Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18091, first published .
Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study

Journals

  1. Maron R, Haggenmüller S, von Kalle C, Utikal J, Meier F, Gellrich F, Hauschild A, French L, Schlaak M, Ghoreschi K, Kutzner H, Heppt M, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Hekler A, Krieghoff-Henning E, Kather J, Fröhling S, Lipka D, Brinker T. Robustness of convolutional neural networks in recognition of pigmented skin lesions. European Journal of Cancer 2021;145:81 View
  2. Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194 View
  3. Cha K, Woo H, Park D, Chang D, Kang M. Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach. JMIR Medical Informatics 2021;9(7):e26000 View
  4. Zhao S, Wang X, Jiang Z, Li Y, Wu Z, Wu X, Chen M, Zhang Y, Zuo K, Li Y, Yin H, Liu S, Yu N, Su J, Yin M, Chen X. The Classification of Six Common Skin Diseases Based on Xiangya-Derm, A Chinese Database for Artificial Intelligence (Preprint). Journal of Medical Internet Research 2021 View
  5. Maron R, Schlager J, Haggenmüller S, von Kalle C, Utikal J, Meier F, Gellrich F, Hobelsberger S, Hauschild A, French L, Heinzerling L, Schlaak M, Ghoreschi K, Hilke F, Poch G, Heppt M, Berking C, Haferkamp S, Sondermann W, Schadendorf D, Schilling B, Goebeler M, Krieghoff-Henning E, Hekler A, Fröhling S, Lipka D, Kather J, Brinker T. A benchmark for neural network robustness in skin cancer classification. European Journal of Cancer 2021;155:191 View
  6. Jones O, Matin R, van der Schaar M, Prathivadi Bhayankaram K, Ranmuthu C, Islam M, Behiyat D, Boscott R, Calanzani N, Emery J, Williams H, Walter F. Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review. The Lancet Digital Health 2022;4(6):e466 View
  7. Martorell A, Martin-Gorgojo A, Ríos-Viñuela E, Rueda-Carnero J, Alfageme F, Taberner R. Inteligencia artificial en dermatología: ¿amenaza u oportunidad?. Actas Dermo-Sifiliográficas 2022;113(1):30 View
  8. Chen X, Cheng G, Wang F, Tao X, Xie H, Xu L. Machine and cognitive intelligence for human health: systematic review. Brain Informatics 2022;9(1) View
  9. Schielein M, Christl J, Sitaru S, Pilz A, Kaczmarczyk R, Biedermann T, Lasser T, Zink A. Outlier detection in dermatology: Performance of different convolutional neural networks for binary classification of inflammatory skin diseases. Journal of the European Academy of Dermatology and Venereology 2023;37(5):1071 View
  10. Maron R, Hekler A, Haggenmüller S, von Kalle C, Utikal J, Müller V, Gaiser M, Meier F, Hobelsberger S, Gellrich F, Sergon M, Hauschild A, French L, Heinzerling L, Schlager J, Ghoreschi K, Schlaak M, Hilke F, Poch G, Korsing S, Berking C, Heppt M, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather J, Fröhling S, Lipka D, Krieghoff-Henning E, Brinker T. Model soups improve performance of dermoscopic skin cancer classifiers. European Journal of Cancer 2022;173:307 View
  11. Li S, Chu Y, Wang Y, Wang Y, Hu S, Wu X, Qi X, Zhang F. Distinguish the Value of the Benign Nevus and Melanomas Using Machine Learning: A Meta-Analysis and Systematic Review. Mediators of Inflammation 2022;2022:1 View
  12. Malciu A, Lupu M, Voiculescu V. Artificial Intelligence-Based Approaches to Reflectance Confocal Microscopy Image Analysis in Dermatology. Journal of Clinical Medicine 2022;11(2):429 View
  13. Rezk E, Eltorki M, El-Dakhakhni W. Leveraging Artificial Intelligence to Improve the Diversity of Dermatological Skin Color Pathology: Protocol for an Algorithm Development and Validation Study. JMIR Research Protocols 2022;11(3):e34896 View
  14. Xue P, Si M, Qin D, Wei B, Seery S, Ye Z, Chen M, Wang S, Song C, Zhang B, Ding M, Zhang W, Bai A, Yan H, Dang L, Zhao Y, Rezhake R, Zhang S, Qiao Y, Qu Y, Jiang Y. Unassisted Clinicians Versus Deep Learning–Assisted Clinicians in Image-Based Cancer Diagnostics: Systematic Review With Meta-analysis. Journal of Medical Internet Research 2023;25:e43832 View
  15. Hui S, Dong L, Zhang K, Nie Z, Jiang X, Li H, Hou Z, Ding J, Wang Y, Li D. Noninvasive identification of Benign and malignant eyelid tumors using clinical images via deep learning system. Journal of Big Data 2022;9(1) View
