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A Deep Learning–Based Rotten Food Recognition App for Older Adults: Development and Usability Study

In addition, we could easily collect photos of rotten apples, bananas, and oranges, so we selected them as the target fruits of our app [28]. Textbox 1 shows the research questions of our study. By answering our research questions, we will contribute to older adults and the community of researchers. First, we proposed a smartphone app that supports older adults in avoiding consuming rotten fruits.

Minki Chun, Ha-Jin Yu, Hyunggu Jung

JMIR Form Res 2024;8:e55342

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Visual “Scrollytelling”: Mapping Aquatic Selfie-Related Incidents in Australia

photos

Samuel Cornell, Amy E Peden

Interact J Med Res 2024;13:e53067

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Photos Shared on Facebook in the Context of Safe Sleep Recommendations: Content Analysis of Images

During data extraction, photos shared among mothers were noted on the extraction spreadsheet. Two reviewers analyzed the photos to identify those related to the infant sleep environment. The identified photos related to the infant sleep environment were then assessed for consistency with safe sleep guidelines. The photos were analyzed based on 5 criteria derived from the AAP safe sleep guidelines (including risk factors and protective factors) that were current at the time of the analysis.

Kelly Pretorius, Sookja Kang, Eunju Choi

JMIR Pediatr Parent 2024;7:e54610

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Tailored Prompting to Improve Adherence to Image-Based Dietary Assessment: Mixed Methods Study

So if you had five, you’re going to put six or four” “I found photos quite easy, because you just… take a photo and it’s done” “… the fact that you just take a photo of it, but it doesn’t come up with any information… would it be nice if like, you take a photo and all the information comes out and you can kind of track it” “… but I don’t feel like it did anything really?

Lachlan Lee, Rosemary Hall, James Stanley, Jeremy Krebs

JMIR Mhealth Uhealth 2024;12:e52074

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Using AI Text-to-Image Generation to Create Novel Illustrations for Medical Education: Current Limitations as Illustrated by Hypothyroidism and Horner Syndrome

Text-to-image tools have ethical issues, including issues of consent for the original photos used to train these tools. Additionally, issues of accuracy are key. Nonmedics might be misled on medical signs by using such tools. Targets for future research are the potential for biases with these tools and the danger of stereotypes being perpetuated. Despite these limitations, AI-generated images may enhance case-based learning, allowing students to study and analyze a diverse range of medical cases.

Ajay Kumar, Pierce Burr, Tim Michael Young

JMIR Med Educ 2024;10:e52155

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Skin of Color Representation on Wikipedia: Cross-sectional Analysis

Therefore, the aim of this study was to investigate the number and quality of SOC photos included in Wikipedia’s skin disease pages and explore the possible ramifications of these findings. Skin diseases from Wikipedia’s “List of Skin Conditions” page (that either specified dermatology as a specialty in the article or were discussed in a separate dermatology textbook) were included in this study [5].

William Kim, Sophia M Wolfe, Caterina Zagona-Prizio, Robert P Dellavalle

JMIR Dermatol 2021;4(2):e27802

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