Dermatologist-level classification of skin cancer with deep neural networks
Open accessAbstract
Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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Esteva et al. found that a DL network has the same ability to diagnose disease as twenty-one board-certified dermatologists using 129,450 images of 2032 diseases.
“Esteva et al. [45] found that a DL network has the same ability to diagnose the disease as twenty-one board-certified dermatologists using 129,450 images of 2032 diseases.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [45]
The Abstract reports training on 129,450 images spanning 2,032 diseases and performance comparable to 21 board-certified dermatologists. But the comparison was limited to specific image-based skin-lesion classification tasks, not diagnosis across all 2,032 diseases or in clinical practice. (LLM, conf=0.85)
Experimental — accuracy not yet measuredEsteva et al. found a CNN achieved a sensitivity of 91.0% and specificity of 88.9% in detecting malignant lesions.
“The CNN achieved a sensitivity of 91.0% and specificity of 88.9% in detecting malignant lesions”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [21]
The paper’s Abstract reports that the CNN distinguished malignant from benign skin lesions and evaluates its sensitivity and specificity. It does not report the claimed pairing of 91.0% sensitivity and 88.9% specificity; the figure discussed there gives sensitivity–specificity curves and an area under the curve over 91%, which is a different measure. (LLM, conf=0.88)
Experimental — accuracy not yet measuredEsteva et al. showed the model had comparable diagnostic performance to dermatologists, with sensitivity 91% and specificity 88.9%.
“They showed that the model had comparable diagnostic performance to dermatologists, with a sensitivity of 91% and a specificity of 88.9%.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [21]
The Methods section (“Sensitivity–specificity curves with different questions”; page unavailable) says the CNN was on par with dermatologists, supporting the performance comparison. The comparison passage reports an area under the curve over 91%, not 91% sensitivity, and the passages found do not report 88.9% specificity. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThis classification aligns algorithm design with validation and implementation requirements and informs regulatory and ethical evaluation.
“This classification aligns algorithm design with validation and implementation requirements and informs regulatory and ethical evaluation [5].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [5]
The Methods (Taxonomy and Disease partitioning algorithm; page unavailable) describe classes designed for machine learning and clinical relevance, and the Abstract describes validation using those classes. The Abstract also discusses possible clinical deployment but says real-world validation is still needed. Our copy does not say that this classification establishes implementation requirements or informs regulatory and ethical evaluation. (LLM, conf=0.84)
Experimental — accuracy not yet measuredDeep neural networks have shown high accuracy at pattern recognition from noisy, complex inputs, including automated detection of diabetic retinopathy from images, skin cancer from mobile phone cameras, and onset of health conditions from electronic medical records.
“Deep neural networks (LeCun, Bengio, and Hinton 2015) have shown high accuracy at pattern recognition from noisy, complex inputs, including automated detection of diabetic retinopathy from images (Gulshan et al. 2016), skin cancer from mobile phone cameras (Esteva et al. 2017), and the onset of health conditions from electronic medical records (Razavian, Marcus, and Sontag 2016; Lipton et al. 2015”
From DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction (2018) · cites this paper as Esteva et al. 2017
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measured
AI systems are increasingly used in health, offering the potential to expand access to health information, support clinicians in delivering high-quality care, and help people make better health decisions.
“Artificial intelligence (AI) systems are increasingly used in health, offering the potential to expand access to health information, support clinicians in delivering high-quality care, and help people make better health decisions (Esteva et al., 2017; Gulshan et al., 2016; Beam and Kohane, 2018; Topol, 2019).”
From HealthBench: Evaluating Large Language Models Towards Improved Human Health (2025) · cites this paper as Esteva et al., 2017
The Abstract says the system could expand access to diagnostic care, augment dermatology specialists’ clinical decision-making, and help patients track lesions and detect cancer earlier. It does not establish that AI systems are increasingly used across healthcare; its patient-facing potential is specific to skin lesions. (LLM, conf=0.84)
Experimental — accuracy not yet measuredFusion approaches in other less complex clinical applications also improved performance over single modality models, even those in which single modality models have been widely reported to achieve high performance, such as pixel-based models for automated skin cancer detection.
