标题/作者	年份
High-performance medicine: the convergence of human and artificial intelligence EJ Topol Nature medicine 25 (1), 44-56	2019
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions P Tschandl, C Rosendahl, H Kittler Scientific data 5 (1), 1-9	2018
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis X Liu, L Faes, AU Kale, SK Wagner, DJ Fu, A Bruynseels, T Mahendiran, ... The lancet digital health 1 (6), e271-e297	2019
Key challenges for delivering clinical impact with artificial intelligence CJ Kelly, A Karthikesalingam, M Suleyman, G Corrado, D King BMC medicine 17, 1-9	2019
Human–computer collaboration for skin cancer recognition P Tschandl, C Rinner, Z Apalla, G Argenziano, N Codella, A Halpern, ... Nature Medicine 26 (8), 1229-1234	2020
Skin cancer classification using convolutional neural networks: systematic review TJ Brinker, A Hekler, JS Utikal, N Grabe, D Schadendorf, J Klode, ... Journal of medical Internet research 20 (10), e11936	2018
Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study P Tschandl, N Codella, BN Akay, G Argenziano, RP Braun, H Cabo, ... The lancet oncology 20 (7), 938-947	2019
Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition JK Winkler, C Fink, F Toberer, A Enk, T Deinlein, R Hofmann-Wellenhof, ... JAMA dermatology 155 (10), 1135-1141	2019
Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks P Tschandl, C Rosendahl, BN Akay, G Argenziano, A Blum, RP Braun, ... JAMA dermatology 155 (1), 58-65	2019
Multimodal skin lesion classification using deep learning J Yap, W Yolland, P Tschandl Experimental dermatology 27 (11), 1261-1267	2018
Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities M Goyal, T Knackstedt, S Yan, S Hassanpour Computers in biology and medicine 127, 104065	2020
Artificial intelligence in medicine: where are we now? S Kulkarni, N Seneviratne, MS Baig, AHA Khan Academic radiology 27 (1), 62-70	2020
Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks RC Maron, M Weichenthal, JS Utikal, A Hekler, C Berking, A Hauschild, ... European Journal of Cancer 119, 57-65	2019
Notice of retraction: AI techniques for COVID-19 AA Hussain, O Bouachir, F Al-Turjman, M Aloqaily IEEE access 8, 128776-128795	2020
Precision medicine, AI, and the future of personalized health care KB Johnson, WQ Wei, D Weeraratne, ME Frisse, K Misulis, K Rhee, ... Clinical and translational science 14 (1), 86-93	2021
Designing deep learning studies in cancer diagnostics A Kleppe, OJ Skrede, S De Raedt, K Liestøl, DJ Kerr, HE Danielsen Nature Reviews Cancer 21 (3), 199-211	2021
Skin cancer detection: Applying a deep learning based model driven architecture in the cloud for classifying dermal cell images MA Kadampur, S Al Riyaee Informatics in Medicine Unlocked 18, 100282	2020
Machine learning in dermatology: current applications, opportunities, and limitations S Chan, V Reddy, B Myers, Q Thibodeaux, N Brownstone, W Liao Dermatology and therapy 10, 365-386	2020
Application of artificial intelligence in healthcare: chances and challenges R Manne, SC Kantheti Current Journal of Applied Science and Technology 40 (6), 78-89	2021
Augmented intelligence dermatology: deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders SS Han, I Park, SE Chang, W Lim, MS Kim, GH Park, JB Chae, CH Huh, ... Journal of Investigative Dermatology 140 (9), 1753-1761	2020

1 - 20

计算机程序会自动估算并确定日期和引用次数。