This paper is in the following e-collection/theme issue:

Skin Cancer and Melanoma Prevention (113) mHealth for Wellness, Behavior Change and Prevention (2922)

Published on in Vol 2, No 1 (2019): 2019

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13376, first published .
Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise

Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise

Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise

Authors of this article:

Tiago M de Carvalho1 Author Orcid Image ;   Eline Noels1 Author Orcid Image ;   Marlies Wakkee1 Author Orcid Image ;   Andreea Udrea2 Author Orcid Image ;   Tamar Nijsten1 Author Orcid Image
Article Authors Cited by (39) Tweetations (14) Metrics

Journals

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  4. Andrade C, Teixeira L, Vasconcelos M, Rosado L. Data Augmentation Using Adversarial Image-to-Image Translation for the Segmentation of Mobile-Acquired Dermatological Images. Journal of Imaging 2020;7(1):2 View
  5. Sangers T, Nijsten T, Wakkee M. Mobile health skin cancer risk assessment campaign using artificial intelligence on a population‐wide scale: a retrospective cohort analysis. Journal of the European Academy of Dermatology and Venereology 2021;35(11) View
  6. Sangers T, Wakkee M, Kramer‐Noels E, Nijsten T, Lugtenberg M. Views on mobile health apps for skin cancer screening in the general population: an in‐depth qualitative exploration of perceived barriers and facilitators*. British Journal of Dermatology 2021;185(5):961 View
  7. Al-Azzawi W, Chenchamma G, Hamad A, Alshudukhi J, Alhamazani K, Meraf Z, Velmurugan P. Use of Radiation Circuits for Diagnosis of Melanoma Skin Cancer in Images of Skin Lesions Using Convolutional Neural Networks. Journal of Nanomaterials 2022;2022(1) View
  8. Kim T, Song H. Communicating the Limitations of AI: The Effect of Message Framing and Ownership on Trust in Artificial Intelligence. International Journal of Human–Computer Interaction 2023;39(4):790 View
  9. Howell P, Abdelhamid M. Protection Motivation Perspective Regarding the Use of COVID-19 Mobile Tracing Apps Among Public Users: Empirical Study. JMIR Formative Research 2023;7:e36608 View
  10. Nag S, Baidya A, Mandal A, Mathew A, Das B, Devi B, Kumar R. Deep learning tools for advancing drug discovery and development. 3 Biotech 2022;12(5) View
  11. Askr H, Elgeldawi E, Aboul Ella H, Elshaier Y, Gomaa M, Hassanien A. Deep learning in drug discovery: an integrative review and future challenges. Artificial Intelligence Review 2023;56(7):5975 View
  12. Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial Intelligence for Skin Cancer Detection: Scoping Review. Journal of Medical Internet Research 2021;23(11):e22934 View
  13. Strzelecki M, Strąkowska M, Kozłowski M, Urbańczyk T, Wielowieyska-Szybińska D, Kociołek M. Skin Lesion Detection Algorithms in Whole Body Images. Sensors 2021;21(19):6639 View
  14. Jahn A, Navarini A, Cerminara S, Kostner L, Huber S, Kunz M, Maul J, Dummer R, Sommer S, Neuner A, Levesque M, Cheng P, Maul L. Over-Detection of Melanoma-Suspect Lesions by a CE-Certified Smartphone App: Performance in Comparison to Dermatologists, 2D and 3D Convolutional Neural Networks in a Prospective Data Set of 1204 Pigmented Skin Lesions Involving Patients’ Perception. Cancers 2022;14(15):3829 View
  15. Zafar M, Sharif M, Sharif M, Kadry S, Bukhari S, Rauf H. Skin Lesion Analysis and Cancer Detection Based on Machine/Deep Learning Techniques: A Comprehensive Survey. Life 2023;13(1):146 View
  16. Omeroglu A, Mohammed H, Oral E, Aydin S. A novel soft attention-based multi-modal deep learning framework for multi-label skin lesion classification. Engineering Applications of Artificial Intelligence 2023;120:105897 View
