This article uses critical discourse analysis to investigate artificial intelligence (AI) generated images of aged care nurses and considers how perspectives and perceptions impact upon the recruitment and retention of nurses. The article demonstrates a recontextualization of aged care nursing, giving rise to hidden ideologies including harmful stereotypes which allow for discrimination and exploitation. It is argued that this may imply that nurses require fewer clinical skills in aged care, diminishing the value of working in this area. AI relies on existing data sets, and thus represent existing stereotypes and biases. The discourse analysis has highlighted key issues which may further impact upon nursing recruitment and retention, and advocates for stronger ethical consideration, including the use of experts in data validation, for the way that aged care services and nurses are depicted and thus valued.

BACKGROUND: Ultrasound-based radiomic features to differentiate between benign and malignant breast lesions with the help of machine learning is currently being researched. The mean echogenicity ratio has been used for the diagnosis of malignant breast lesions. However, gray scale intensity histogram values as a single radiomic feature for the detection of malignant breast lesions using machine learning algorithms have not been explored yet.
OBJECTIVE: This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion.
METHODS: An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions\' diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set.
RESULTS: The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively.
CONCLUSIONS: Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel.

UNASSIGNED: Information about the range of Hounsfield values for healthy teeth tissues could become an additional tool in assessing dental health and could be used, among other data, for subsequent machine learning.
UNASSIGNED: The purpose of our study was to determine dental tissue densities in Hounsfield units (HU).
UNASSIGNED: The total sample included 36 healthy children (n=21, 58% girls and n=15, 42% boys) aged 10-11 years at the time of the study. The densities of 320 teeth tissues were analyzed. Data were expressed as means and SDs. The significance was determined using the Student (1-tailed) t test. The statistical significance was set at P<.05.
UNASSIGNED: The densities of 320 teeth tissues were analyzed: 72 (22.5%) first permanent molars, 72 (22.5%) permanent central incisors, 27 (8.4%) second primary molars, 40 (12.5%) tooth germs of second premolars, 37 (11.6%) second premolars, 9 (2.8%) second permanent molars, and 63 (19.7%) tooth germs of second permanent molars. The analysis of the data showed that tissues of healthy teeth in children have different density ranges: enamel, from mean 2954.69 (SD 223.77) HU to mean 2071.00 (SD 222.86) HU; dentin, from mean 1899.23 (SD 145.94) HU to mean 1323.10 (SD 201.67) HU; and pulp, from mean 420.29 (SD 196.47) HU to mean 183.63 (SD 97.59) HU. The tissues (enamel and dentin) of permanent central incisors in the mandible and maxilla had the highest mean densities. No gender differences concerning the density of dental tissues were reliably identified.
UNASSIGNED: The evaluation of Hounsfield values for dental tissues can be used as an objective method for assessing their densities. If the determined densities of the enamel, dentin, and pulp of the tooth do not correspond to the range of values for healthy tooth tissues, then it may indicate a pathology.

BACKGROUND: The integration of artificial intelligence (AI), particularly deep learning models, has transformed the landscape of medical technology, especially in the field of diagnosis using imaging and physiological data. In otolaryngology, AI has shown promise in image classification for middle ear diseases. However, existing models often lack patient-specific data and clinical context, limiting their universal applicability. The emergence of GPT-4 Vision (GPT-4V) has enabled a multimodal diagnostic approach, integrating language processing with image analysis.
OBJECTIVE: In this study, we investigated the effectiveness of GPT-4V in diagnosing middle ear diseases by integrating patient-specific data with otoscopic images of the tympanic membrane.
METHODS: The design of this study was divided into two phases: (1) establishing a model with appropriate prompts and (2) validating the ability of the optimal prompt model to classify images. In total, 305 otoscopic images of 4 middle ear diseases (acute otitis media, middle ear cholesteatoma, chronic otitis media, and otitis media with effusion) were obtained from patients who visited Shinshu University or Jichi Medical University between April 2010 and December 2023. The optimized GPT-4V settings were established using prompts and patients\' data, and the model created with the optimal prompt was used to verify the diagnostic accuracy of GPT-4V on 190 images. To compare the diagnostic accuracy of GPT-4V with that of physicians, 30 clinicians completed a web-based questionnaire consisting of 190 images.
RESULTS: The multimodal AI approach achieved an accuracy of 82.1%, which is superior to that of certified pediatricians at 70.6%, but trailing behind that of otolaryngologists at more than 95%. The model\'s disease-specific accuracy rates were 89.2% for acute otitis media, 76.5% for chronic otitis media, 79.3% for middle ear cholesteatoma, and 85.7% for otitis media with effusion, which highlights the need for disease-specific optimization. Comparisons with physicians revealed promising results, suggesting the potential of GPT-4V to augment clinical decision-making.
CONCLUSIONS: Despite its advantages, challenges such as data privacy and ethical considerations must be addressed. Overall, this study underscores the potential of multimodal AI for enhancing diagnostic accuracy and improving patient care in otolaryngology. Further research is warranted to optimize and validate this approach in diverse clinical settings.

