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Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach

Improving Suicidal Ideation Detection in Social Media Posts: Topic Modeling and Synthetic Data Augmentation Approach

Our protocol was developed using the scoping review methodological framework proposed by Arksey and O’Malley [39] and further refined by Peters et al [40]. The central research question guiding this review was as follows: “What are the most frequently reported risk factors associated with suicidal ideation in the psychology and mental health literature?”

Hamideh Ghanadian, Isar Nejadgholi, Hussein Al Osman

JMIR Form Res 2025;9:e63272

Improving Patient Communication by Simplifying AI-Generated Dental Radiology Reports With ChatGPT: Comparative Study

Improving Patient Communication by Simplifying AI-Generated Dental Radiology Reports With ChatGPT: Comparative Study

In a recent meta-analysis by Armache et al [31], the readability of 1124 education materials for patients with head and neck cancer was evaluated that was produced by professional societies, hospitals, and other organizations intended to inform patients, promote their engagement, and enhance their adherence to treatment plans. The mean Flesch-Kincaid grade level of these materials ranged from 8.8 to 14.8, with none of them meeting the recommended sixth-grade readability standard.

Daniel Stephan, Annika S Bertsch, Sophia Schumacher, Behrus Puladi, Matthias Burwinkel, Bilal Al-Nawas, Peer W Kämmerer, Daniel GE Thiem

J Med Internet Res 2025;27:e73337

Exploring the Link Between Visual Attention to Familiar or Novel Food Stimuli and Food Choice Using Integrated Electroencephalography and Eye Tracking: Protocol for Nonrandomized Pilot Study

Exploring the Link Between Visual Attention to Familiar or Novel Food Stimuli and Food Choice Using Integrated Electroencephalography and Eye Tracking: Protocol for Nonrandomized Pilot Study

For instance, in the study by Gere et al [38], the authors proposed using alternative models to analyze visual attention patterns during food choices. Their approach emphasized the use of multimodal data, such as eye-tracking and neurophysiological measures, to predict food preferences and decision outcomes more accurately, which supports the use of combined methodologies in our pilot study to capture these intricate cognitive processes.

Farshad Arsalandeh, Ali Shahbazi, Mohammad Ali Nazari

JMIR Res Protoc 2025;14:e69541

Using WhatsApp for Nutrition Surveillance Among Children Under 5 Years in West Java, Indonesia: Cross-Sectional Survey and Feasibility Study

Using WhatsApp for Nutrition Surveillance Among Children Under 5 Years in West Java, Indonesia: Cross-Sectional Survey and Feasibility Study

In April and May, fewer results were entered due to Ramadan and Eid al-Fitr affecting how well cadres could input data. Toward the study’s end, a reminder was sent to cadres by DTO-Pusdatin Ministry of Health and village leaders about data entry. The comparison of total CU5 being measured and those reported through Whats App, and the linearity trend of total Whats App data reporting month by month. CU5: children under 5 years.

Dewi Nur Aisyah, Chyntia Aryanti Mayadewi, Astri Utami, Fauziah Mauly Rahman, Nathasya Humaira Adriani, Erlangga Al Farozi, Meldi Hafizh Sayoko, Aulia Chairunisa, Liza Restiana, Logan Manikam, Zisis Kozlakidis

JMIR Pediatr Parent 2025;8:e58752

Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review

Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review

Manzini and do Vale [11] and Liao et al [12] address this critical gap by exploring remote monitoring methods facilitated by ITs. By leveraging innovative approaches, the research seeks to enhance the quality of care provided to patients with AD and substantially reduce the burden experienced by family members and caregivers.

Mohmmad Arif Shaik, Fahim Islam Anik, Md. Mehedi Hasan, Sumit Chakravarty, Mary Dioise Ramos, Mohammad Ashiqur Rahman, Sheikh Iqbal Ahamed, Nazmus Sakib

JMIR Aging 2025;8:e69175

Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review

Application of Artificial Intelligence in Cardio-Oncology Imaging for Cancer Therapy–Related Cardiovascular Toxicity: Systematic Review

In total, 7 studies conducted between 2018 and 2023 were included, with 5 from the United States (Kar et al [29-31], Zhang et al [32], Edalati et al [33]), 1 from China (Shen et al [34]), and 1 from Taiwan (Chang et al [35]). Of these, 6 studies involved patients with breast cancer with additional cancers (eg, sarcoma, lymphoma, leukemia) in some cohorts.

Hayat Mushcab, Mohammed Al Ramis, Abdulrahman AlRujaib, Rawan Eskandarani, Tamara Sunbul, Anwar AlOtaibi, Mohammed Obaidan, Reman Al Harbi, Duaa Aljabri

JMIR Cancer 2025;11:e63964

The Diagnostic Performance of Large Language Models and Oral Medicine Consultants for Identifying Oral Lesions in Text-Based Clinical Scenarios: Prospective Comparative Study

The Diagnostic Performance of Large Language Models and Oral Medicine Consultants for Identifying Oral Lesions in Text-Based Clinical Scenarios: Prospective Comparative Study

However, Copilot was significantly less accurate than Chat GPT (P=.015) and one of the oral medicine consultants (P Our findings are consistent with those obtained by Altamimi et al [16], who concluded that AI tools can be useful in clinical settings to provide diagnoses for certain conditions.

Sarah AlFarabi Ali, Hebah AlDehlawi, Ahoud Jazzar, Heba Ashi, Nihal Esam Abuzinadah, Mohammad AlOtaibi, Abdulrahman Algarni, Hazzaa Alqahtani, Sara Akeel, Soulafa Almazrooa

JMIR AI 2025;4:e70566