UNASSIGNED: To understand whether financial barriers or the lack of accessibility to dermatology services was a significant motivation for the public to seek free skin cancer screening.
UNASSIGNED: An anonymous and voluntary survey was administered to participants of The Sun Bus free skin cancer screening program in 2023 at U.S. outdoor events in Colorado, Texas, Arizona, New Mexico, Iowa, Wyoming, Missouri, and Montana. 491 respondents answered questions on motivation, healthcare coverage, and demographics. Survey data was analyzed using Qualtrics\' crosstab IQ and statistical tests software.
UNASSIGNED: Skin screening found suspicious lesions in 45 % of 1300 participants with 18 % of respondents sharing a previous history of skin cancer. Concern for a lesion or Curiosity were the two top reasons for 60 % of respondents to seek free skin screening and remained the top reasons after data was stratified by gender, age, or income. Only 15 % of respondent were motivated by the cost of dermatology services or a long wait to see a dermatologist. A total of 38 % of people surveyed reported comprehensive plans covering skin screening while 46 % were unaware of the inclusion of screening in their healthcare plan. Notably, this unawareness increased up to 52 % among younger and less affluent respondents. Additionally, females were less likely than males to be aware of skin screening options in their healthcare plans.
UNASSIGNED: This work highlights the significance of promoting public awareness of dermatology services covered by health insurance and the need for continued efforts in skin cancer education and screening programs.

Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.

The occurrence of second primary malignancies is becoming increasingly important among cancer survivors. Melanoma, an aggressive neoplasm originating from the melanocytes, is responsible for most skin cancer-related deaths. This review aims to explore the risk of melanoma occurrence as a second primary cancer after the most common subtypes of hematologic neoplasia, a malignant disease originating from myeloid or lymphocytic cell lineages. Chronic lymphocytic leukemia (CLL) and non-Hodgkin lymphoma (NHL) are among the most associated subtypes with melanoma development. We also discuss the underlying hypotheses that may explain the associations between these malignancies and the impact of melanoma on survival. The review emphasizes the importance of increasing awareness of melanoma risk in hematologic cancer survivors, as it can lead to prompt recognition, improved skin surveillance, and better survival outcomes.

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Modern diagnostic procedures, such as three-dimensional total body photography (3D-TBP), digital dermoscopy (DD), and reflectance confocal microscopy (RCM), can improve melanoma diagnosis, particularly in high-risk patients. This study assessed the benefits of combining these advanced imaging techniques in a three-step programme in managing high-risk patients. This study included 410 high-risk melanoma patients who underwent a specialised imaging consultation in addition to their regular skin examinations in outpatient care. At each visit, the patients underwent a 3D-TBP, a DD for suspicious findings, and an RCM for unclear DD findings. The histological findings of excisions initiated based on imaging consultation and outpatient care were compared. Imaging consultation detected sixteen confirmed melanomas (eight invasive and eight in situ) in 39 excised pigmented lesions. Outpatient care examination detected seven confirmed melanomas (one invasive and six in situ) in 163 excised melanocytic lesions. The number needed to excise (NNE) in the imaging consultation was significantly lower than that in the outpatient care (2.4 vs. 23.3). The NNE was 2.6 for DD and 2.3 for RCM. DD, 3D-TBP, or RCM detected melanomas that were not detected by the other imaging methods. The three-step imaging programme improves melanoma detection and reduces the number of unnecessary excisions in high-risk patients.

