What Are Patients' Perceptions and Attitudes Regarding the Use of Artificial Intelligence in Skin Cancer Screening and Diagnosis? Narrative Review.

DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection

Solid organ transplant recipients (SOTRs) have a high risk of developing aggressive skin cancers. However, there are no standardized triage guidelines to assist dermatology clinics with scheduling new patients pre- or post-transplant. Dermatologic care of SOTRs requires multidisciplinary coordination, extensive assessment, tailored counseling, and longitudinal care. Specialized high-risk transplant clinics are designed to address this clinical need but are a limited resource. This triage algorithm aims to provide a practical framework for tertiary care centers or community practice clinics receiving pre- or post-transplant referrals for active concerning growths or routine skin cancer screening exams. In summary, our expert panel recommends SOTRs are seen within 1–2 weeks for evaluation of an active growth and triaged according to their risk factors for the initial post-transplant screening visit (6 months–2+ years post-transplant). Transplant candidates should be seen for pre-transplant evaluation within 1 month of the referral for a skin cancer screening exam, depending on the transplant team’s timeline and dermatologist availability. Overall, dermatologists face numerous challenges in caring for transplant patients, and scheduling these patients in a timely manner according to the acuity of their needs will facilitate prevention and early diagnosis of skin cancer, thus improving transplant patient outcomes.

Skin cancers, including melanoma and keratinocyte cancers, are among the most common cancers worldwide, and their incidence is rising in most populations. Earlier detection of skin cancer leads to better outcomes for patients. Artificial intelligence (AI) technologies have been applied to skin cancer diagnosis, but many technologies lack clinical evidence and/or the appropriate regulatory approvals. There are few qualitative studies examining the views of relevant stakeholders or evidence about the implementation and positioning of AI technologies in the skin cancer diagnostic pathway. This study aimed to understand the views of several stakeholder groups on the use of AI technologies to facilitate the early diagnosis of skin cancer, including patients, members of the public, general practitioners, primary care nurse practitioners, dermatologists, and AI researchers. This was a qualitative, semistructured interview study with 29 stakeholders. Participants were purposively sampled based on age, sex, and geographical location. We conducted the interviews via Zoom between September 2022 and May 2023. Transcribed recordings were analyzed using thematic framework analysis. The framework for the Nonadoption, Abandonment, and Challenges to Scale-Up, Spread, and Sustainability was used to guide the analysis to help understand the complexity of implementing diagnostic technologies in clinical settings. Major themes were "the position of AI in the skin cancer diagnostic pathway" and "the aim of the AI technology"; cross-cutting themes included trust, usability and acceptability, generalizability, evaluation and regulation, implementation, and long-term use. There was no clear consensus on where AI should be placed along the skin cancer diagnostic pathway, but most participants saw the technology in the hands of either patients or primary care practitioners. Participants were concerned about the quality of the data used to develop and test AI technologies and the impact this could have on their accuracy in clinical use with patients from a range of demographics and the risk of missing skin cancers. Ease of use and not increasing the workload of already strained health care services were important considerations for participants. Health care professionals and AI researchers reported a lack of established methods of evaluating and regulating AI technologies. This study is one of the first to examine the views of a wide range of stakeholders on the use of AI technologies to facilitate early diagnosis of skin cancer. The optimal approach and position in the diagnostic pathway for these technologies have not yet been determined. AI technologies need to be developed and implemented carefully and thoughtfully, with attention paid to the quality and representativeness of the data used for development, to achieve their potential.

Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically reviewed studies on artificial intelligence and machine learning (AI/ML) algorithms that aim to facilitate the early diagnosis of skin cancers, focusing on their application in primary and community care settings. We searched MEDLINE, Embase, Scopus, and Web of Science (from Jan 1, 2000, to Aug 9, 2021) for all studies providing evidence on applying AI/ML algorithms to the early diagnosis of skin cancer, including all study designs and languages. The primary outcome was diagnostic accuracy of the algorithms for skin cancers. The secondary outcomes included an overview of AI/ML methods, evaluation approaches, cost-effectiveness, and acceptability to patients and clinicians. We identified 14 224 studies. Only two studies used data from clinical settings with a low prevalence of skin cancers. We reported data from all 272 studies that could be relevant in primary care. The primary outcomes showed reasonable mean diagnostic accuracy for melanoma (89·5% [range 59·7-100%]), squamous cell carcinoma (85·3% [71·0-97·8%]), and basal cell carcinoma (87·6% [70·0-99·7%]). The secondary outcomes showed a heterogeneity of AI/ML methods and study designs, with high amounts of incomplete reporting (eg, patient demographics and methods of data collection). Few studies used data on populations with a low prevalence of skin cancers to train and test their algorithms; therefore, the widespread adoption into community and primary care practice cannot currently be recommended until efficacy in these populations is shown. We did not identify any health economic, patient, or clinician acceptability data for any of the included studies. We propose a methodological checklist for use in the development of new AI/ML algorithms to detect skin cancer, to facilitate their design, evaluation, and implementation.

