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“Digital Clinicians” Performing Obesity Medication Self-Injection Education: Feasibility Randomized Controlled Trial

“Digital Clinicians” Performing Obesity Medication Self-Injection Education: Feasibility Randomized Controlled Trial

Automation may offer benefits in standardization, efficiency, effectiveness, cost, confidentiality, and access. Social desirability response bias is associated with higher levels of treatment nonadherence [25] and reduces the accuracy of clinical history taking [26] in human-human interactions. In educational settings, certain interactions, such as quizzing, enhance information retention but may be more socially appropriate from a “digital clinician” than from a health care professional.

Sean Coleman, Caitríona Lynch, Hemendra Worlikar, Emily Kelly, Kate Loveys, Andrew J Simpkin, Jane C Walsh, Elizabeth Broadbent, Francis M Finucane, Derek O' Keeffe

JMIR Diabetes 2025;10:e63503

Leveraging AI to Optimize Maintenance of Health Evidence and Offer a One-Stop Shop for Quality-Appraised Evidence Syntheses on the Effectiveness of Public Health Interventions: Quality Improvement Project

Leveraging AI to Optimize Maintenance of Health Evidence and Offer a One-Stop Shop for Quality-Appraised Evidence Syntheses on the Effectiveness of Public Health Interventions: Quality Improvement Project

AI commonly refers to the interdisciplinary study and development of models engineered to perform varied levels of automation that would typically require human intelligence [16,17]. Machine learning is a subset of AI that involves algorithms that autonomously learn patterns from the data they are trained on without being explicitly programmed [16].

Kristin Rogers, Alanna Miller, Ashley Girgis, Emily C Clark, Sarah E Neil-Sztramko, Maureen Dobbins

J Med Internet Res 2025;27:e69700

Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study

Advancing Clinical Chatbot Validation Using AI-Powered Evaluation With a New 3-Bot Evaluation System: Instrument Validation Study

Artificial intelligence (AI) automation in low- to mid-level tasks like patient education and initial therapy screening emerges as a strategic response to mitigate this shortage, reallocating medical staff to higher-priority tasks [2,3]. The advent of advanced multimodal large language models (LLMs) such as GPT-4 introduces a paradigm shift, promising scalable, cost-effective chatbot solutions, which are particularly helpful for tasks that require the provider to interact with the patient [4].

Seungheon Choo, Suyoung Yoo, Kumiko Endo, Bao Truong, Meong Hi Son

JMIR Nursing 2025;8:e63058

Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation

Automated Process for Monitoring of Amiodarone Treatment: Development and Evaluation

In this study, we present a model for the automation of repetitive follow-up of patients with amiodarone treatment. First, we constructed an automated solution including the whole surveillance process, using a robotic process automation (RPA) software tool. We then compared the RPA to standard manual amiodarone follow-up as presently practiced.

Birgitta I Johansson, Jonas Landahl, Karin Tammelin, Erik Aerts, Christina E Lundberg, Martin Adiels, Martin Lindgren, Annika Rosengren, Nikolaos Papachrysos, Helena Filipsson Nyström, Helen Sjöland

J Med Internet Res 2025;27:e65473

The Effects of Presenting AI Uncertainty Information on Pharmacists’ Trust in Automated Pill Recognition Technology: Exploratory Mixed Subjects Study

The Effects of Presenting AI Uncertainty Information on Pharmacists’ Trust in Automated Pill Recognition Technology: Exploratory Mixed Subjects Study

Trust in automation, defined as “the attitude that an agent will help achieve an individual’s goals in situations characterized by uncertainty and vulnerability” [26], is one of the most crucial factors determining the use of automation [27,28]. There is a growing body of research examining people’s trust in autonomous and robotic technologies in various domains, including transportation [29-31], health care [32,33], education [34], and defense [35,36].

Jin Yong Kim, Vincent D Marshall, Brigid Rowell, Qiyuan Chen, Yifan Zheng, John D Lee, Raed Al Kontar, Corey Lester, Xi Jessie Yang

JMIR Hum Factors 2025;12:e60273