BACKGROUND: There is great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems capable of identifying melanoma could save lives, enable immediate access to screenings, and reduce unnecessary care and health care costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system.
OBJECTIVE: The aim of this study was two-fold: (1) to determine the relative importance of the provider (in-person physician, physician via teledermatology, AI, personalized AI), costs of screening (free, 10€, 25€, 40€; 1€=US $1.09), and waiting time (immediate, 1 day, 1 week, 4 weeks) as attributes contributing to patients\' choices of a particular mode of skin cancer screening; and (2) to investigate whether sociodemographic characteristics, especially age, were systematically related to participants\' individual choices.
METHODS: A choice-based conjoint analysis was used to examine the acceptance of medical AI for a skin cancer screening from the patient\'s perspective. Participants responded to 12 choice sets, each containing three screening variants, where each variant was described through the attributes of provider, costs, and waiting time. Furthermore, the impacts of sociodemographic characteristics (age, gender, income, job status, and educational background) on the choices were assessed.
RESULTS: Among the 383 clicks on the survey link, a total of 126 (32.9%) respondents completed the online survey. The conjoint analysis showed that the three attributes had more or less equal importance in contributing to the participants\' choices, with provider being the most important attribute. Inspecting the individual part-worths of conjoint attributes showed that treatment by a physician was the most preferred modality, followed by electronic consultation with a physician and personalized AI; the lowest scores were found for the three AI levels. Concerning the relationship between sociodemographic characteristics and relative importance, only age showed a significant positive association to the importance of the attribute provider (r=0.21, P=.02), in which younger participants put less importance on the provider than older participants. All other correlations were not significant.
CONCLUSIONS: This study adds to the growing body of research using choice-based experiments to investigate the acceptance of AI in health contexts. Future studies are needed to explore the reasons why AI is accepted or rejected and whether sociodemographic characteristics are associated with this decision.

This study investigated the outcomes and follow-up behaviors of participants from two free skin cancer screening events in the United States. This survey, with 296 participants and a 31% response rate, gathered information on participant demographics, personal history of skin cancer, knowledge of skin screening practices, and follow-up behaviors. There was a high follow-up rate of 92.3% among individuals recommended for further dermatological consultation, but a low (22%) concordance rate between the preliminary diagnoses from the screening and patient-recalled diagnoses. Additionally, about one-sixth of participants identified limited access to care as a motivation for participating in the screening. The study emphasizes the need to improve awareness about the limitations of free screenings, enhance participant education, and ensure equitable access to skin cancer screening. Future research should focus on factors influencing follow-up behaviors and the development of targeted interventions to increase awareness and access to skin cancer screening.

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy.

In 2008, a nationwide skin cancer screening (SCS) program was implemented in Germany. However, participation rates remain low. YouTube videos on SCS might educate eligible persons to undergo SCS. Until now, no scientific evaluation of the quality of videos available for German-speaking persons eligible for SCS has been performed. Here, we identified and evaluated videos on SCS provided on YouTube. YouTube was searched in May 2022 for German terms related to SCS. Two authors evaluated the videos of the first three pages that met the predefined eligibility criteria. The quality of the videos´ information was evaluated using DISCERN and the Global Quality Scale (GQS). The understandability and actionability were assessed with the Patient Education Materials Assessment Tool (PEMAT). The reliability was assessed with the Journal of American Medical Association (JAMA) score. Subgroup differences were identified by the Kruskal-Wallis test. Overall, 38 videos were included in the evaluation. Most videos were provided by health professionals (clinics and practices). The average scores (mean (SD)) for the individual tools were as follows: DISCERN 3.1/5 points (± 0.52), GQS 3.72/5 points (± 0.7), understandability 64,27% (± 13.53%), actionability 58.22% (± 15.18%), JAMA 37.17% (± 18.94%). These results indicate a mediocre to good understandability, a mediocre quality and actionability, and a low reliability. Videos that were assessed as useful were of significantly better quality. An improvement of freely available informational videos on SCS, especially with regard to the reliability criteria, is urgently needed.

The national skin cancer screening (SCS) was introduced in Germany in 2008. However, public awareness and participation rates remain low. There are no campaigns or target group-specific invitation strategies for SCS yet. Thus, our aim was to derive potential suggestions on how to best inform German residents about the possibility of SCS.
Semi-structured, individual interviews with male and female German residents aged ≥35 years were conducted in Erlangen (Germany) to explore opportunities on raising awareness of SCS. Interviews were audiotaped, transcribed verbatim, and analyzed using qualitative content analysis.
Overall, 39 persons were interviewed. About 79.5% (31/39) had already undergone at least one SCS. Numerous opportunities to raise awareness of the possibility of SCS were suggested which were categorized into three main topics: the role of public promotion, health-related caregivers, and health insurance. Similar themes were identified for inviting entitled persons to undergo SCS after 2 years. Furthermore, age-dependent communication approaches were proposed, that is, younger persons should be approached electronically, while the older generation should be targeted with traditional media like mail.
The results of this project will inform stakeholders to take appropriate actions. The findings may contribute to increase participation rates in SCS and thus lead to earlier detection of skin cancer.