  16. Orzan O, Dorobanțu A, Gurău C, Ali S, Mihai M, Popa L, Giurcăneanu C, Tudose I, Bălăceanu B. Challenging Patterns of Atypical Dermatofibromas and Promising Diagnostic Tools for Differential Diagnosis of Malignant Lesions. Diagnostics 2023;13(4):671 View
  17. Ba W, Wu H, Chen W, Wang S, Zhang Z, Wei X, Wang W, Yang L, Zhou D, Zhuang Y, Zhong Q, Song Z, Li C. Convolutional neural network assistance significantly improves dermatologists’ diagnosis of cutaneous tumours using clinical images. European Journal of Cancer 2022;169:156 View
  18. Skuhala T, Trkulja V, Rimac M, Dragobratović A, Desnica B. Analysis of Types of Skin Lesions and Diseases in Everyday Infectious Disease Practice—How Experienced Are We?. Life 2022;12(7):978 View
  19. Daneshjou R, Smith M, Sun M, Rotemberg V, Zou J. Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms. JAMA Dermatology 2021;157(11):1362 View
  20. Hauser K, Kurz A, Haggenmüller S, Maron R, von Kalle C, Utikal J, Meier F, Hobelsberger S, Gellrich F, Sergon M, Hauschild A, French L, Heinzerling L, Schlager J, Ghoreschi K, Schlaak M, Hilke F, Poch G, Kutzner H, Berking C, Heppt M, Erdmann M, Haferkamp S, Schadendorf D, Sondermann W, Goebeler M, Schilling B, Kather J, Fröhling S, Lipka D, Hekler A, Krieghoff-Henning E, Brinker T. Explainable artificial intelligence in skin cancer recognition: A systematic review. European Journal of Cancer 2022;167:54 View
  21. Melarkode N, Srinivasan K, Qaisar S, Plawiak P. AI-Powered Diagnosis of Skin Cancer: A Contemporary Review, Open Challenges and Future Research Directions. Cancers 2023;15(4):1183 View
  22. Frisinger A, Papachristou P. The voice of healthcare: introducing digital decision support systems into clinical practice - a qualitative study. BMC Primary Care 2023;24(1) View
  23. Grossarth S, Mosley D, Madden C, Ike J, Smith I, Huo Y, Wheless L. Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods. Current Oncology Reports 2023;25(6):635 View
  24. Jiminez V, Chung M, Saleem M, Yusuf N. Use of Artificial Intelligence in Skin Aging. OBM Geriatrics 2023;07(02):1 View
  25. Winkler J, Blum A, Kommoss K, Enk A, Toberer F, Rosenberger A, Haenssle H. Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study. JAMA Dermatology 2023;159(6):621 View
  26. Adebayo O, Bhuiyan Z, Ahmed Z. Exploring the Effectiveness of Artificial Intelligence, Machine Learning and Deep Learning in Trauma Triage: A Systematic Review and Meta-Analysis. SSRN Electronic Journal 2022 View
  27. Patel R, Foltz E, Witkowski A, Ludzik J. Analysis of Artificial Intelligence-Based Approaches Applied to Non-Invasive Imaging for Early Detection of Melanoma: A Systematic Review. Cancers 2023;15(19):4694 View
  28. Adebayo O, Bhuiyan Z, Ahmed Z. Exploring the effectiveness of artificial intelligence, machine learning and deep learning in trauma triage: A systematic review and meta-analysis. DIGITAL HEALTH 2023;9 View
  29. Miller I, Stapelberg M, Rosic N, Hudson J, Coxon P, Furness J, Walsh J, Climstein M. Implementation of artificial intelligence for the detection of cutaneous melanoma within a primary care setting: prevalence and types of skin cancer in outdoor enthusiasts. PeerJ 2023;11:e15737 View
  30. Schuh S, Schiele S, Thamm J, Kranz S, Welzel J, Blum A. Implementation of a dermatoscopy curriculum during residency at Augsburg University Hospital in Germany. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2023;21(8):872 View
  31. Schuh S, Schiele S, Thamm J, Kranz S, Welzel J, Blum A. Implementierung eines Dermatoskopie‐Curriculums in der Facharztausbildung am Universitätsklinikum Augsburg. JDDG: Journal der Deutschen Dermatologischen Gesellschaft 2023;21(8):872 View
  32. Higgins H, Nakhla A, Lotfalla A, Khalil D, Doshi P, Thakkar V, Shirini D, Bebawy M, Ammari S, Lopci E, Schwartz L, Postow M, Dercle L. Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma. Diagnostics 2023;13(22):3483 View
  33. Yee J, Rosendahl C, Aoude L. The role of artificial intelligence and convolutional neural networks in the management of melanoma: a clinical, pathological, and radiological perspective. Melanoma Research 2024;34(2):96 View