  17. Wen H, Yu W, Wu Y, Zhao J, Liu X, Kuang Z, Fan R. Acne detection and severity evaluation with interpretable convolutional neural network models. Technology and Health Care 2022;30:143 View
  18. Ahmedt-Aristizabal D, Nguyen C, Tychsen-Smith L, Stacey A, Li S, Pathikulangara J, Petersson L, Wang D. Monitoring of Pigmented Skin Lesions Using 3D Whole Body Imaging. Computer Methods and Programs in Biomedicine 2023;232:107451 View
  19. Paulo M, Symanzik C, Maia M, Lapão L, Carvalho F, Conneman S, Dias J, Gobba F, John S, Loney T, Pinho C, Rodrigues A, Strehl C, Tenkate T, Wittlich M, Modenese A. Digitally measuring solar ultraviolet radiation in outdoor workers: A study protocol for establishing the use of electronic personal dosimeters in Portugal. Frontiers in Public Health 2023;11 View
  20. Smak Gregoor A, Sangers T, Bakker L, Hollestein L, Uyl – de Groot C, Nijsten T, Wakkee M. An artificial intelligence based app for skin cancer detection evaluated in a population based setting. npj Digital Medicine 2023;6(1) View
  21. Sangers T, Kittler H, Blum A, Braun R, Barata C, Cartocci A, Combalia M, Esdaile B, Guitera P, Haenssle H, Kvorning N, Lallas A, Navarrete‐Dechent C, Navarini A, Podlipnik S, Rotemberg V, Soyer H, Tognetti L, Tschandl P, Malvehy J. Position statement of the EADV Artificial Intelligence (AI) Task Force on AI‐assisted smartphone apps and web‐based services for skin disease. Journal of the European Academy of Dermatology and Venereology 2024;38(1):22 View
  22. He X, Zheng X, Ding H. Existing Barriers Faced by and Future Design Recommendations for Direct-to-Consumer Health Care Artificial Intelligence Apps: Scoping Review. Journal of Medical Internet Research 2023;25:e50342 View
  23. Hong W. Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones. JMIR mHealth and uHealth 2024;12:e48803 View
  24. Sangers T. Artificial intelligence in skin cancer smartphone applications. Die Dermatologie 2024;75(4):344 View
  25. Strzelecki M, Kociołek M, Strąkowska M, Kozłowski M, Grzybowski A, Szczypiński P. Artificial intelligence in the detection of skin cancer: State of the art. Clinics in Dermatology 2024;42(3):280 View
  26. Yuan C, Zhao D, Agaian S. UCM-Net: A lightweight and efficient solution for skin lesion segmentation using MLP and CNN. Biomedical Signal Processing and Control 2024;96:106573 View
  27. Kalidindi S. The Role of Artificial Intelligence in the Diagnosis of Melanoma. Cureus 2024 View
  28. Grove G, Reedtz G, Vangsgaard B, Eskandarani H, Haedersdal M, Andersen F, Bjerring P. Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study. Skin Research and Technology 2024;30(10) View
  29. Yuan C, Zhao D, Agaian S. MUCM-Net: a Mamba powered UCM-Net for skin lesion segmentation. Exploration of Medicine 2024:694 View
  30. Kabir M, Borshon R, Wasi M, Sultan R, Hossain A, Khan R. Skin cancer detection using lightweight model souping and ensembling knowledge distillation for memory-constrained devices. Intelligence-Based Medicine 2024;10:100176 View

Books/Policy Documents

  1. Zhao Q, Chen L. Wound Healing - Recent Advances and Future Opportunities. View
  2. Vasconcelos M, Moreira D, Alves P, Graça R, Franco R, Rosado L. Biomedical Engineering Systems and Technologies. View
  3. Strąkowska M, Kociołek M. Information Technology in Biomedicine. View
  4. Kamalanathan A, Muthu B, Kuniyil Kaleena P. Marvels of Artificial and Computational Intelligence in Life Sciences. View
  5. Pizzoli S, Durosini I, Strika M, Pravettoni G. Artificial Intelligence for Medicine. View
  6. Mangai T, Al-Turjman F. The Smart IoT Blueprint: Engineering a Connected Future. View