporated into long-term self-management of health conditions, yet their social impact on stroke survivors and carers remains limited.

Due to concerns about privacy breaches and the fear of social stigma, some individuals engaging in high-risk behaviors—particularly men who have sex with men (MSM)—may avoid in-person HIV testing services and instead choose to purchase HIV self-test kits online. Unlike traditional e-commerce platforms that provide self-test kits directly without conducting risk assessments, the “Easy Test Know” platform integrates a structured behavioral risk assessment prior to purchasing an HIV self-test kit, allowing users to be categorized into different HIV risk groups.