“Fusion approaches in other less complex clinical applications also improved performance over single modality models, even those in which single modality models have been widely reported to achieve high performance, such as pixel-based models for automated skin cancer detection43.”
From Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines (2020) · cites this paper as [43]
AI/ML systems have the potential to outperform human experts on prediction tasks.
“Although previous research describes the potential of AI/ML systems to outperform human experts on prediction tasks [18, 32, 40]”
From Understanding the Effect of Counterfactual Explanations on Trust and Reliance on AI for Human-AI Collaborative Clinical Decision Making (2023) · cites this paper as [18]
In 2017, deep CNNs achieved performance comparable with dermatologists in classifying images of skin lesions.
“A prominent demonstration of this was in 2017, when deep CNNs achieved performance comparable with that of dermatologists in classifying images of skin lesions (Esteva et al, 2017).”
From Artificial intelligence–enabled precision medicine for inflammatory skin diseases (2026) · cites this paper as Esteva et al, 2017
Esteva et al. trained their CNN on 129,450 clinical images, including more than 2,000 skin diseases.
“Esteva et al. (21) trained their CNN on 129,450 clinical images, which included more than 2,000 skin diseases.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [21]
This highlights the potential for its use in clinical practice.
“This highlights the potential for its use in clinical practice.”
From Revolutionizing dermatopathology using AI in skin diagnostics: scoping review (2026) · cites this paper as [21]
Previous work has shown that fine-tuning of a pretrained model based on Google's Inception v3 architecture on medical data achieved near human expert performance.
“Previous work in this area has shown that fine-tuning of a pretrained model, based on Google's Inception v3 architecture on medical data, achieved near human expert performance (16, 17).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [16]
CNN has achieved expert-level performances in various fields of medical research, including diabetic retinopathy screening, skin lesion classification, and lymph node metastasis detection.
“Medical research is no exception, as CNN has achieved expert-level performances in various fields. Gulshan et al. [4], Esteva et al. [5], and Ehteshami Bejnordi et al. [6] demonstrated the potential of deep learning for diabetic retinopathy screening, skin lesion classification, and lymph node metastasis detection, respectively.”
From Convolutional neural networks: an overview and application in radiology (2018) · cites this paper as [5]
Several studies have compared clinician performance with ML models in other fields such as dermatology, radiology, and pathology.
“While several studies have compared clinician performance with machine learning (ML) models in other fields-such as dermatology [26,27], radiology [28] and pathology [29]-”
From Prediction of Postoperative ICU Requirements: Closing the Translational Gap with a Real-World Clinical Benchmark for Artificial Intelligence Approaches (2025) · cites this paper as [26]
Previous Head-to-Head comparisons of clinician and AI performance in dermatology, radiology, and pathology have provided valuable insights but remained confined to image-based or narrowly defined diagnostic tasks.
“Previous Head-to-Head comparisons of clinician and AI performance-such as in dermatology [26,27], radiology [28], and pathology [29]-have provided valuable insights, but they remained confined to image-based or narrowly defined diagnostic tasks.”
From Prediction of Postoperative ICU Requirements: Closing the Translational Gap with a Real-World Clinical Benchmark for Artificial Intelligence Approaches (2025) · cites this paper as [26]
Los sistemas de diagnóstico basados en aprendizaje profundo han demostrado precisión comparable a la de los especialistas.
“Los sistemas de diagnóstico basados en aprendizaje profundo han demostrado precisión comparable a la de los especialistas (Esteva et al., 2017), pero la decisión final siempre recae en el juicio clínico humano.”
From Entre Códigos y Corazones: Investigación e Inteligencia Artificial hacia una Tecnología más Humana (2025) · cites this paper as Esteva et al., 2017
Traditional approaches to assessing segmentation quality heavily depend on the availability of ground truth data.
“Traditional approaches to assessing segmen- tation quality, such as boundary-based, region-based, and hy- brid metrics, heavily depend on the availability of ground truth data [7], [8], [9].”
From Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation (2025) · cites this paper as [9]
The Abstract discusses lesion segmentation as preprocessing used by earlier classification methods, but does not discuss how segmentation quality is assessed. A search for boundary-based, region-based, and hybrid metrics and segmentation ground truth found no passage addressing the claimed dependence on ground-truth data. (LLM, conf=0.76)
Experimental — accuracy not yet measuredEsteva et al analysed clinical images to identify skin cancer subtypes.
