Faculty Bibliography

Accessing high-quality, open-access dermatopathology image datasets for learning and cross-referencing is a common challenge for clinicians and trainees. To establish a comprehensive open-access dermatopathology dataset for educational, cross-referencing, and machine-learning purposes, we employed a hybrid workflow to curate and categorize images from the PubMed Central (PMC) repository. We used specific keywords to extract relevant images, and classified them using a novel hybrid method that combined deep learning-based image modality classification with figure caption analyses. Validation on 651 manually annotated images demonstrated the robustness of our workflow, with an F-score of 89.6% for the deep learning approach, 61.0% for the keyword-based retrieval method, and 90.4% for the hybrid approach. We retrieved over 7,772 images across 166 diagnoses and released this fully annotated dataset, reviewed by board-certified dermatopathologists. Using our dataset as a challenging task, we found the current image analysis algorithm from OpenAI inadequate for analyzing dermatopathology images. In conclusion, we have developed a large, peer-reviewed, open-access dermatopathology image dataset, DermpathNet, which features a semi-automated curation workflow.

Dermatopathology has rapidly advanced due to the implementation of deep learning models and artificial intelligence (AI). From convolutional neural networks (CNNs) to transformer-based foundation models, these systems are now capable of accurate whole-slide analysis and multimodal integration. This review synthesizes the most recent advents of deep-learning architecture and synthesizes its evolution from first-generation CNNs to hybrid CNN-transformer systems to large-scale foundational models such as Paige's PanDerm AI and Virchow. Herein, we examine performance benchmarks from real-world deployments of major dermatopathology deep learning models (DermAI, PathAssist Derm), as well as emerging next-generation models still under research and development. We assess barriers to clinical workflow adoption such as dataset bias, AI interpretability, and government regulation. Further, we discuss potential future research directions and emphasize the need for diverse, prospectively curated datasets, explainability frameworks for trust in AI, and rigorous compliance to Good Machine-Learning-Practice (GMLP) to achieve safe and scalable deep learning dermatopathology models that can fully integrate into clinical workflows.

Radiation recall dermatitis is a known but rare adverse effect that is characterized by the development of dermatitis in the region of prior irradiated tissue triggered by exposure to a systemic agent. Capivasertib is a small-molecule inhibitor targeting the phosphatidylinositol 3-kinase/protein kinase B pathway recently approved in locally advanced and metastatic breast cancer; however, the safety of its use in the setting of palliative radiation is currently unclear. Here, we report a case of radiation recall dermatitis in a patient with metastatic breast cancer on capivasertib with history of radiation to the right lower extremity managed with corticosteroids, antibiotics, and switching to alpelisib.

Sarcoidosis is a multisystem disorder characterised by granulomatous inflammation affecting various organs. The skin is commonly involved and can serve as an initial indicator of disease. While the precise aetiology of sarcoidosis remains elusive, evidence suggests involvement of T-helper type (TH)-1 and TH-17 pathways. Psoriasis shares common inflammatory pathways with sarcoidosis, prompting the repurposing of biologic therapies approved for psoriasis for off-label treatment of cutaneous sarcoidosis. However, this approach has raised concerns due to the development of granulomatous eruptions in some patients. We present a case of a patient with psoriasis who was diagnosed with systemic sarcoidosis while on ustekinumab. We review previous cases of ustekinumab-associated sarcoidosis and discuss the challenges associated with utilising biologic agents originally intended for psoriasis treatment for sarcoidosis.

Correct coding is an important component of effective dermatology practice management. Over the past several years there have been updates to many commonly used codes within dermatology. This review highlights many of these updates, such as: the skin biopsy codes have been subdivided to reflect the different biopsy techniques. The definition of complex linear repairs has been updated and clarified. Outpatient and inpatient evaluation and management visits have new coding guidelines to determine level of care. Dermatopathology consultation codes have been updated and category III codes related to digital pathology have been created. Understanding the details and nuances of each of these categories of codes is vital to ensuring appropriate coding is performed.