Journal of Medical Internet Research

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.

Imaging-based AI may help predict pathological complete response (pCR) after neoadjuvant therapy for esophageal cancer. However, an encouraging average diagnostic performance does not establish reliable performance across clinical settings or justify treatment decisions without further validation.

Multimodal large language models (LLMs) are rapidly expanding in radiology and digital health; however, their diagnostic performance across diverse clinical scenarios and fine-grained visual reasoning tasks requires systematic evaluation.

Pregnancy- or childbirth-related traumatic experiences may lead to posttraumatic stress disorder (PTSD) symptoms during the postpartum period, adversely affecting women and their families. Digital health interventions may improve access to psychological support; however, evidence of their effectiveness for pregnancy- and childbirth-related posttraumatic stress disorder (PCR-PTSD) symptoms remains limited and has not been systematically synthesized.

Clinical notes are rich sources of clinical information and are routinely used in clinical practice to infer past diagnoses and other comorbidities. Large language models (LLMs) may simplify the task by extracting this information from clinical notes.

Richter and colleagues offer a careful systematic review of how large language model–based tools affect physician-patient communication, but the outcomes that matter most in deciding to scale a solution—trust and safety—are still unknown. Before deploying these tools, health systems need local validation, ongoing monitoring for accuracy and equity, and pragmatic trials that measure trust, safety, and outcomes alongside perceived empathy.