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The White Matter Hyperintensity Shape and Brain Clearance (WHIMAS) Study for Identification of Novel 7T Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease: Protocol for a Cross-Sectional Study

The White Matter Hyperintensity Shape and Brain Clearance (WHIMAS) Study for Identification of Novel 7T Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease: Protocol for a Cross-Sectional Study

This study involves a whole-day visit to the LUMC for each participant and includes the following procedures: a 3 T brain MRI scan of 60 minutes, a 7 T brain MRI scan of 60 minutes, a neuropsychological assessment, and questionnaires on demographics and vascular risk factors. An overview of the study procedures can be found in Figure 1.

Jasmin Annica Kuhn-Keller, Ingmar Eiling, Lydiane Hirschler, Lena Václavů, Marie-Noëlle Witjes-Ané, Marjolein Wijngaarden, Martijn Nagtegaal, Ece Ercan, Nathaly Rius Ottenheim, Marjan van der Elst, Evelien Sohl, Mark A van Buchem, Simon Mooijaart, Matthias JP van Osch, Jeroen de Bresser

JMIR Res Protoc 2025;14:e77681


Assessing Usefulness of the Dashboard Instrument to Review Equity (DIRE) Checklist to Evaluate Equity in Public Health Dashboards: Reliability Study

Assessing Usefulness of the Dashboard Instrument to Review Equity (DIRE) Checklist to Evaluate Equity in Public Health Dashboards: Reliability Study

Upon categorization of states by these parameters, we chose a selection of at least 1‐3 dashboards per region and territory, based on the inclusion of all categories and upon the dashboard being (1) available for public viewing and (2) fitting the definition of a dashboard, “a visual display of the most important information needed to achieve one or more objectives, consolidated and arranged on a single screen so the information can be monitored at a glance” [5].

Paulina Sosa, Emir A Syailendra, Harold P Lehmann, Hadi Kharrazi

JMIR Public Health Surveill 2025;11:e71094


Differentiating Pediatric Bipolar Disorder, Attention-Deficit/Hyperactivity Disorder, and Other Psychopathologies Using Self-Reported Mood and Energy Data and Actigraphy Findings: Correlation and Machine Learning–Based Prediction of Mood Severity

Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and Validation

Clinical Large Language Model Evaluation by Expert Review (CLEVER): Framework Development and Validation

Additionally, a custom dataset allows for the identification of domain-specific challenges, leading to more effective implementations and improvements in real-world applications. By adhering to these principles, we aim to provide a more robust and meaningful evaluation of LLMs in the medical domain, avoiding the pitfalls highlighted by the Goodhart law: “When a measure becomes a target, it ceases to be a good measure” [14].

Veysel Kocaman, Mustafa Aytuğ Kaya, Andrei Marian Feier, David Talby

JMIR AI 2025;4:e72153


The Effectiveness of Digital Cognitive Behavioral Therapy to Treat Insomnia Disorder in US Adults: Nationwide Decentralized Randomized Controlled Trial

The Effectiveness of Digital Cognitive Behavioral Therapy to Treat Insomnia Disorder in US Adults: Nationwide Decentralized Randomized Controlled Trial

First, many digital clinical trials fail to enroll participants from a broad range of demographic groups, limiting the generalizability of the effects [45]. This is a common problem across clinical trials [46], and certain trial designs, such as decentralized trials, may help address participation barriers by relying on remote data collection methods [47]. Second, few trials have enrolled participants with a confirmed insomnia diagnosis established through structured clinical interviews.

Aric A Prather, Andrew D Krystal, Richard Emsley, Jenna Carl, Tali Ball, Kathryn Tarnai, Adrian Aguilera, Colin A Espie, Alasdair L Henry

JMIR Ment Health 2025;12:e84323


The Perceived Roles of AI in Clinical Practice: National Survey of 941 Academic Physicians

The Perceived Roles of AI in Clinical Practice: National Survey of 941 Academic Physicians

Artificial intelligence (AI) is a topic of significant interest in recent literature. It has garnered considerable attention as a potential tool to aid in detecting, diagnosing, and managing diseases and demonstrated efficacy in detecting diabetic retinopathy, diagnosing skin cancers, and even predicting patients’ surgical candidacy [1-5].

Anshul Ratnaparkhi, Simon Moore, Abhinav Suri, Bayard Wilson, Jacob Alderete, TJ Florence, David Zarrin, David Berin, Rami Abuqubo, Kirstin Cook, Matiar Jafari, Joseph S Bell, Luke Macyszyn, Andrew C Vivas, Joel Beckett

JMIR AI 2025;4:e72535


Prevalence of Metabolic Syndrome and Noncommunicable Disease Risk Factors in Andaman and Nicobar Islands, India, and Their Association With Ayurvedic Psychosomatic Constitution (Prakriti) and Socioeconomic Status: Protocol for a Cross-Sectional Study

Prevalence of Metabolic Syndrome and Noncommunicable Disease Risk Factors in Andaman and Nicobar Islands, India, and Their Association With Ayurvedic Psychosomatic Constitution (Prakriti) and Socioeconomic Status: Protocol for a Cross-Sectional Study

A Pitta-dominant individual is more susceptible to diseases like peptic ulcers, hypertension, and skin disorders [14]. However, a Vata-dominant individual is more prone to musculoskeletal issues like back pain and joint degeneration, and a Kapha-dominant individual faces a higher risk of developing metabolic conditions such as obesity, diabetes, and atherosclerosis [14].

Akashlal M, Azeem Ahmad, Abhayadev A, Lisha S Raj, Manisha M, Arunabh Tripathi, Saroj Kumar Debnath, Bhogavalli Chandra Sekhara Rao, Narayanam Srikanth, Rabinarayan Acharya

JMIR Res Protoc 2025;14:e81329


Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset

Unsupervised Characterization of Temporal Dataset Shifts as an Early Indicator of AI Performance Variations: Evaluation Study Using the Medical Information Mart for Intensive Care-IV Dataset

In classification problems involving input (x) and output (y) variables, a dataset shift occurs when the joint probability distribution of the data differs between training and test datasets. This can happen, for instance, when translating a model to a different site or during the AI’s use or adaptation over time (equation 1). In this work, we considered 3 primary sources of dataset shift: covariate shift, prior probability shift, and concept shift [11], as described next.

David Fernández-Narro, Pablo Ferri, Alba Gutiérrez-Sacristán, Juan M García-Gómez, Carlos Sáez

JMIR Med Inform 2025;13:e78309


Gender Differences in Trajectories of Depressive Symptoms Among Talkspace Clients: Naturalistic Observational Study

Gender Differences in Trajectories of Depressive Symptoms Among Talkspace Clients: Naturalistic Observational Study

Talkspace [33] is a telemedicine platform of over 5000 licensed therapists across the United States. Clients access the platform through the internet and complete a brief intake with a consulting clinician to identify their presenting problems and treatment history.

Meghan Romanelli, Julien Rouvere, Isaac A Sanders, Aarthi Padmanabhan, Emily Peake, Thomas D Hull, Tim Althoff

JMIR Form Res 2025;9:e75290