Artificial intelligence (AI) has made remarkable advances in recent years that have ushered in a new era of precision medicine, particularly when it comes to the early diagnosis of skin cancer. This chapter explores the potential role of artificial intelligence (AI), which is powered by imaging in dermatology, with a focus on early skin cancer diagnosis. This allows artificial intelligence to analyze complex dermatological photos with statistically greater accuracy, significantly streamlining the diagnostic process. It makes use of the latest algorithms and teaching approaches. AIbased technologies integrated with existing diagnostic methods, such as dermoscopy and molecular diagnostics, offer a comprehensive solution to the identification of skin tumors. This strategy improves the ability to detect neoplasms at their most early and treatable periods. Evidence of AI-driven solutions is applied successfully in clinical practice with case studies provided by Leicester ICS and Lancashire ICB. The examples depicted here demonstrate how AI may broaden diagnostic reach, reduce wait times, and provide more precise evaluations with flow-through benefits for patients. Lastly, the chapter explores several ethical and regulatory topics necessary for implementing artificial intelligence within health care. Special emphasis is placed on its importance in terms of data protection, security, reduction of bias, and patient approval. Future work in this field would include the development of real-time diagnostic and telemedicine applications, further optimization of AI algorithms, and better integration with other diagnostic modalities. Elimination of biases and improving generalizability of AI models across diverse populations remains a major area of ongoing challenge. Research and development of AI-powered imaging is maturing to the point where it could transform early-stage skin cancer detection and treatment. This promises a future where healthcare becomes more precise, efficient, and accessible.

e21587 Background: Firefighters have both an increased risk (21%) and 20-year younger median age (42) at diagnosis of melanoma compared to the general population. The World Health Organization classifies firefighters’ occupational exposures as "carcinogenic to humans," but there is limited knowledge regarding sun protection habits, skin cancer history, and community outreach in this at-risk population. Methods: Free, full-body skin examinations conducted by board-certified dermatologists using the American Academy of Dermatology’s SPOT Skin CancerTM screening form were offered to firefighters from 26 cities and towns in Massachusetts on two separate days in August 2021 and October 2022. Data from the screening form and subsequent skin biopsy results were obtained and reported. Results: Of 195 firefighters screened, 30 (15.4%) were referred for biopsy. All were white males, mean age was 44.7 years, 26.7% had prior tanning bed exposure, 80% had prior blistering sunburns, 20% used sunscreen regularly, 86.7% did not have a regular dermatologist, and 6.7% had a history of keratinocyte-based skin cancer. Nineteen (63.3%) had biopsies, most frequently on the back or face, which resulted in three nodular basal cell carcinomas and 11 dysplastic nevi (seven moderate-to-severe). Conclusions: Firefighters are an underrecognized vulnerable population for the development of skin cancer. Community outreach in the form of free skin cancer screenings is a successful model for increasing awareness and early diagnosis of skin cancer among this population. In addition to providing access to care, community screenings offer tailored educational opportunities that promote skin cancer prevention in this high-risk group.

The role of the nurse in skin cancer prevention, screening, and early detection

Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI's effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment.

In this research, present a Hybrid Attention and Triple-Force Deep Learning (TFDL) framework for automated skin cancer detection and classification via dermoscopic images. Our proposed framework combines the Hybrid Attention U-Net (HAU-Net) for precise segmentation, combining Channel Attention (CAM), and Spatial Attention (SAM) modules to increase feature localization and delineate lesion boundaries. The Adaptive Ensemble Learning model consisting of three state-of-the-art convolutional networks (VGG16, EfficientNet-B3, and ResNet50) is then used for classification, where feature aggregation is dynamically weighted, referring to the acronym AEL. The design of AEL enables improved learning efficiency, generalization, and interpretability of the model. The experiments using benchmark skin cancer datasets (HAM10000, ISIC, and DermNet) for evaluation purposes and show that we achieve 98.1% accurate diagnostic accuracy, 97.5% sensitivity, and 98.6% specificity when combined. These accuracy metrics are 3–5% better than that of the original CNN or even existing ensemble models in diagnostic accuracy. The findings of this research confirm that the proposed triple force deep learning framework provides a clinically viable, explainable, and high-precision AI system for real-time skin cancer screening and early diagnosis of skin cancer, reducing diagnostic errors, and assisting in the clinical decision-making process for dermatologists.

