Background: Artificial intelligence (AI) is increasingly used in digital health, particularly through large language models (LLMs), to support patient engagement and behavior change. One novel application is the delivery of motivational interviewing (MI), an evidence-based, patient-centered counseling technique designed to enhance motivation and resolve ambivalence around health behaviors. AI tools, including chatbots, mobile apps, and web-based agents, are being developed to simulate MI techniques at scale. While these innovations are promising, important questions remain about how faithfully AI systems can replicate MI principles or achieve meaningful behavioral impact. Objective: This scoping review aimed to summarize existing empirical studies evaluating AI-driven systems that apply MI techniques to support health behavior change. Specifically, we examined the feasibility of these systems; their fidelity to MI principles; and their reported behavioral, psychological, or engagement outcomes. Methods: We systematically searched PubMed, Embase, Scopus, Web of Science, and Cochrane Library for empirical studies published between January 1, 2018, and February 25, 2025. Eligible studies involved AI-driven systems using natural language generation, understanding, or computational logic to deliver MI techniques to users targeting a specific health behavior. We excluded studies using AI solely for training clinicians in MI. Three independent reviewers screened and extracted data on study design, AI modality and type, MI components, health behavior focus, MI fidelity assessment, and outcome domains. Results: Of the 1001 records identified, 15 (1.5%) met the inclusion criteria. Of these 15 studies, 6 (40%) were exploratory feasibility or pilot studies, and 3 (20%) were randomized controlled trials. AI modalities included rule-based chatbots (9/15, 60%), LLM-based systems (4/15, 27%), and virtual or mobile agents (2/15, 13%). Targeted behaviors included smoking cessation (6/15, 40%), substance use (3/15, 20%), COVID-19 vaccine hesitancy, type 2 diabetes self-management, stress, mental health service use, and opioid use during pregnancy. Of the 15 studies, 13 (87%) reported positive findings on feasibility or user acceptability, while 6 (40%) assessed MI fidelity using expert review or structured coding, with moderate to high alignment reported. Several studies found that users perceived the AI systems as judgment free, supportive, and easier to engage with than human counselors, particularly in stigmatized contexts. However, limitations in empathy, safety transparency, and emotional nuance were commonly noted. Only 3 (20%) of the 15 studies reported substantially significant behavioral changes. Conclusions: AI systems delivering MI show promise for enhancing patient engagement and scaling behavior change interventions. Early evidence supports their usability and partial fidelity to MI principles, especially in sensitive domains. However, most systems remain in early development, and few have been rigorously tested. Future research should prioritize randomized evaluations; standardized fidelity measures; and safeguards for LLM safety, empathy, and accuracy in health-related dialogue. Trial Registration: OSF Registries 10.17605/OSF.IO/G9N7E; https://osf.io/g9n7e

Background: Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions, by using historical nursing workload data to forecast future patient care requirements, which could help with staff planning while also improving patient outcomes and nurse well-being. Objective: This methodological study aims to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; “documentation of nursing activities”), to predict future workload requirements using machine learning techniques. Methods: We conducted a retrospective observational study at the University Hospital of Zürich, using nursing workload data for inpatients across eight wards, collected between 2017 and 2021. Data were transformed to represent nursing workload per ward and shift, with three shifts per day. Variables used in modeling included historical workload trends, patient characteristics, and upcoming operations. Machine learning models, including linear regression variants and tree-based methods (Random Forest and XGBoost), were trained and tested on this dataset to predict workload 72 hours in advance, on a shift-by-shift basis. Model performance was assessed using mean absolute error (MAE) and mean absolute percentage error (MAPE), and results were compared against a baseline of assuming no change in workload from the time of prediction. Prediction accuracy was further evaluated by categorizing future workload changes into decreased, similar, or increased workload relative to current shift levels. Results: Our findings demonstrate that machine learning models consistently outperform the baseline across all wards. The best-performing model was the Lasso Regression model, which achieved an average improvement in accuracy of 25.0% compared to the baseline. When used to predict upcoming changes in workload levels, the model achieved strong classification performance, giving an average AUROC of 0.79 and precision values between 66.2% and 75.3%. Crucially, the model severely misclassified—predicting an upcoming increase as a decrease, and vice versa—in just 0.17% of cases, highlighting potential reliability for using the model in practice. Key variables identified as important for predictions include historical shift workload averages and overall ward workload trends. Conclusions: This study suggests the potential of machine learning to enhance nurse workload prediction, while highlighting the need for refinement. Limitations due to potential discrepancies between recorded nursing activities and the actual workload highlight the need for further investigation into data quality. To maximize impact, future research should focus on: 1) utilizing more diverse data, 2) more advanced machine learning architecture that perform time-series modelling, 3) addressing data quality concerns, and 4) conducting controlled trials for real-world evaluation.

Nature Biotechnology, Published online: 15 September 2025; doi:10.1038/s41587-025-02822-6

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