“Esteva et al analysed clinical images to identify skin cancer subtypes.20”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [20]
Esteva et al performed CNN to identify skin cancer from clinical images.
“Esteva et al performed the CNN to identify skin cancer from clinical images.20”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [20]
The proportions of correctly predicted malignant lesions (sensitivity) and benign lesions (specificity) are both over 90% for Esteva et al's CNN.
“The proportions of correctly predicted malignant lesions (ie, sensitivity) and benign lesions (ie, specificity) are both over 90%, which indicates the superior performance of the CNN.”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [20]
Recently, the CNN has been successfully implemented in the medical area to assist disease diagnosis.
“Recently, the CNN has been successfully implemented in the medical area to assist disease diagnosis.”
From Artificial intelligence in healthcare: past, present and future (2017) · cites this paper as [20]
Using large datasets, computer vision models can acquire many pattern-recognition abilities, from physician-level diagnostics to medical scene perception.
“Using large datasets, CV models can acquire many pattern-recognition abilities-from physician-level diagnostics 7 to medical scene perception 8 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [7]
Given sufficient data, the accuracy of computer vision models often matches or surpasses the level of expert physicians.
“Given sufficient data, the accuracy often matches or surpasses the level of expert physicians 7,16 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [7]
Studies have consistently demonstrated that CNN sensitivity and specificity in classifying skin lesions matches or even exceeds physician levels.
“These studies have sequentially tested increasing numbers of dermatologists (25-7 57-93 , 157-94 ), consistently demonstrating a sensitivity and specificity in classification that matches or even exceeds physician levels.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [7]
A series of works have demonstrated that CNNs can match the performance of board-certified dermatologists at classifying malignant skin lesions from benign ones.
“A series of works have demonstrated that CNNs can match the performance of board-certified dermatologists at classifying malignant skin lesions from benign ones 7,93,94 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [7]
CNNs trained to classify disease states have been extensively tested across diseases and benchmarked against physicians.
“CNNs-trained to classify disease states-have been extensively tested across diseases, and benchmarked against physicians.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [7]
CNN performance is typically on par with experts when both are tested on the same image classification task.
“Their performance is typically on par with experts when both are tested on the same image classification task.”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [7]
In oncology, machine-learning-based tumour detection, subtyping, and outcome prediction have been successful.
“Particularly in oncology, success has been reported in machine-learning-based tumour detection3,37, subtyping38, and outcome prediction39”
From Swarm Learning for decentralized and confidential clinical machine learning (2021) · cites this paper as [37]
Machine learning algorithms have already shown expert diagnostic performance based on imaging data for conditions including diabetic retinopathy, skin cancer, and pneumonia.
“Machine learning algorithms have already shown expert diagnostic performance based on imaging data for conditions including diabetic retinopathy, 1 skin cancer, 2 and pneumonia. 3”
From Clinical applications of machine learning algorithms: beyond the black box (2019) · cites this paper as [2]
Esteva et al. demonstrated the potential of AI-powered deep learning models in detecting skin cancer, matching the performance of dermatologists.
“Similarly, Esteva et al. (78) demonstrated the potential of AI-powered deep learning models in detecting skin cancer, matching the performance of dermatologists.”
From Improving health literacy using the power of digital communications to achieve better health outcomes for patients and practitioners. (2023) · cites this paper as [78]
ML and AI techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data.
“These techniques have been applied to predict cancer prognosis and estimate treatment response based on genomic and clinical data [8–14].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [14]
Convolutional neural networks have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation.
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [18]
A 2017 publication demonstrated neural network performance on par with human experts across large image datasets.
“most notably evidenced by a 2017 publication demonstrating neural network performance on par with human experts across large image datasets2”
From Large language models and multimodal foundation models for precision oncology (2024) · cites this paper as [2]
Medical AI is cost-effective, scalable, and often provides expert-level accuracy or outperforms human providers.
“Algorithmic-based healthcare services (medical 'artificial intelligence' (AI)) are cost-effective and scalable and provide expert-level accuracy in applications ranging from the detection of skin cancer 5 and emergency department triage 6,7 to diagnoses of COVID-19 from chest X-rays 8 .”