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UNASSIGNED: Sudden unexpected infant death (SUID) remains a leading cause of infant mortality; therefore, understanding parental practices of infant sleep at home is essential. Since social media analyses yield invaluable patient perspectives, understanding sleep practices in the context of safe sleep recommendations via a Facebook mothers\' group is instrumental for policy makers, health care providers, and researchers.
UNASSIGNED: This study aimed to identify photos shared by mothers discussing SUID and safe sleep online and assess their consistency with infant sleep guidelines per the American Academy of Pediatrics (AAP). We hypothesized the photos would not be consistent with guidelines based on prior research and increasing rates of accidental suffocation and strangulation in bed.
UNASSIGNED: Data were extracted from a Facebook mothers\' group in May 2019. After trialing various search terms, searching for the term \"SIDS\" on the selected Facebook group resulted in the most relevant discussions on SUID and safe sleep. The resulting data, including 20 posts and 912 comments among 512 mothers, were extracted and underwent qualitative descriptive content analysis. In completing the extraction and subsequent analysis, 24 shared personal photos were identified among the discussions. Of the photos, 14 pertained to the infant sleep environment. Photos of the infant sleep environment were then assessed for consistency with safe sleep guidelines per the AAP standards by 2 separate reviewers.
UNASSIGNED: Of the shared photos relating to the infant sleep environment, 86% (12/14) were not consistent with AAP safe sleep guidelines. Specific inconsistencies included prone sleeping, foreign objects in the sleeping environment, and use of infant sleeping devices. Use of infant monitoring devices was also identified.
UNASSIGNED: This study is unique because the photos originated from the home setting, were in the context of SUID and safe sleep, and were obtained without researcher interference. Despite study limitations, the commonality of prone sleeping, foreign objects, and the use of both infant sleep and monitoring devices (ie, overall inconsistency regarding AAP safe sleep guidelines) sets the stage for future investigation regarding parental barriers to practicing safe infant sleep and has implications for policy makers, clinicians, and researchers.

This document presents the protocol of a study conducted as a part of the WEB DATA OPP project, which is funded by the H2020 program. The study aimed to investigate different aspects of the collection of images through web surveys. To do this, we implemented a mobile web survey in an opt-in online panel in Spain. The survey had various questions, some of which were about the books that the participants have at their main residence. The questions related to books were asked in three different ways: regular survey questions showing visual examples of how different numbers of books fit in a 74 centimetre wide shelf depending on their thickness, regular survey questions without the visual examples, and questions where participants were asked to send photos of the books at their home. This report explains how the study was designed and conducted. It covers important aspects such as the experimental design, the questionnaire used, the characteristics of the participants, ethical considerations, and plans for disseminating the results.
This document presents the protocol of our study asking respondents for information about the books they have at home. This information was solicited through conventional types of questions ( i.e., typing in answers or choosing one answer category), and/or through asking respondents to take and send photos of the books. This study has methodological and substantive objectives. The former involves investigating respondents’ preference, evaluation of the questions, participation levels, compliance, and data quality. The latter focuses on exploring the impact of the number of books on the academic achievement of children and examining other factors that might influence these relations. We conducted a mobile web survey, assigning respondents to four groups:    •   Choice: Respondents could choose their preferred answering method.    •   Text-TextPlus: Respondents answered conventional questions first, and later received illustrations of how different numbers of books looked like to help respondents provide accurate answers.    •   TextPlus-Images: Respondents answered conventional questions with the illustrations and then submitted photos of the books at home.    •   Images-Text: Respondents shared photos of the books and then answered the conventional questions. Respondents were asked to evaluate their respective answering methods. The questionnaire had up to 65 questions covering various dimensions, including respondents’ sociodemographic characteristics, children\'s academic performance, literacy-related activities, and camera usage. Data were collected using the Netquest opt-in online panel in Spain. The tool WebdataVisual was used to capture and share the photos. The target population included parents of children living with them and who attended the first, third, or fifth year of primary school. The sample size was 1,202 cases. We expect this study to provide valuable insights regarding visual data collection through web surveys. Further, we expect to gain a better understanding of the data on the books respondents have at home when such data are collected through different methods.

