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

mHealth for Screening (194) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317) mHealth for Telemedicine and Homecare (367) mHealth for Wellness, Behavior Change and Prevention (2754) Usability of Apps and User Perceptions of mHealth (902) Use and User Demographics of mHealth (313) Mobile Health (mhealth) (2777) Skin Cancer and Melanoma Prevention (110) Innovations and Technology in Cancer Care (405) Innovations and Technology for Cancer Prevention and Screening (140) Mobile Apps for Cancer Care and Cancer Prevention and Screening (197)

Published on in Vol 9, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22909, first published .
Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study

Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study

Digital Natives’ Preferences on Mobile Artificial Intelligence Apps for Skin Cancer Diagnostics: Survey Study

Authors of this article:

Sarah Haggenmüller1 Author Orcid Image ;   Eva Krieghoff-Henning1 Author Orcid Image ;   Tanja Jutzi1 Author Orcid Image ;   Nicole Trapp1 Author Orcid Image ;   Lennard Kiehl1 Author Orcid Image ;   Jochen Sven Utikal2, 3 Author Orcid Image ;   Sascha Fabian4 Author Orcid Image ;   Titus Josef Brinker1 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

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  2. Lyu P, Wang Y, Meng Q, Fan P, Ma K, Xiao S, Cao X, Lin G, Dong S. Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis. Frontiers in Oncology 2022;12 View
  3. Jutzi T, Krieghoff-Henning E, Brinker T. Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome. Aktuelle Dermatologie 2022;48(03):84 View
  4. 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
  5. Jutzi T, Krieghoff-Henning E, Brinker T. Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome. Onkologische Welt 2022;13(05):253 View
  6. Jutzi T, Krieghoff-Henning E, Brinker T. Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome. Laryngo-Rhino-Otologie 2022 View
  7. Dolezel M, Smutny Z. Adoption of a COVID-19 Contact Tracing App by Czech Youth: Cross-Cultural Replication Study. JMIR Human Factors 2023;10:e45481 View
  8. Shevtsova D, Ahmed A, Boot I, Sanges C, Hudecek M, Jacobs J, Hort S, Vrijhoef H. Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study. JMIR Human Factors 2024;11:e47031 View
  9. Karaa S. Impact of direct use of artificial intelligence algorithms on patient autonomy in dermatology. Annales de Dermatologie et de Vénéréologie 2024;151(1):103245 View
  10. Gordon E, Trager M, Kontos D, Weng C, Geskin L, Dugdale L, Samie F. Ethical considerations for artificial intelligence in dermatology: a scoping review. British Journal of Dermatology 2024;190(6):789 View
  11. Frost E, Bosward R, Aquino Y, Braunack-Mayer A, Carter S. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. International Journal of Medical Informatics 2024;186:105417 View
  12. Gaube S, Biebl I, Engelmann M, Kleine A, Lermer E. Comparing preferences for skin cancer screening: AI-enabled app vs dermatologist. Social Science & Medicine 2024;349:116871 View