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Exploring Primary Care Patients’ Perspectives on Artificial Intelligence: Systematic Literature Review and Qualitative Meta-Synthesis

Exploring Primary Care Patients’ Perspectives on Artificial Intelligence: Systematic Literature Review and Qualitative Meta-Synthesis

Patients expressed concerns regarding the accessibility of AI in health care, particularly for individuals with unique characteristics, such as those who use sign language, have strong accents, or have atypical sensory processing [12]. Some thought AI could increase health care costs [46], and overdiagnosis was mentioned as a reason [1]. Patients expressed trust in the health system and their physicians [1,12,47].

Alisa Mundzic, Robin Bogdanffy, David Sundemo, Pär-Daniel Sundvall, Jonathan Widén, Peter Nymberg, Carl Wikberg, Anna Moberg, Ronny Gunnarsson, Artin Entezarjou

JMIR AI 2025;4:e72211


Challenges and Solutions in Applying Large Language Models to Guideline-Based Management Planning and Automated Medical Coding in Health Care: Algorithm Development and Validation

Challenges and Solutions in Applying Large Language Models to Guideline-Based Management Planning and Automated Medical Coding in Health Care: Algorithm Development and Validation

In summary, through explainable AI design, strong human oversight, and educational transparency, Rhazes can be deployed in a way that upholds ethical standards and supports clinicians and patients alike. Any digital health technology company operating within the United Kingdom collecting or processing any form of personal data must comply with UK General Data Protection Regulation and the Data Protection Act [95].

Peter Sarvari, Zaid Al-fagih, Alexander Abou-Chedid, Paul Jewell, Rosie Taylor, Arouba Imtiaz

JMIR Biomed Eng 2025;10:e66691


Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation

Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation

These studies have often achieved strong (although not clinically translatable) performances, frequently exceeding 80% in F1-scores or accuracy. For example, Dinkel et al [32] applied a text-based multitask network to the DAIC-WOZ dataset, achieving an F1-score of 0.84 for binary detection.

Kaiying Lin, Abdur Rasool, Saimourya Surabhi, Cezmi Mutlu, Haopeng Zhang, Dennis P Wall, Peter Washington

JMIR AI 2025;4:e75030


Developing a Quick Isolation Bed Inquiry System During the COVID-19 Outbreak: User-Centered Design Approach Based on the Toyota Production System

Developing a Quick Isolation Bed Inquiry System During the COVID-19 Outbreak: User-Centered Design Approach Based on the Toyota Production System

Ohno [32] consistently emphasized his strong belief in the concept of gemba (on-site work). Even after joining Toyota’s top management, he continued to spend most of his time on the shop floor [32]. Consequently, it is fair to say that TPS is fundamentally centered on on-site management [32-35]. According to Ohno, work activities are classified based on their contribution to value into 3 categories: waste, non-value–added work (necessary but non-value–adding activities), and value-added work [32,33].

Chien-Chung Lin, Jian-Hong Shen, Shan-Li Chang, Tai-Chih Kuo, Hui-Ling Huang, Cheng-Yi Peter Lin, Hung-Meng Huang

JMIR Form Res 2025;9:e67152