Lallas, Aimilios

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

Haenssle, H. A., Fink, C., Schneiderbauer, R., Toberer, F., Buhl, T., Blum, A., Kalloo, A., Hassen, A. B. H., Thomas, L., Enk, A., et al. (2018) · Annals of Oncology

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 1,571

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

Cutaneous Melanoma Detection and ManagementArtificial Intelligence in Healthcare and Education

Cited by 1,024

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

Tschandl, P., Codella, N., Akay, B. N., Argenziano, G., Braun, R. P., Cabo, H., Gutman, D., Halpern, A., Helba, B., Hofmann-Wellenhof, R., et al. (2019) · The Lancet Oncology

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 555

Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition

Winkler, J. K., Fink, C., Toberer, F., Enk, A., Deinlein, T., Hofmann-Wellenhof, R., Thomas, L., Lallas, A., Blum, A., Stolz, W., et al. (2019) · JAMA Dermatology

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 365

Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts

Haggenmüller, S., Maron, R. C., Hekler, A., Utikal, J. S., Barata, C., Barnhill, R. L., Beltraminelli, H., Berking, C., Betz-Stablein, B., Blum, A., et al. (2021) · European Journal of Cancer

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 267

Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin

Sinz, C., Tschandl, P., Rosendahl, C., Akay, B. N., Argenziano, G., Blum, A., Braun, R. P., Cabo, H., Gourhant, J.-Y., Kreusch, J., et al. (2017) · Journal of the American Academy of Dermatology

Cutaneous Melanoma Detection and ManagementNonmelanoma Skin Cancer Studies

Cited by 127

Impact of the COVID-19 Pandemic on Dermatology Practice Worldwide: Results of a Survey Promoted by the International Dermoscopy Society (IDS)

Conforti, C., Lallas, A., Argenziano, G., Dianzani, C., Di Meo, N., Giuffrida, R., Kittler, H., Malvehy, J., Marghoob, A. A., Soyer, H. P., et al. (2021) · Dermatology Practical & Conceptual

Dermatological and COVID-19 studiesCOVID-19 and healthcare impacts

Cited by 33

Diagnostic performance of multimodal large language models in distinguishing melanoma from nevi in clinical and dermoscopic images

Boostani, M., Lallas, A., Goldust, M., Nádudvari, N., Lőrincz, K., Bánvölgyi, A., Holló, P., Wikonkál, N. M., Paragh, G., & Kiss, N. (2025) · JAAD International

Cutaneous Melanoma Detection and ManagementAI in cancer detection

Cited by 21

Teledermoscopy of common pink, flat and scaly lesions as an adjuvant diagnostic method in everyday clinical practice: so far, so close

Papadimitriou, I., Bakirtzi, K., Sotiriou, E., Lallas, A., Vakirlis, E., Lazaridou, E., & Ioannides, D. (2021) · Journal of the European Academy of Dermatology and Venereology

Cutaneous Melanoma Detection and ManagementNonmelanoma Skin Cancer Studies

Cited by 1