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Innovation in healthcare has led to the development of numerous implantable medical devices (IMDs). However, advances in our knowledge of breast implant–associated malignancies have raised questions about the prevalence, etiology, and management of malignancies associated with non-breast IMDs. The objective of this study was to examine the prevalence and characteristics of malignancies associated with non-breast IMDs. An expert medical librarian developed the search strategy for this review. Databases included MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. In addition, gray literature sources were searched, and relevant references from systematic reviews and meta-analyses were included. The PRISMA guideline was followed for the review. Risk of bias was evaluated with the JBI Critical Appraisal tools. A total of 12,230 articles were reviewed, with a total of 77 meeting inclusion criteria. Risk of bias was highest with case reports (moderate, average of 65.1% with range of 37.5% to 100%) and low for the remaining study types. In total, 616 cases of IMD-associated malignancies were identified. Malignancies associated with IMDs were reported in the head and neck (543, 88.1%), lower extremity (57, 9.6%), thorax (9, 1.4%), abdomen (3, 0.5%), and genitourinary system (2, 0.3%). The most common malignancy type in the lower extremity was sarcoma, in the head and neck was squamous cell carcinoma, and in the thorax was lymphoma. This study is the first comprehensive systematic review of its kind. Overall, the oncologic risk of IMDs is low. The discussion of malignancy is an important part of the overall consent process, and malignancy should be considered with any new signs or symptoms in the anatomic area of an implant. More data are needed to better understand how primary malignancies occur around IMDs and how to reduce this risk. Level of Evidence: 3 [ABSTRACT FROM AUTHOR]

There is a global rise in skin cancer incidence, resulting in an increase in patient care needs and healthcare costs. To optimize health care planning, costs, and patient care, Ontario Health developed a provincial skin cancer plan to streamline the quality of care. We conducted a systematic review and a grey literature search to evaluate the definitions and management of skin cancer within other jurisdictions, as well as a provincial survey of skin cancer care practices, to identify care gaps. The systematic review did not identify any published comprehensive skin cancer management plans. The grey literature search revealed skin cancer plans in isolated regions of the United Kingdom (U.K.), National Institute for Health and Care Excellence (NICE) guidelines for skin cancer quality indicators and regional skin cancer biopsy clinics, and wait time guidelines in Australia and the U.K. With the input of the Ontario Cancer Advisory Committee (CAC), unique definitions for complex and non-complex skin cancers and the appropriate cancer services were created. A provincial survey of skin cancer care yielded 44 responses and demonstrated gaps in biopsy access. A skin cancer pathway map was created and a recommendation was made for regional skin cancer biopsy clinics. We have created unique definitions for complex and non-complex skin cancer and a skin cancer pathways map, which will allow for the implementation of both process and performance metrics to address identified gaps in care. [ABSTRACT FROM AUTHOR]

Introduction: Self-examinations for skin cancer detection are limited by sensitivity. ChatGPT-4 has image recognition capabilities that can be a useful adjunct for screening cancers and tele-health applications. This study investigated the efficacy of ChatGPT-4 in identifying skin lesions. Methods: Dermoscopic images were retrospectively selected from the PH 2 dataset, categorized by clinical diagnosis, and uploaded to ChatGPT-4 with a predesigned prompt. Responses were compared against clinical diagnoses. Confidence intervals were calculated using the bootstrap method assessing precision and significance was calculated using McNemar's test. Analyses were performed using Jupyter Notebook and Python. Results: The GPT-4 model showed moderate performance in melanoma detection with 68.5% accuracy, 52.5% sensitivity, and 72.5% specificity, significantly differing from the clinical standard ( P  = .002). For suspicious lesion detection, it performed better with 68.0% accuracy, 78.0% precision, and 70.0% F-measure, still not closely matching clinical diagnosis for atypical nevi and melanoma ( P  = .0169). Conclusion: The statistical difference between ChatGPT-4 diagnosis of melanoma and suspicious lesions compared with clinical diagnoses and other AI models suggests the need for improvement in ChatGPT-4 algorithms. This study's limitations included the use of a secondary care database with a higher melanoma incidence, high-quality dermoscopic images that limit generalizability, a small sample size lacking diversity, and the need for larger datasets to validate findings in broader contexts., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© 2025 The Author(s).)