Wu, E. et al. How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals. Nat. Med. 27, 582–584 (2021).
Reddy, S. Explainability and artificial intelligence in medicine. Lancet Digit. Health 4, E214–E215 (2022).
Young, A. T. et al. Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models. npj Digit. Med. 4, 10 (2021).
DeGrave, A. J., Janizek, J. D. & Lee, S.-I. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3, 610–619 (2021).
Singh, N. et al. Agreement between saliency maps and human-labeled regions of interest: applications to skin disease classification. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 3172–3181 (IEEE, 2020).
Bissoto, A., Fornaciali, M., Valle, E. & Avila, S. (De) constructing bias on skin lesion datasets. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2766–2774 (IEEE, 2019).
Winkler, J. K. et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol. 155, 1135–1141 (2019).
Singla, S., Pollack, B., Chen, J. & Batmanghelich, K. Explanation by progressive exaggeration. In International Conference on Learning Representations (ICLR, 2020).
Mertes, S., Huber, T., Weitz, K., Heimerl, A., & Andr, E. GANterfactual—counterfactual explanations for medical non-experts using generative adversarial learning. Front. Artif. Intell. 5, 825565 (2022).
Ghoshal, B. & Tucker, A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. Preprint at arXiv:2003.10769 (2020).
Ozturk, T. et al. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med. 121, 103792 (2020).
Brunese, L., Mercaldo, F., Reginelli, A. & Santone, A. Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput. Methods Programs Biomed. 196, 105608 (2020).
Karim, M. et al. DeepCOVIDExplainer: explainable COVID-19 diagnosis from chest X-ray images. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 1034–1037 (IEEE, 2020).
Geirhos, R. et al. Shortcut learning in deep neural networks. Nat. Mach. Intell. 2, 665–673 (2020).
Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).
Liu, Y. et al. A deep learning system for differential diagnosis of skin diseases. Nat. Med. 26, 900–908 (2020).
Han, S. S. et al. Augmented intellignece dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J. Invest. Dermatol. 140, 1753–1761 (2020).
Sun, M. D. et al. Accuracy of commercially available smartphone applications for the detection of melanoma. Br. J. Dermatol. 186, 744–746 (2022).
Freeman, K. et al. Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies. Br. Med. J. 368, m127 (2020).
Beltrami, E. J. et al. Artificial intelligence in the detection of skin cancer. J. Am. Acad. Dermatol. 87, 1336–1342 (2022).
Daneshjou, R. et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci. Adv. 8, eabq6147 (2022).
Han, S. S. et al. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. J. Invest. Dermatol. 138, 1529–1538 (2018).
Ha, Q., Liu, B. & Liu, F. Identifying melanoma images using EfficientNet ensemble: winning solution to the SIIM-ISIC melanoma classification challenge. Preprint at arXiv:2010.05351 (2020).
Rotemberg, V. et al. A patient-centric dataset of images and metadata for identifying melanomas using clinical context. Sci. Data 8, 34 (2021).
Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5, 180161 (2018).
Combalia, M. et al. BCN20000: dermoscopic lesions in the wild. Preprint at arXiv:1908.02288 (2019).
Groh, M. et al. Evaluating deep neural networks trained on clinical images in dermatology with the Fitzpatrick 17k dataset. In Proceedings of the Computer Vision and Pattern Recognition (CVPR) Sixth ISIC Skin Image Analysis Workshop (IEEE, 2021).
Karras, T. et al. Analyzing and improving the image quality of StyleGAN. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 8107–8116 (IEEE, 2020).
Shi, K. et al. A retrospective cohort study of the diagnostic value of different subtypes of atypical pigment network on dermoscopy. J. Am. Acad. Dermatol. 83, 1028–1034 (2020).
Yélamos, O. et al. Usefulness of dermoscopy to improve the clinical and histopathologic diagnosis of skin cancers. J. Am. Acad. Dermatol. 80, 365–377 (2019).
Halpern, A. C., Marghoob, A. A. & Reiter, O. Melanoma Warning Signs: What You Need to Know About Early Signs of Skin Cancer (Skin Cancer Foundation, 2021); https://www.skincancer.org/skin-cancer-information/melanoma/melanoma-warningsigns-and-images/. Accessed April 2023.