From Understanding, explaining, and utilizing medical artificial intelligence (2021) · cites this paper as [5]
Several algorithms incorporating artificial intelligence have been developed to help interpret clinical and dermoscopic images for a variety of skin conditions.
“Several algorithms incorporating artificial intelligence (AI) have been developed to help interpret both clinical11,12,13,14,15 and dermoscopic16,17,18,19,20,21,22,23 images for a variety of skin conditions”
Applications of DL techniques based on transfer learning have reported performance comparable to or better than that of human experts.
“The applications of DL techniques based on transfer learning have reported performance comparable to that of the human experts 5 , , 6 or better 7 ., 8”
From The need for uncertainty quantification in machine-assisted medical decision making (2019) · cites this paper as [6]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 1× · see citing works
“Deep learning algorithms, powered by advances in computation and very large datasets 25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games 26 , strategic board games like Go 27 and object recognition 6 .”
MethodGoing deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138 · see citing works
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357 · cited 1× · see citing works
“Deep learning algorithms, powered by advances in computation and very large datasets 25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games 26 , strategic board games like Go 27 and object recognition 6 .”
MethodPan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · cited 1× · see citing works
“Deep learning algorithms, powered by advances in computation and very large datasets 25 , have recently been shown to exceed human performance in visual tasks such as playing Atari games 26 , strategic board games like Go 27 and object recognition 6 .”
MethodBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273 · see citing works
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408 · see citing works
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196 · see citing works
Incidence Estimate of Nonmelanoma Skin Cancer (Keratinocyte Carcinomas) in the US Population, 2012
Rogers, H. W., Weinstock, M. A., Feldman, S. R., & Coldiron, B. M. (2015) · JAMA Dermatology · doi:10.1001/jamadermatol.2015.1187 · Cited by 1,815 · cited 2× · see citing works
“Together these components strike a balance between (1) generating training classes that are overly fine grained and that do not have sufficient data to be learned properly; (2) generating training classes that are too coarse, too data abundant and bias the algorithm towards them.”
BackgroundDiagnostic accuracy of dermoscopy
Kittler, H., Pehamberger, H., Wolff, K., & Binder, M. (2002) · The Lancet Oncology · doi:10.1016/s1470-2045(02)00679-4 · Cited by 1,349 · cited 1× · see citing works
“The existing body of work uses small datasets of typically less than a thousand images of skin lesions 16,18,19 , which, as a result, do not generalize well to new images.”
Model Predicting Survival in Stage I Melanoma Based on Tumor Progression
Clark, W. H., Elder, D. E., Guerry, D., Braitman, L. E., Trock, B. J., Schultz, D., Synnestvedt, M. B., & Halpern, A. C. (1989) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/81.24.1893 · Cited by 1,247 · see citing works
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2015) · arXiv · doi:10.48550/arxiv.1512.00567 · Cited by 741 · see citing works
Prevalence of a History of Skin Cancer in 2007
Stern, R. S. (2010) · Archives of Dermatology · doi:10.1001/archdermatol.2010.4 · Cited by 421 · see citing works
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images
Codella, N., Cai, J., Abedini, M., Garnavi, R., Halpern, A. C., & Smith, J. R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24888-2_15 · Cited by 400 · see citing works
Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms
Masood, A., & Al-Jumaily, A. A. (2013) · International Journal of Biomedical Imaging · doi:10.1155/2013/323268 · Cited by 347 · see citing works
Ballerini, L., Fisher, R. B., Aldridge, B., & Rees, J. (2012) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-94-007-5389-1_4 · Cited by 289 · see citing works
Melanoma Computer-Aided Diagnosis
Burroni, M., Corona, R., Dell’Eva, G., Sera, F., BONO, R., Puddu, P., Perotti, R., Nobile, F., Andreassi, L., & Rubegni, P. (2004) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-03-0039 · Cited by 151 · see citing works
Binder, M., Kittler, H., Seeber, A., Steiner, A., Pehamberger, H., & Wolff, K. (1998) · Melanoma Research · doi:10.1097/00008390-199806000-00009 · Cited by 127 · cited 1× · see citing works
“The existing body of work uses small datasets of typically less than a thousand images of skin lesions 16,18,19 , which, as a result, do not generalize well to new images.”
