Smoking has severe health consequences, and secondhand smoke (SHS) exposure among children increases the risk of sudden infant death syndrome, chronic respiratory diseases, such as asthma, and lung cancer in adulthood. For many parents, pediatricians are the primary source of interaction with the healthcare system. Nevertheless, in pediatric settings, appropriate tobacco treatments are rarely, if ever, provided to parents who smoke. To best address tobacco use among parents, it is ideal to develop scalable solutions that are coordinated across health systems, community partners, and national services within pediatric settings. We describe our experience developing and implementing a parent tobacco treatment platform (PTTP) within a pediatric institution that leverages multiple international standards to support interoperability, with the overarching goal of providing a model for how such work can be approached. The clinical decision support (CDS) system includes clinician- and patient-facing components, connects parents to three different treatment options (nicotine replacement therapy, text-based counseling, and telephonic counseling), and incorporates three international standards (Fast Healthcare Interoperability Resources [FHIR], SMART on FHIR, and CDS Hooks). FHIR is used across all components. SMART on FHIR is limited to the clinician-facing tool, and CDS Hooks is used in the patient-facing portion. While healthcare interoperability standards supported a significant portion of the overall system, non-standard technologies and enhancements of existing standards were also required. Further, no connections with community partners could use existing interoperability standards. Over one year, the CDS was used in 194,946 visits, identified 7,847 parents who smoke, and connected 2,954 parents to 6,320 distinct treatment services, a significant improvement compared to prior efforts. Our project demonstrates that building CDS systems using international standards, such as SMART on FHIR, FHIR, and CDS Hooks, is possible, but challenges remain. Limits in the CDS Hooks standard to support common workflows and a lack of communication standards used by 3rd parties outside the healthcare system represent areas for future work. To support these requirements, additional EHR-specific records and communication mechanisms were required.

Background: The COVID-19 pandemic has intensified mental health issues globally, highlighting the urgent need for remote mental health monitoring. Digital phenotyping using smart devices has emerged as a promising approach, but it remains unclear which features are essential for predicting depression and anxiety. Objective: This systematic review aimed to identify the types of features collected through smart packages—integrated systems combining smartphones with wearable devices such as Actiwatches, smartbands, and smartwatches—and to determine which features should be considered essential for mental health monitoring based on the type of device used. Methods: A systematic review was conducted. Searches were performed across Web of Science, PubMed, and Scopus on February 5, 2025. Inclusion criteria comprised quantitative studies involving adults (≥19 years) using smart devices to predict depression or anxiety based on passive data collection. Studies focusing solely on smartphones or qualitative designs were excluded. Risk of bias was assessed using the Mixed Methods Appraisal Tool and Quality Criteria Checklist. Data were synthesized descriptively, and the relative contribution of each feature was further assessed by calculating coverage (proportion of studies using a feature) and importance among used (proportion identifying it as important when used). These metrics were visualized in quadrant-based scatter plots to identify consistently important features across devices. Results: From 1,382 records, 22 studies across 11 countries were included. The overall synthesis identified a core feature package—accelerometer (ACC), steps, Heart Rate (HR), and sleep. Device-specific analyses revealed further nuances: In Actiwatch studies, ACC and activity were consistently important, but sleep features were rarely examined. In smartbands, HR, steps, sleep, and phone usage were essential, while Global Positioning System (GPS), Electrodermal Activity (EDA), and skin temperature (TEMP) showed high importance when used, suggesting opportunities for broader adoption. In smartwatch studies, sleep and HR emerged as core features, whereas steps and ACC were widely used but often not identified as important. Conclusions: This systematic review identified a core feature package comprising ACC, steps, HR, and sleep that consistently contributes to mood disorder prediction across devices. At the same time, device-specific differences were observed: Actiwatch studies mainly emphasized ACC and activity but underutilized sleep features; smartbands highlighted HR, steps, sleep, and phone usage, with EDA, TEMP, and GPS showing additional promise; and smartwatches most reliably leveraged sleep and HR, while steps and ACC were widely used yet less effective. These findings suggest that while a shared core set of features exists, optimizing digital phenotyping requires tailoring feature selection to the characteristics of each device type. To advance this field, improving data accessibility, particularly in smartwatch ecosystems, and adopting standardized reporting frameworks will be essential to enhance comparability, reproducibility, and future meta-analytic integration. Clinical Trial: Open Science Framework https://osf.io/nz7k8

Nature Medicine, Published online: 05 November 2025; doi:10.1038/s41591-025-03982-3

Pretrained using 335,645 whole-slide images, a foundation model is developed to provide representations for slide- and patient-level tasks. It is capable of performing clinical tasks and generating reports even in data-scarce scenarios, such as rare cancer diagnosis and survival prediction, without requiring further fine-tuning.

Nature, Published online: 05 November 2025; doi:10.1038/s41586-025-09716-2

The Fair Human-Centric Image Benchmark (FHIBE, pronounced ‘Feebee’)—an image dataset that implements best practices for consent, privacy, compensation, safety, diversity and utility—can be used responsibly as a fairness evaluation dataset for many human-centric computer vision applications.