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Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Challenges in Implementing Artificial Intelligence in Breast Cancer Screening Programs: Systematic Review and Framework for Safe Adoption

Many countries worldwide have embraced mammographic screening programs as a vital tool for identifying breast cancer in its early stages, significantly reducing the risk of associated mortality [2]. Despite the perceived advantages, numerous challenges remain in the interpretation of screening mammograms. First, the high volume of screenings, combined with the requirement for independent, blinded double-reading by radiologists, places significant pressure on the existing radiology workforce [3].

Serene Goh, Rachel Sze Jen Goh, Bryan Chong, Qin Xiang Ng, Gerald Choon Huat Koh, Kee Yuan Ngiam, Mikael Hartman

J Med Internet Res 2025;27:e62941

Application of an Innovative Methodology to Build Infrastructure for Digital Transformation of Health Systems: Developmental Program Evaluation

Application of an Innovative Methodology to Build Infrastructure for Digital Transformation of Health Systems: Developmental Program Evaluation

To address this critical gap in digital health platform development, this study aimed to apply mixed evaluation methods to assess the development of a digital health platform that was exclusively developed by a research and development team working remotely during the COVID-19 pandemic to manage, monitor, and mitigate household risk of COVID-19.

M Claire Buchan, Tarun Reddy Katapally, Jasmin Bhawra

JMIR Form Res 2025;9:e53339

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study

With over 300 million noncardiac surgeries performed annually, accurate preoperative risk assessment has become essential to optimize patient outcomes and reduce health care costs [5,6]. However, the predictive accuracy of traditional assessment tools is not consistently high, and various tools are used at different physicians’ discretion [7].

Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

J Med Internet Res 2025;27:e66366

Consumer Engagement With Risk Information on Prescription Drug Social Media Pages: Findings From In-Depth Interviews

Consumer Engagement With Risk Information on Prescription Drug Social Media Pages: Findings From In-Depth Interviews

Risk information is also sometimes conveyed in a featured text post, a scrolling video post, a risk information pop-up, or a highlighted story. Many pharmaceutical companies place the risk information in multiple places on their social media pages and posts. However, even when this information is displayed in various locations, UI or UX features of social media platforms may still inhibit consumers’ ability to find, review, and comprehend the risk information.

Jacqueline B Amoozegar, Peyton Williams, Kristen C Giombi, Courtney Richardson, Ella Shenkar, Rebecca L Watkins, Amie C O'Donoghue, Helen W Sullivan

J Med Internet Res 2025;27:e67361

Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

Machine Learning–Based Explainable Automated Nonlinear Computation Scoring System for Health Score and an Application for Prediction of Perioperative Stroke: Retrospective Study

The risk scoring systems have been proposed to prognosticate critical medical conditions, aiming to identify high-risk patients who are likely to experience adverse outcomes [1-6]. Traditionally, risk-scoring systems have been developed using conventional statistical approaches, based on the assumption of linearity between variables and outcomes [7,8].

Mi-Young Oh, Hee-Soo Kim, Young Mi Jung, Hyung-Chul Lee, Seung-Bo Lee, Seung Mi Lee

J Med Internet Res 2025;27:e58021

Organizational Leaders’ Views on Digital Health Competencies in Medical Education: Qualitative Semistructured Interview Study

Organizational Leaders’ Views on Digital Health Competencies in Medical Education: Qualitative Semistructured Interview Study

This decision was based on the determination that the activities involved posed no more than minimal risk to participants. Despite this waiver, the research adhered strictly to the ethical principles outlined in the World Medical Association’s Declaration of Helsinki and institutional guidelines. A total of 33 participants took part in the study.

Humairah Zainal, Xin Xiao Hui, Julian Thumboo, Fong Kok Yong

JMIR Med Educ 2025;11:e64768

Digital Isolation and Dementia Risk in Older Adults: Longitudinal Cohort Study

Digital Isolation and Dementia Risk in Older Adults: Longitudinal Cohort Study

Consequently, individuals who are digitally isolated may miss these protective effects, which could accelerate cognitive decline and elevate their risk of dementia [11-13]. Limited literacy and education levels may further restrict older adults’ ability to engage with digital technologies, hindering their ability to benefit and potentially increasing the risk of cognitive decline [14,15].

Cheng Deng, Na Shen, Guangzhou Li, Ke Zhang, Shijun Yang

J Med Internet Res 2025;27:e65379

Reconstructing Risk Dimensions in Telemedicine: Investigating Technology Adoption and Barriers During the COVID-19 Pandemic in Taiwan

Reconstructing Risk Dimensions in Telemedicine: Investigating Technology Adoption and Barriers During the COVID-19 Pandemic in Taiwan

Previous literature about perceived risk and telemedicine using structural equation modeling (SEM). a PR: perceived risk. b FNR: financial risk. c TMR: time risk. d PFR: performance risk. e PLR: psychological risk. f SCR: social risk. g PRR: privacy risk. h PSR: physical risk. i TNR: technology risk. j PVR: provider risk. k COR: COVID-19 infection risk. The financial risk refers to potential monetary loss owing to transaction errors or subsequent maintenance costs of the productor service [11].

Tzu-Chi Wu, Chien-Ta Ho

J Med Internet Res 2025;27:e53306

Effectiveness of Electronic Quality Improvement Activities to Reduce Cardiovascular Disease Risk in People With Chronic Kidney Disease in General Practice: Cluster Randomized Trial With Active Control

Effectiveness of Electronic Quality Improvement Activities to Reduce Cardiovascular Disease Risk in People With Chronic Kidney Disease in General Practice: Cluster Randomized Trial With Active Control

Kidney Health Australia guidelines support the detection of CKD in high-risk populations and recommend pharmacological treatment with angiotensin-converting enzyme inhibitor inhibitors (ACEI) or angiotensin receptor blockers (ARB), or statins or both statins and an ACEI or ARB to reduce CKD progression and CVD risk [5]. It has been identified that additional strategies are required to optimize CVD risk management in general practice [7,8].

Jo-Anne Manski-Nankervis, Barbara Hunter, Natalie Lumsden, Adrian Laughlin, Rita McMorrow, Douglas Boyle, Patty Chondros, Shilpanjali Jesudason, Jan Radford, Megan Prictor, Jon Emery, Paul Amores, An Tran-Duy, Craig Nelson

JMIR Form Res 2025;9:e54147

Use of the FHTHWA Index as a Novel Approach for Predicting the Incidence of Diabetes in a Japanese Population Without Diabetes: Data Analysis Study

Use of the FHTHWA Index as a Novel Approach for Predicting the Incidence of Diabetes in a Japanese Population Without Diabetes: Data Analysis Study

The diabetes risk test created by the American Diabetes Association (ADA) serves as a screening tool designed to categorize individuals at a heightened risk within the community to enhance awareness regarding modifiable risk factors and promote the adoption of a healthy lifestyle [15].

Jiao Wang, Jianrong Chen, Ying Liu, Jixiong Xu

JMIR Med Inform 2025;13:e64992