A Mobile Automated Skin Lesion Classification System
Ramlakhan, K., & Shang, Y. (2011) · Proceedings - International Conference on Tools with Artificial Intelligence, TAI · doi:10.1109/ictai.2011.29 · Cited by 124 · cited 1× · see citing works
“images (for example, smartphone images) exhibit variability in factors such as zoom, angle and lighting, making classification substantially more challenging 23,24 .”
BackgroundAccuracy of Computer Diagnosis of Melanoma
Rosado, B. (2003) · Archives of Dermatology · doi:10.1001/archderm.139.3.361 · Cited by 120 · see citing works
Schindewolf, T., Stolz, W., Albert, R., Abmayr, W., & Harms, H. (1993) · PubMed · Cited by 101 · see citing works
Machine learning in medical imaging
Suzuki, K., Zhou, L., & Wang, Q. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.020 · Cited by 42 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [5]LeCun Y & Bengio Y In The Handbook of Brain Theory and Neural Networks (ed. Arbib MA) 3361. 10 (MIT Press, 1995).
- [18]Gutman D Skin lesion analysis toward melanoma detection. International Symposium on Biomedical Imaging (ISBI), (International Skin Imaging Collaboration (ISIC), 2016).
- [20]Menzies SW In Skin Cancer and UV Radiation (eds Altmeyer P, Hoffmann K & Stücker M) 1064– 1070 (Springer, 1997).
- [24]Ballerini L In Color Medical Image Analysis. (eds, Celebi ME & Schaefer G) 63– 86 (Springer, 2013).
Cited by
Sorted by citations
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
A survey on Image Data Augmentation for Deep Learning
Shorten, C., & Khoshgoftaar, T. M. (2019) · Journal of Big Data · doi:10.1186/s40537-019-0197-0 · Cited by 13,203 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021) · Journal of Big Data · doi:10.1186/s40537-021-00444-8 · Cited by 7,906 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
Self-citationArtificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · see citing works
Convolutional neural networks: an overview and application in radiology
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0639-9 · Cited by 4,682 · see citing works
“Gulshan et al. [4], Esteva et al. [5], and Ehteshami Bejnordi et al. [6] demonstrated the potential of deep learning for diabetic retinopathy screening, skin lesion classification, and lymph node metastasis detection, respectively.”
BackgroundRajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215 · see citing works
“They have also been used in medical imaging to detect skin cancer [5], brain cancer [28], and breast cancer [29].”
BackgroundArtificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978 · see citing works
Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628 · see citing works
Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · see citing works
International evaluation of an AI system for breast cancer screening
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works
The role of artificial intelligence in achieving the Sustainable Development Goals
Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Fuso Nerini, F. (2020) · Nature Communications · doi:10.1038/s41467-019-14108-y · Cited by 3,294 · see citing works
Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-021-01614-0 · Cited by 3,212 · see citing works
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019) · Nature Medicine · doi:10.1038/s41591-018-0268-3 · Cited by 3,148 · see citing works
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146 · see citing works
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works
An overview of clinical decision support systems: Benefits, risks, and strategies for success
Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · see citing works
Key challenges for delivering clinical impact with artificial intelligence
Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G., & King, D. (2019) · BMC Medicine · doi:10.1186/s12916-019-1426-2 · Cited by 3,136 · see citing works
Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works
Clinical-grade computational pathology using weakly supervised deep learning on whole slide images
Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817 · see citing works
Clinically applicable deep learning for diagnosis and referral in retinal disease
De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works
Automated detection of COVID-19 cases using deep neural networks with X-ray images
Öztürk, T., Talo, M., Yildirim, E. A., Baloğlu, U. B., Yıldırım, Ö., & Acharya, U. R. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103792 · Cited by 2,607 · see citing works
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
Data-efficient and weakly supervised computational pathology on whole-slide images
Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00682-w · Cited by 2,282 · see citing works
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep LearningPreprint
Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D. Y., Bagul, A., Langlotz, C. P., Shpanskaya, K. S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.05225 · Cited by 2,283 · see citing works
The practical implementation of artificial intelligence technologies in medicine
He, J., Baxter, S. L., Xu, J., Xu, J., Zhou, X., & Zhang, K. (2019) · Nature Medicine · doi:10.1038/s41591-018-0307-0 · Cited by 2,246 · see citing works
A guide to machine learning for biologists
Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127 · see citing works
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
Akhtar, N., & Mian, A. (2018) · IEEE Access · doi:10.1109/access.2018.2807385 · Cited by 2,021 · see citing works
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works
Attention gated networks: learning to leverage salient regions in medical images
Schlemper, J., Oktay, O., Schaap, M., Heinrich, M., Kainz, B., Glocker, B., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.012 · Cited by 2,014 · see citing works
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015 · see citing works
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0195-0 · Cited by 1,865 · see citing works
Review of Deep Learning Algorithms and Architectures
Shrestha, A., & Mahmood, A. (2019) · IEEE Access · doi:10.1109/access.2019.2912200 · Cited by 1,883 · see citing works
“, [12] reported that an deep learning network was able to learn from 129,450 images of 2,032 diseases and was able to diagnose at the same level as 21 board certified dermatologists.”