  34. Chanda T, Hauser K, Hobelsberger S, Bucher T, Garcia C, Wies C, Kittler H, Tschandl P, Navarrete-Dechent C, Podlipnik S, Chousakos E, Crnaric I, Majstorovic J, Alhajwan L, Foreman T, Peternel S, Sarap S, Özdemir İ, Barnhill R, Llamas-Velasco M, Poch G, Korsing S, Sondermann W, Gellrich F, Heppt M, Erdmann M, Haferkamp S, Drexler K, Goebeler M, Schilling B, Utikal J, Ghoreschi K, Fröhling S, Krieghoff-Henning E, Salava A, Thiem A, Dimitrios A, Ammar A, Vučemilović A, Yoshimura A, Ilieva A, Gesierich A, Reimer-Taschenbrecker A, Kolios A, Kalva A, Ferhatosmanoğlu A, Beyens A, Pföhler C, Erdil D, Jovanovic D, Racz E, Bechara F, Vaccaro F, Dimitriou F, Rasulova G, Cenk H, Yanatma I, Kolm I, Hoorens I, Sheshova I, Jocic I, Knuever J, Fleißner J, Thamm J, Dahlberg J, Lluch-Galcerá J, Figueroa J, Holzgruber J, Welzel J, Damevska K, Mayer K, Maul L, Garzona-Navas L, Bley L, Schmitt L, Reipen L, Shafik L, Petrovska L, Golle L, Jopen L, Gogilidze M, Burg M, Morales-Sánchez M, Sławińska M, Mengoni M, Dragolov M, Iglesias-Pena N, Booken N, Enechukwu N, Persa O, Oninla O, Theofilogiannakou P, Kage P, Neto R, Peralta R, Afiouni R, Schuh S, Schnabl-Scheu S, Vural S, Hudson S, Saa S, Hartmann S, Damevska S, Finck S, Braun S, Hartmann T, Welponer T, Sotirovski T, Bondare-Ansberga V, Ahlgrimm-Siess V, Frings V, Simeonovski V, Zafirovik Z, Maul J, Lehr S, Wobser M, Debus D, Riad H, Pereira M, Lengyel Z, Balcere A, Tsakiri A, Braun R, Brinker T. Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nature Communications 2024;15(1) View
  35. De A, Mishra N, Chang H. An approach to the dermatological classification of histopathological skin images using a hybridized CNN-DenseNet model. PeerJ Computer Science 2024;10:e1884 View
  36. Papachristou P, Söderholm M, Pallon J, Taloyan M, Polesie S, Paoli J, Anderson C, Falk M. Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial. British Journal of Dermatology 2024;191(1):125 View
  37. Wu A, Ngo M, Thomas C. Assessment of patient perceptions of artificial intelligence use in dermatology: A cross‐sectional survey. Skin Research and Technology 2024;30(3) View
  38. Krakowski I, Kim J, Cai Z, Daneshjou R, Lapins J, Eriksson H, Lykou A, Linos E. Human-AI interaction in skin cancer diagnosis: a systematic review and meta-analysis. npj Digital Medicine 2024;7(1) View
  39. Salinas M, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, Rotemberg V, Briones J, Mery D, Navarrete-Dechent C. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. npj Digital Medicine 2024;7(1) View
  40. Shinde R, Hossain M, Rizvi S, Imtiaz S, Kwon K, Kim N. DermSegNet: smart IoT model for multi-class dermatological lesion diagnosis using adaptive segmentation and improved EfficientNetB3. Applied Intelligence 2024;54(9-10):6930 View
  41. Ye Z, Zhang D, Zhao Y, Chen M, Wang H, Seery S, Qu Y, Xue P, Jiang Y. Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis. Artificial Intelligence in Medicine 2024;155:102934 View
  42. Shapiro J, Lyakhovitsky A. Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans. Clinics in Dermatology 2024;42(5):492 View
  43. Allen K, Yawson A, Haggenmüller S, Kather J, Brinker T. Human-centered AI as a framework guiding the development of image-based diagnostic tools in oncology: a systematic review. ESMO Real World Data and Digital Oncology 2024;6:100077 View
  44. Wang Z, Chang L, Shi T, Hu H, Wang C, Lin K, Zhang J. Identifying diagnostic biomarkers for Erythemato-Squamous diseases using explainable machine learning. Biomedical Signal Processing and Control 2025;100:107101 View
  45. Chen J, Fernandez K, Fadadu R, Reddy R, Kim M, Tan J, Wei M. Skin Cancer Diagnosis by Lesion, Physician, and Examination Type. JAMA Dermatology 2024 View

Books/Policy Documents

  1. Bi Y, Xue B, Zhang M. Genetic Programming for Image Classification. View
  2. Antonova E, Yu G, Yarushkina N, Sapunkov A, Khambikova A. Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). View
  3. Antonova E, Guskov G, Yarushkina N, Sapunkov A, Khambikova A. Artificial Intelligence in Models, Methods and Applications. View
  4. Duran-Lopez L, Hernández-Rodríguez J, Dominguez-Morales J, Pereyra-Rodríguez J. Recent Advances and Emerging Challenges in STEM. View