Human–computer collaboration for skin cancer recognition

By P. Tschandl, C. Rinner, Z. Apalla, Giuseppe Argenziano, Noel C. F. Codella, Allan Halpern, M. Janda, A. Lallas, C. Longo, J. Malvehy and 6 more. Nature Medicine, 2020

912 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biophysics, Oncology

View Paper: https://www.semanticscholar.org/paper/af1196f56db222abf8cba22d602405a6eb80b53b

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Citations in the corpus

A patient-centric dataset of images and metadata for identifying melanomas using clinical context (2020, background): “g is rapidly progressing and has the potential to reduce melanoma-associated mortality, morbidity, and healthcare costs by improving access to expertise, diagnostic accuracy, and screening efficiency [1, 2, 3]. Here we present a dermatology image dataset that includes patient- and lesion-related cli”
Disparities in dermatology AI performance on a diverse, curated clinical image set (2021, background): “Artiﬁcial intelligence (AI) tools have been suggested as a way to improve early skin cancer detection with rapid development of AI algorithms which claim to detect skin cancer over the last two years (Tschandl et al., 2020; Esteva et al., 2017; Daneshjou et al., 2021).”
Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network (2022, background): “It also defends us against bacteria and the elements, and the presence of intercellular lipids prevents moisture loss [3], [4].”
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset (2021, background): “How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? The emergence of deep neural network models that can accurately classify images of skin conditions presents an opportunity to improve dermatology and healthcare at large”
Evaluation and mitigation of the limitations of large language models in clinical decision-making (2024, neutral): “Importantly, studies as to the impact of automation bias 53,54 and human– AI interaction biases could also be explored in such a context 55–57 .”
AI in health and medicine (2022, background): “Furthermore, some clinicians may benefit more from AI assistance than others; studies suggest that less experienced clinicians, such as trainees, benefit more from AI input than their more experienced peers 8,92 .”