BackgroundArtificial intelligence in cancer imaging: Clinical challenges and applications
Bi, W. L., Hosny, A., Schabath, M. B., Giger, M. L., Birkbak, N. J., Mehrtash, A., Allison, T., Arnaout, O., Abbosh, C., Dunn, I. F., et al. (2019) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21552 · Cited by 1,846 · see citing works
Causability and explainability of artificial intelligence in medicine
Holzinger, A., Langs, G., Denk, H., Zatloukal, K., & Müller, H. (2019) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1312 · Cited by 1,885 · see citing works
Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology
Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-019-0252-y · Cited by 1,698 · see citing works
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,561 · see citing works
Artificial Intelligence and Deep Learning in Ophthalmology
Ting, D. S. W., Pasquale, L. R., Peng, L., Campbell, J. P., Lee, A., Raman, R., Tan, G. S. W., Schmetterer, L. F., Keane, P. A., & Wong, T. Y. (2018) · Springer eBooks · doi:10.1007/978-3-030-64573-1_200 · Cited by 1,616 · see citing works
Privacy in the age of medical big data
Price, W. N., & Cohen, I. G. (2019) · Nature Medicine · doi:10.1038/s41591-018-0272-7 · Cited by 1,522 · see citing works
11 TOPS photonic convolutional accelerator for optical neural networks
Xu, X., Tan, M., Corcoran, B., Wu, J., Boes, A., Nguyen, T. G., Chu, S. T., Little, B. E., Hicks, D. G., Morandotti, R., et al. (2021) · Nature · doi:10.1038/s41586-020-03063-0 · Cited by 1,531 · see citing works
Deep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,521 · see citing works
Big data and machine learning algorithms for health-care delivery
Ngiam, K. Y., & Khor, I. W. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30149-4 · Cited by 1,424 · see citing works
Artificial intelligence in surgery: promises and perils
Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468 · see citing works
Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., et al. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002686 · Cited by 1,449 · see citing works
Deep learning-enabled medical computer vision
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021) · npj Digital Medicine · doi:10.1038/s41746-020-00376-2 · Cited by 1,406 · see citing works
Self-citationSetio, A. A. A., Traverso, A., de Bel, T., Berens, M. S. N., van den Bogaard, C., Cerello, P., Chen, H., Dou, Q., Fantacci, M. E., Geurts, B. H. J., et al. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.06.015 · Cited by 1,408 · see citing works
Liu, X., Cruz Rivera, S., Moher, D., Calvert, M. J., Denniston, A. K., The SPIRIT-AI and CONSORT-AI Working Group, SPIRIT-AI and CONSORT-AI Steering Group, Chan, A.-W., Darzi, A., Holmes, C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1034-x · Cited by 1,421 · see citing works
Self-citationDeep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225 · see citing works
Machine learning in medicine: a practical introduction
Sidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0681-4 · Cited by 1,308 · see citing works
A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT
Zheng, C., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., Liu, W., & Wang, X. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2995965 · Cited by 1,237 · see citing works
A Deep Learning System to Screen Novel Coronavirus Disease 2019 Pneumonia
Xu, X., Jiang, X., Ma, C., Du, P., Li, X., Lv, S., Yu, L., Ni, Q., Chen, Y., Su, J., et al. (2020) · Engineering · doi:10.1016/j.eng.2020.04.010 · Cited by 1,209 · see citing works
Deep learning: new computational modelling techniques for genomics
Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,229 · see citing works
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202 · see citing works
Video-based AI for beat-to-beat assessment of cardiac function
Ouyang, D., He, B., Ghorbani, A., Yuan, N., Ebinger, J., Langlotz, C. P., Heidenreich, P. A., Harrington, R. A., Liang, D. H., Ashley, E. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2145-8 · Cited by 1,153 · see citing works
Predicting cancer outcomes from histology and genomics using convolutional networks
Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084 · see citing works