UNASSIGNED: Accurately assessing an individual\'s diet is vital in the management of personal nutrition and in the study of the effect of diet on health. Despite its importance, the tools available for dietary assessment remain either too imprecise, expensive, or burdensome for clinical or research use. Image-based methods offer a potential new tool to improve the reliability and accessibility of dietary assessment. Though promising, image-based methods are sensitive to adherence, as images cannot be captured from meals that have already been consumed. Adherence to image-based methods may be improved with appropriately timed prompting via text message.
UNASSIGNED: This study aimed to quantitatively examine the effect of prompt timing on adherence to an image-based dietary record and qualitatively explore the participant experience of dietary assessment in order to inform the design of a novel image-based dietary assessment tool.
UNASSIGNED: This study used a randomized crossover design to examine the intraindividual effect of 3 prompt settings on the number of images captured in an image-based dietary record. The prompt settings were control, where no prompts were sent; standard, where prompts were sent at 7:15 AM, 11:15 AM, and 5:15 PM for every participant; and tailored, where prompt timing was tailored to habitual meal times for each participant. Participants completed a text-based dietary record at baseline to determine the timing of tailored prompts. Participants were randomized to 1 of 6 study sequences, each with a unique order of the 3 prompt settings, with each 3-day image-based dietary record separated by a washout period of at least 7 days. The qualitative component comprised semistructured interviews and questionnaires exploring the experience of dietary assessment.
UNASSIGNED: A total of 37 people were recruited, and 30 participants (11 male, 19 female; mean age 30, SD 10.8 years), completed all image-based dietary records. The image rate increased by 0.83 images per day in the standard setting compared to control (P=.23) and increased by 1.78 images per day in the tailored setting compared to control (P≤.001). We found that 13/21 (62%) of participants preferred to use the image-based dietary record versus the text-based dietary record but reported method-specific challenges with each method, particularly the inability to record via an image after a meal had been consumed.
UNASSIGNED: Tailored prompting improves adherence to image-based dietary assessment. Future image-based dietary assessment tools should use tailored prompting and offer both image-based and written input options to improve record completeness.

BACKGROUND: In the evolving field of health care, multimodal generative artificial intelligence (AI) systems, such as ChatGPT-4 with vision (ChatGPT-4V), represent a significant advancement, as they integrate visual data with text data. This integration has the potential to revolutionize clinical diagnostics by offering more comprehensive analysis capabilities. However, the impact on diagnostic accuracy of using image data to augment ChatGPT-4 remains unclear.
OBJECTIVE: This study aims to assess the impact of adding image data on ChatGPT-4\'s diagnostic accuracy and provide insights into how image data integration can enhance the accuracy of multimodal AI in medical diagnostics. Specifically, this study endeavored to compare the diagnostic accuracy between ChatGPT-4V, which processed both text and image data, and its counterpart, ChatGPT-4, which only uses text data.
METHODS: We identified a total of 557 case reports published in the American Journal of Case Reports from January 2022 to March 2023. After excluding cases that were nondiagnostic, pediatric, and lacking image data, we included 363 case descriptions with their final diagnoses and associated images. We compared the diagnostic accuracy of ChatGPT-4V and ChatGPT-4 without vision based on their ability to include the final diagnoses within differential diagnosis lists. Two independent physicians evaluated their accuracy, with a third resolving any discrepancies, ensuring a rigorous and objective analysis.
RESULTS: The integration of image data into ChatGPT-4V did not significantly enhance diagnostic accuracy, showing that final diagnoses were included in the top 10 differential diagnosis lists at a rate of 85.1% (n=309), comparable to the rate of 87.9% (n=319) for the text-only version (P=.33). Notably, ChatGPT-4V\'s performance in correctly identifying the top diagnosis was inferior, at 44.4% (n=161), compared with 55.9% (n=203) for the text-only version (P=.002, χ2 test). Additionally, ChatGPT-4\'s self-reports showed that image data accounted for 30% of the weight in developing the differential diagnosis lists in more than half of cases.
CONCLUSIONS: Our findings reveal that currently, ChatGPT-4V predominantly relies on textual data, limiting its ability to fully use the diagnostic potential of visual information. This study underscores the need for further development of multimodal generative AI systems to effectively integrate and use clinical image data. Enhancing the diagnostic performance of such AI systems through improved multimodal data integration could significantly benefit patient care by providing more accurate and comprehensive diagnostic insights. Future research should focus on overcoming these limitations, paving the way for the practical application of advanced AI in medicine.