Background: After the outbreak of the corona virus disease-19 (COVID-19) pandemic, teledermatology was implemented in the Hungarian public healthcare system for the first time. Our objective was to assess aggregated diagnostic agreements and to determine the effectiveness of an asynchronous teledermatology system for skin cancer screening. Methods: This retrospective single-center study included cases submitted for teledermatology consultation during the first wave of the COVID-19 pandemic. Follow-up of the patients was performed to collect the results of any subsequent personal examination. Results: 749 patients with 779 lesions were involved. 15 malignant melanomas (9.9%), 78 basal cell carcinomas (51.3%), 21 squamous cell carcinomas (13.8%), 7 other malignancies (4.6%) and 31 actinic keratoses (20.4%) were confirmed. 87 malignancies were diagnosed in the high-urgency group (42.2%), 49 malignancies in the moderate-urgency group (21.6%) and 16 malignancies in the low-urgency group (4.6%) (p < 0.0001). Agreement of malignancies was substantial for primary (86.3%; κ = 0.647) and aggregated diagnoses (85.3%; κ = 0.644). Agreement of total lesions was also substantial for primary (81.2%; κ = 0.769) and aggregated diagnoses (87.9%; κ = 0.754). Conclusions: Our findings showed that asynchronous teledermatology using a mobile phone application served as an accurate skin cancer screening system during the first wave of the COVID-19 pandemic.

Major mortality rate among human beings is due to cancer. Early diagnosis of Skin cancer especially Melanoma is having the potentiality to reduce morbidity as the major reason behind the disastrous repercussions of three out four homo-sapiens is due to skin cancer. Detection of cancer using machine learning and deep learning algorithms makes it very much feasible and economical. The ultimate focus of this paper is for detecting skin cancer at an early stage and helping to combat the increasing cases in skin cancer patients. In this paper, we have implemented different types of CNNs of different configurations on categorical classification where architectures were trained on different input image size and selecting of best architecture was based on various metric evaluations like Maximum Accuracy, Precision, Recall, and F1 score and best architecture has achieved high accuracy and performed outstandingly in all the evaluation section. Architecture 4 performed overall excellent in terms of every field of metric evaluations. This architecture will be a helpful tool for diagnosing skin cancer at an early stage and will take the less computational cost for classifying the skin cancer disease.

Purpose: Skin Cancer (SC) is one of the most threatening diseases worldwide. Skin cancer diagnosis is still a challenging task. Recently, Deep Learning (DL) algorithms have demonstrated exceptional performance on many tasks compared to the traditional Machine Learning (ML) methods. Particularly, they have been applied to skin disease diagnosis tasks. The aim is to provide a comprehensive overview of the advancements, challenges, and potential applications in this critical domain of dermatology. Materials and Methods: The review encompasses a wide range of scholarly articles, research papers, and relevant literature focusing on integrating deep learning techniques in skin cancer diagnosis. Materials include studies that employ various imaging modalities such as dermoscopy, histopathology, and other advanced imaging technologies. The initial phase involves acquiring images of SC from various patients through primary sources and standardized databases. Subsequently, a thorough data cleaning process is implemented, encompassing noise reduction, resizing, and contrast enhancement. Further refinement occurs through the segmentation of the malignant sections, employing edge-based, region-based, and morphological-based techniques. Feature extraction is followed by deep learning approaches, it enhanced with Federated Learning (FL) that is applied to image classification. Finally, leveraging FL-aided deep learning techniques, the images are categorized as either malignant or non-cancerous. Results: The metrics include Accuracy (AC%), Specificity (Spe%), Sensitivity (Sen%), and Dice Coefficient (DC%), providing a comprehensive evaluation of the classification performance. Generative Adversarial Network (G-AN) demonstrates the highest accuracy 98.5% among the considered techniques, making it the top-performing neural network architecture for skin cancer classification. Conclusion: This review was undertaken by pulling data from 90 papers published between the years 2019 and 2023, it provides a thorough statistical analysis. A review of various neural network algorithms for skin cancer identification and classification, despite Generative Adversarial Network, has emerged as the most promising approach, underscoring their potential to revolutionize the accurate early diagnosis of skin cancer. Finally, this survey will be beneficial for SCD researchers.