Overview of artificial intelligence in medicine
Amisha, Malik, P., Pathania, M., & Rathaur, V. K. (2019) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_440_19 · Cited by 1,106 · see citing works
Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine · doi:10.1371/journal.pmed.1002730 · Cited by 1,082 · see citing works
Recent advances and clinical applications of deep learning in medical image analysis
Chen, X., Wang, X., Zhang, K., Fung, K.-M. A., Thai, T., Moore, K. N., Mannel, R. S., Liu, H., Zheng, B., & Qiu, Y. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102444 · Cited by 1,129 · see citing works
Yang, C., Zhao, H., Bruzzone, L., Benediktsson, J. A., Liang, Y., Liu, B., Zeng, X., Guan, R., Li, C., & Ouyang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-20215-y · Cited by 1,065 · see citing works
Ethical and regulatory challenges of AI technologies in healthcare: A narrative review
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26297 · Cited by 1,083 · see citing works
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035 · see citing works
History of artificial intelligence in medicine
Kaul, V., Enslin, S., & Gross, S. A. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.06.040 · Cited by 1,094 · see citing works
Lee, D., & Yoon, S. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010271 · Cited by 1,017 · see citing works
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019) · Marketing Science · doi:10.1287/mksc.2019.1192 · Cited by 1,027 · see citing works
Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study
Chilamkurthy, S., Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K., Mahajan, V., Rao, P., & Warier, P. (2018) · The Lancet · doi:10.1016/s0140-6736(18)31645-3 · Cited by 1,020 · see citing works
Human-computer collaboration for skin cancer recognition
Tschandl, P., Rinner, C., Apalla, Z., Argenziano, G., Codella, N., Halpern, A., Janda, M., Lallas, A., Longo, C., Malvehy, J., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0942-0 · Cited by 1,024 · see citing works
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., et al. (2018) · Circulation · doi:10.1161/circulationaha.118.034338 · Cited by 1,006 · see citing works
A foundation model for generalizable disease detection from retinal images
Zhou, Y., Chia, M. A., Wagner, S. K., Ayhan, M. S., Williamson, D. J., Struyven, R. R., Liu, T., Xu, M., Lozano, M. G., Woodward-Court, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06555-x · Cited by 1,014 · see citing works
Lee, J., Kim, D., Jeong, S., & Choi, S. (2018) · Journal of Dentistry · doi:10.1016/j.jdent.2018.07.015 · Cited by 1,005 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012 · see citing works
Artificial intelligence, bias and clinical safety
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956 · see citing works
What can machine learning do? Workforce implications
Brynjolfsson, E., & Mitchell, T. (2017) · Science · doi:10.1126/science.aap8062 · Cited by 1,131 · see citing works
<scp>eD</scp> octor: machine learning and the future of medicine
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956 · see citing works
Working and organizing in the age of the learning algorithm
Faraj, S., Pachidi, S., & Sayegh, K. (2018) · Information and Organization · doi:10.1016/j.infoandorg.2018.02.005 · Cited by 929 · see citing works
Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00341-z · Cited by 934 · see citing works
Park, S. H., & Han, K. (2018) · Radiology · doi:10.1148/radiol.2017171920 · Cited by 894 · see citing works
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886 · see citing works
Swarm Learning for decentralized and confidential clinical machine learning
Warnat-Herresthal, S., Schultze, H., Shastry, K. L., Manamohan, S., Mukherjee, S., Garg, V., Sarveswara, R., Händler, K., Pickkers, P., Aziz, N. A., et al. (2021) · Nature · doi:10.1038/s41586-021-03583-3 · Cited by 884 · see citing works
Designing Theory-Driven User-Centric Explainable AI
Wang, D., Yang, Q., Abdul, A., & Lim, B. Y. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300831 · Cited by 906 · see citing works
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