Massi, D., De Giorgi, V., Carli, P. & Santucci, M. Diagnostic significance of the blue hue in dermoscopy of melanocytic lesions: a dermoscopic-pathologic study. Am. J. Dermatopathol. 23, 463–469 (2001).
Marghoob, N. G., Liopyris, K. & Jaimes, N. Dermoscopy: a review of the structures that facilitate melanoma detection. J. Osteopath. Med. 119, 380–390 (2019).
Oliveria, S. A., Saraiya, M., Geller, A. C., Heneghan, M. K. & Jorgensen, C. Sun exposure and risk of melanoma. Arch. Dis. Child. 91, 131–138 (2006).
Zhu, J.-Y., Park, T., Isola, P. & Efros, A. A. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV) 2223–2232 (IEEE, 2017).
Illumination, I. C. on. ISO/CIE 11664-5:2016(e) Colorimetry—part 5: CIE 1976 L*u*v* colour space and u’, v’ uniform chromaticity scale diagram (2016).
Deng, Z., Gijsenij, A. & Zhang, J. Source camera identification using auto-white balance approximation. In 2011 IEEE International Conference on Computer Vision 57–64 (IEEE, 2011).
Rader, R. K. et al. The pink rim sign: location of pink as an indicator of melanoma in dermoscopic images. J. Skin Cancer 2014, 719740 (2014).
Tschandl, P. et al. Human–computer collaboration for skin cancer recognition. Nat. Med. 26, 1229–1234 (2020).
Tschandl, P. et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based international, diagnostic study. Lancet Oncol. 20, 938–947 (2019).
Weber, P., Sinz, C., Rinner, C., Kittler, H. & Tschandl, P. Perilesional sun damage as a diagnostic clue for pigmented actinic keratosis and Bowen’s disease. J. Eur. Acad. Dermatol. Venereol. 35, 2022–2026 (2021).
Fitzpatrick, J. E., High, W. A. & Kyle, W. L. Urgent Care Dermatology: Symptom-Based Diagnosis. 477–488 (Elsevier, 2018).
Wu, E. et al. Toward Stronger FDA Approval Standards for AI Medical Devices (Stanford University Human-centered Artificial Intelligence (2022).
Bansal, G. et al. Does the whole exceed its parts? The effect of AI explanations on complementary team performance. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (ACM, 2021).
Rok, R. & Weld, D. S. In search of verifiability: explanations rarely enable complementary performance in AI-advised decision making. Preprint at arXiv:2305.07722v3 (2023).
Roth, L. Looking at Shirley, the ultimate norm: colour balance, image technologies, and cognitive equity. Can. J. Commun. 34, 111–136 (2009).
Lester, J. C., Clark, L., Linos, E. & Daneshjou, R. Clinical photography in skin of colour: tips and best practices. Br. J. Dermatol. 184, 1177–1179 (2021).
Poplin, R. et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat. Biomed. Eng. 2, 158–164 (2018).
Yamashita, T. et al. Factors in color fundus photographs that can be used by humans to determine sex of individuals. Transl Vis. Sci. Technol. 9, 4 (2020).
Codella, N. C. F. et al. Skin lesion analysis toward melanoma detection: a challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), hosted by the International Skin Imaging Collaboration (ISIC). In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI), 168–172 (IEEE, 2018).
Tan, M. et al. MnasNet: platform-aware neural architecture search for mobile. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2820–2828 (IEEE, 2019).
Jacob, B. et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2704–2713 (IEEE, 2018)
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770–778 (IEEE, 2016).
Tan, M. & Le, Q. EfficientNet: rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML 2019) 6105–6114 (PMLR, 2019).
Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 7132–7141 (IEEE, 2018).
Zhang, H. et al. ResNeSt: split-attention networks. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2735–2745 (IEEE, 2022).
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2818–2826 (IEEE, 2016).
Giotis, I. et al. MED-NODE: a computer-assisted melanoma diagnosis system using non-dermoscopic images. Expert Syst. Appl. 42, 6578–6585 (2015).
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