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Reference 30: Psychiatrists' experiences and opinions of generative artificial intelligence in mentalopinions
JMIR Ment Health 2025;12:e64414
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A 2018 scoping review explored the views of nurses and other health care providers on the use of assistive humanoid and animal-like robots and identified mixed opinions, but more positive than negative, and concerns related to patient safety and privacy [32]. While this review did focus on the perspectives of health care providers, including nurses, the authors excluded robots without a social or interactive element and did not focus on older adults or the ALF setting.
JMIR Aging 2024;7:e58629
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Assessing Patient Trust in Automation in Health Care Systems: Within-Subjects Experimental Study
opinions
JMIR Hum Factors 2024;11:e48584
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A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study
opinions
JMIR Form Res 2024;8:e55342
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Alarm Management in Intensive Care: Qualitative Triangulation Study
AI was considered a polarized term, evoking personal opinions, as people may have subjective and varied views on the topic [38-40]. Consequently, the interviewees were provided with an explanation of the functionality of the INALO system, leaving out the specification AI (refer to question 5 in the interview guide in Textbox 1).
JMIR Hum Factors 2024;11:e55571
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In these interview sessions, we sought to learn about participants’ opinions regarding wearable technology for health.
A preliminary round of interviews centered on exploring participants’ access to Wi-Fi connectivity, technology, and any resource constraints they may encounter was conducted.
JMIR Mhealth Uhealth 2024;12:e50826
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“Sentiment analysis” or “opinion mining” is a natural language processing technique used to analyze and extract insights from text data, enabling the identification and understanding of the sentiment, emotions, and subjective opinions expressed within the text, which can be valuable for various applications such as market research, customer feedback analysis, and social media monitoring. We used lexicon-based approaches to conduct analyses using the full data set of tweets.
JMIR Cancer 2024;10:e52061
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