Asan, O., Bayrak, A. E., & Choudhury, A. (2020) · Journal of Medical Internet Research · doi:10.2196/15154 · Cited by 825 · see citing works
Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics
Brynjolfsson, E., Rock, D., & Syverson, C. (2017) · National Bureau of Economic Research · doi:10.3386/w24001 · Cited by 805 · see citing works
Layer-Wise Relevance Propagation: An Overview
Montavon, G., Binder, A., Lapuschkin, S., Samek, W., & Müller, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-28954-6_10 · Cited by 825 · see citing works
Current Applications and Future Impact of Machine Learning in Radiology
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800 · see citing works
Littlejohns, T. J., Holliday, J., Gibson, L. M., Garratt, S., Oesingmann, N., Alfaro-Almagro, F., Bell, J. D., Boultwood, C., Collins, R., Conroy, M. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15948-9 · Cited by 812 · see citing works
Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · see citing works
Hollon, T. C., Pandian, B., Adapa, A. R., Urias, E., Save, A. V., Khalsa, S. S. S., Eichberg, D. G., D’Amico, R. S., Farooq, Z. U., Lewis, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0715-9 · Cited by 790 · see citing works
Machine Learning for Precision Psychiatry: Opportunities and Challenges
Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 833 · see citing works
Artificial Intelligence in Cancer Research and Precision Medicine
Bhinder, B., Gilvary, C., Madhukar, N. S., & Elemento, O. (2021) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-0090 · Cited by 781 · see citing works
A deep learning system for differential diagnosis of skin diseases
Liu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0842-3 · Cited by 853 · see citing works
Introduction to artificial intelligence in medicine
Mintz, Y., & Brodie, R. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1575882 · Cited by 818 · see citing works
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754 · see citing works
Accelerating the discovery of materials for clean energy in the era of smart automation
Tabor, D. P., Roch, L. M., Saikin, S. K., Kreisbeck, C., Sheberla, D., Montoya, J. H., Dwaraknath, S., Aykol, M., Ortiz, C., Tribukait, H., et al. (2018) · Nature Reviews Materials · doi:10.1038/s41578-018-0005-z · Cited by 781 · see citing works
Wang, Y., & Kosinski, M. (2018) · Journal of Personality and Social Psychology · doi:10.1037/pspa0000098 · Cited by 766 · see citing works
- Authors
- Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- VA Palo Alto Health Care System (US)
- Institute for Stem Cell Biology and Regenerative Medicine (US)
- Corresponding author
- Esteva, Andre
- Kuprel, Brett
- Novoa, Roberto A.
- Thrun, Sebastian
- Venue
- Nature
- Published
- 2017-01-25
- Volume
- 542
- Issue
- 7639
- Pages
- 115-118
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC8382232
- OpenAlex
- W2581082771
- Semantic Scholar
- e1ec11a1cb3d9745fb18d3bf74247f95a6663d08
- Cited by
- 14,362
- References
- 26
- Citations by year
- 2026: 1681 · 2025: 2079 · 2024: 1706 · 2023: 1544 · 2022: 1495 · 2021: 1739 · 2020: 1647 · 2019: 1324 · 2018: 862 · 2017: 266 · 2016: 1 · 2015: 2 · 2014: 3 · 2012: 1
- FWCI
- 490.95
- Type
- journal article
- Language
- en
- Update notices
- erratum (2017-06-29)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health (UL1TR001085)
- National Institute on Aging (R01AG009521, R21AG044815, R01AG020961)
- National Institute of Neurological Disorders and Stroke (R01NS089533)
- National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01AR063963)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin cancer classification, skin lesion images, deep convolutional neural networks, melanoma, non-melanoma skin cancer, seborrheic keratosis, benign nevi, mobile phone imaging, clinical screening, fine-grained image classification
- MeSH terms
- Dermatologists, Automation, Humans, Melanoma, Nevus, Photography, Skin Neoplasms, Reproducibility of Results, Keratinocytes, Neural Networks, Computer, Keratosis, Seborrheic, Cell Phone, Datasets as Topic
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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