Non-melanoma-skin cancer is an emerging clinical problem in the elderly, fair skinned population which predominantly affects patients aged older than 70 years. Its steady increase in incidence rates and morbidity is paralleled by related medical costs. Despite the fact that many elderly patients are in need of care and are living in nursing homes, specific data on the prevalence of skin cancer in home care and the institutional long-term care setting is currently lacking. A representative multicenter prevalence study was conducted in a random sample of ten institutional long-term care facilities in the federal state of Berlin, Germany. In total, n = 223 residents were included. Actinic keratoses, the precursor lesions of invasive cutaneous squamous cell carcinoma were the most common epithelial skin lesions (21.1%, 95% CI 16.2 to 26.9). Non-melanoma skin cancer was diagnosed in 16 residents (7.2%, 95% CI 4.5 to 11.3). None of the residents had a malignant melanoma. Only few bivariate associations were detected between non-melanoma skin cancer and demographic, biographic and functional characteristics. Male sex was significantly associated with actinic keratosis whereas female sex was associated with non-melanoma skin cancer. Smoking was associated with an increased occurrence of non-melanoma skin cancer. Regular dermatology check-ups in nursing homes would be needed but already now due to financial limitations, lack of time in daily clinical practice and limited number of practising dermatologists, it is not the current standard. With respect to the worldwide growing aging population new programs and decisions are required. Overall, primary health care professionals should play a more active role in early diagnosis of skin cancer in nursing home residents. Dermoscopy courses, web-based or smartphone-based applications and teledermatology may support health care professionals to provide elderly nursing home residents an early diagnosis of skin cancer.

BackgroundArtificial Intelligence (AI) is significantly transforming dermatology, particularly in early skin cancer detection and diagnosis. This technological advancement addresses a crucial public health issue by enhancing diagnostic accuracy, efficiency, and accessibility. AI integration in medical imaging and diagnostic procedures offers promising solutions to the limitations of traditional methods, which often rely on subjective clinical evaluations and histopathological analyses. This study systematically reviews current AI applications in skin cancer classification, providing a comprehensive overview of their advantages, challenges, methodologies, and functionalities.MethodologyIn this study, we conducted a comprehensive analysis of artificial intelligence (AI) applications in the classification of skin cancer. We evaluated publications from three prominent journal databases: Scopus, IEEE, and MDPI. We conducted a thorough selection process using the PRISMA guidelines, collecting 1,156 scientific articles. Our methodology included evaluating the titles and abstracts and thoroughly examining the full text to determine their relevance and quality. Consequently, we included a total of 95 publications in the final study. We analyzed and categorized the articles based on four key dimensions: advantages, difficulties, methodologies, and functionalities.ResultsAI-based models exhibit remarkable performance in skin cancer detection by leveraging advanced deep learning algorithms, image processing techniques, and feature extraction methods. The advantages of AI integration include significantly improved diagnostic accuracy, faster turnaround times, and increased accessibility to dermatological expertise, particularly benefiting underserved areas. However, several challenges remain, such as concerns over data privacy, complexities in integrating AI systems into existing workflows, and the need for large, high-quality datasets. AI-based methods for skin cancer detection, including CNNs, SVMs, and ensemble learning techniques, aim to improve lesion classification accuracy and increase early detection. AI systems enhance healthcare by enabling remote consultations, continuous patient monitoring, and supporting clinical decision-making, leading to more efficient care and better patient outcomes.ConclusionsThis comprehensive review highlights the transformative potential of AI in dermatology, particularly in skin cancer detection and diagnosis. While AI technologies have significantly improved diagnostic accuracy, efficiency, and accessibility, several challenges remain. Future research should focus on ensuring data privacy, developing robust AI systems that can generalize across diverse populations, and creating large, high-quality datasets. Integrating AI tools into clinical workflows is critical to maximizing their utility and effectiveness. Continuous innovation and interdisciplinary collaboration will be essential for fully realizing the benefits of AI in skin cancer detection and diagnosis.