Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Artificial intelligence (AI), particularly large language models (LLMs), is increasingly used in digital health 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 and virtual agents, have shown promise in simulating human-like dialogue and applying MI techniques at scale. However, the extent to which AI systems can faithfully replicate MI principles and generate meaningful behavioral outcomes remains unclear. Objective: This scoping review aimed to assess the scope, characteristics, and findings of existing studies that evaluate AI systems delivering motivational interviewing directly to patients. Specifically, we examined the feasibility of these systems, their fidelity to MI principles, and any reported outcomes related to health behavior change. Methods: We conducted a comprehensive search of five electronic databases (PubMed, Embase, Scopus, Web of Science, and Cochrane Library) for studies published between January 1, 2018, and February 25, 2025. Eligible studies included any empirical design that used AI to perform MI with patients targeting a specific health behavior (e.g., smoking cessation, vaccine uptake). We excluded studies using AI solely for training clinicians in MI. Three independent reviewers conducted screening and data extraction. Extracted variables included study design, AI modality and type, health behavior focus, MI fidelity assessment, and reported outcomes. Data were synthesized narratively to map the evidence landscape. Results: Out of 1001 records identified, 8 studies met the inclusion criteria. Most were exploratory feasibility or pilot studies; only one was a randomized controlled trial. AI modalities included rule-based chatbots, large language models (such as GPT-4), and virtual reality conversational agents. Targeted behaviors included smoking cessation, substance use reduction, vaccine hesitancy, type 2 diabetes self-management, and opioid use during pregnancy. Across studies, AI-delivered MI was rated as usable and acceptable. Patients frequently described AI systems as “judgment-free” and supportive, which enhanced openness and engagement, particularly in stigmatized contexts. Expert evaluations of MI fidelity reported high alignment with MI principles in most cases. However, participants also noted a lack of emotional depth and limited perceived empathy. One study improved these perceptions by adjusting conversational pacing and content complexity. Only one study evaluated behavioral outcomes and found no statistically significant changes. Conclusions: AI systems, particularly those powered by LLMs, show promise in delivering motivational interviewing that is scalable, accessible, and perceived as nonjudgmental. While AI can replicate many structural aspects of MI and foster engagement, current evidence on its efficacy in driving behavior change is limited. More rigorous studies, including randomized controlled trials with diverse populations, are needed to assess long-term outcomes and to refine AI-human hybrid models that balance efficiency with relational depth.

Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSSs) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. A cross-sectional survey was conducted with 65 unique specialist physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and 90 corresponding AI-CDSS recommendations. Participants assessed the perceived helpfulness of AI decision-making, the impact of AI recommendations on their own clinical judgment, and the concordance between AI recommendations and their own clinical judgment, as well as the validity of changing therapy based on CDSS recommendations. The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Findings show that 97.8% [(95% CI: 92.2–99.7%)] of physicians reported that AI facilitated decision-making and substantial concordance (87.8% [95% CI: 79.2–93.7%; Cohen’s κ = 0.76]) between AI recommendations and physicians’ therapeutic recommendations. Stratification by pathogen revealed the highest concordance for Escherichia coli bacteremia (96.6%, 28/29 cases). Implementation analysis revealed a meaningful clinical impact, with 68.9% [(95% CI: 58.3–78.2%)] of cases resulting in AI-guided treatment modifications. These findings indicate that AI-powered CDSSs effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship, providing clinicians with evidence-based therapeutic recommendations that can be integrated into routine practice to optimize antibiotic selection, particularly in settings with limited access to infectious disease specialists. For optimal clinical integration, we recommend that clinicians utilize AI-CDSS recommendations as an adjunct to clinical judgment rather than a replacement, particularly in complex cases involving immunocompromised hosts or polymicrobial infections. Future research should prioritize prospective clinical trials that evaluate direct patient outcomes to establish evidence of broader clinical effectiveness and applicability across diverse healthcare settings.

Background: Bacteremia is a life-threatening condition contributing significantly to sepsis-related mortality worldwide. With delayed appropriate antibiotic therapy, mortality increases by 20% regardless of antimicrobial resistance. This study evaluated the perceived clinical utility of Artificial Intelligence (AI)-powered Clinical Decision Support Systems (CDSS) (OneChoice and OneChoice Fusion) among specialist physicians managing bacteremia cases. Methods: A cross-sectional survey was conducted with 65 physicians from multiple medical specialties who were presented with clinical vignettes describing patients with bacteremia and corresponding 90 AI-CDSS recommendations. Participants assessed perceived helpfulness, the impact of AI decision-making, concordance between AI recommendations and their own clinical judgment, and whether changing therapy based on CDSS recommendations is valid. Results: The study encompassed a diverse range of bacterial pathogens, with Escherichia coli representing 38.7% of the isolates and 30% being extended-spectrum β-lactamase (ESBL) producers. Results demonstrated 97.8% of physicians reported that AI facilitated decision-making and substantial concordance (87.8%) between AI recommendations and physicians’ therapeutic recommendations. Implementation analysis revealed a meaningful clinical impact, with 68.9% of cases resulting in AI-guided treatment modifications. Conclusions: These findings indicate that AI-powered CDSS effectively bridge critical gaps in infectious disease expertise and antimicrobial stewardship. Future research should prioritize prospective clinical trials evaluating direct patient outcomes to establish evidence of broader clinical effectiveness and application across diverse healthcare settings.