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Use of a Medical Communication Framework to Assess the Quality of Generative Artificial Intelligence Replies to Primary Care Patient Portal Messages: Content Analysis

A third option may be to have patients self-select questions to ask Gen AI versus PCPs, so that patients format questions de novo for input into Gen AI, and responses do not suffer from lack of contextual information. Organizations will have to weigh the potential net benefit of these approaches with other potential solutions, such as investing in robust best practices for inbox management [8] or hiring dedicated clinical staff for inbox management. Our report has several key strengths.

Natalie S Lee, Nathan Richards, Jodi Grandominico, Robert M Cronin, Amanda K Hendricks, Ravi S Tripathi, Daniel E Jonas

JMIR Form Res 2025;9:e71966

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eHealth Literacy and Participation in Remote Blood Pressure Monitoring Among Patients With Hypertension: Cross-Sectional Study

Hypertension, defined as systolic blood pressure (BP) ≥130 mm Hg or diastolic BP ≥80 mm Hg or both, has a prevalence of 46.7%, and control (systolic BP Over the years, several models and scales of e-HL have been proposed to measure e-HL [6-20]. There is currently no gold standard for e-HL measurement.

Chinwe E Eze, Michael P Dorsch, Antoinette B Coe, Corey A Lester, Lorraine R Buis, Karen B Farris

J Med Internet Res 2025;27:e71926

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Data Collection for Automatic Depression Identification in Spanish Speakers Using Deep Learning Algorithms: Protocol for a Case-Control Study

The dataset we built in this work, called D3 TEC (TEC de Monterrey’s Depression Detection Dataset), stands out for providing 2 new types of data previously unavailable in voice depression classification: Spanish recordings and simultaneous recordings using both professional and smartphone microphones. Moreover, audio quality standards are higher than most publicly available voice depression datasets.

Luis F Brenes, Luis A Trejo, Jose Antonio Cantoral-Ceballos, Daniela Aguilar-De León, Fresia Paloma Hernández-Moreno

JMIR Res Protoc 2025;14:e60439

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Large Language Model Symptom Identification From Clinical Text: Multicenter Study

Andrew J McMurry, Dylan Phelan, Brian E Dixon, Alon Geva, Daniel Gottlieb, James R Jones, Michael Terry, David E Taylor, Hannah Callaway, Sneha Manoharan, Timothy Miller, Karen L Olson, Kenneth D Mandl

J Med Internet Res 2025;27:e72984

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Leveraging Large Language Models for Simulated Psychotherapy Client Interactions: Development and Usability Study of Client101

To assess the mental health professionals’ perception of the chatbot’s ability to simulate mental health client characteristics, we used a survey designed per the CHERRIES (Checklist for Reporting Results of Internet E-Surveys) [27]: This study targeted professionals in psychotherapy, specifically clinical psychologists, psychiatrists, and counselors. The survey was a postsession evaluation form completed by participants after interacting with the chatbots.

Daniel Cabrera Lozoya, Mike Conway, Edoardo Sebastiano De Duro, Simon D'Alfonso

JMIR Med Educ 2025;11:e68056

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