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

mHealth for Screening (298) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1741) mHealth for Telemedicine and Homecare (533) mHealth for Wellness, Behavior Change and Prevention (3332) Usability of Apps and User Perceptions of mHealth (1195) Use and User Demographics of mHealth (405) Mobile Health (mhealth) (3481) Skin Cancer and Melanoma Prevention (124) Innovations and Technology in Cancer Care (533) Innovations and Technology for Cancer Prevention and Screening (202) Mobile Apps for Cancer Care and Cancer Prevention and Screening (267)

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 (22) Tweetations (3) Metrics

Journals

  1. Fritsch S, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. DIGITAL HEALTH 2022;8:205520762211167 View
  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
  13. Singla D, Verma N. Performance Analysis of Authentication System: A Systematic Literature Review. Recent Advances in Computer Science and Communications 2024;17(7) View
  14. Jones O, Calanzani N, Scott S, Matin R, Emery J, Walter F. User and Developer Views on Using AI Technologies to Facilitate the Early Detection of Skin Cancers in Primary Care Settings: Qualitative Semistructured Interview Study. JMIR Cancer 2025;11:e60653 View
  15. Kuppanda P, Janda M, Soyer H, Caffery L. What Are Patients’ Perceptions and Attitudes Regarding the Use of Artificial Intelligence in Skin Cancer Screening and Diagnosis? Narrative Review. Journal of Investigative Dermatology 2025;145(8):1858 View
  16. Boxebeld S, Mouter N, van Exel J. Public preferences for skin cancer prevention policies: a discrete choice experiment in three European countries. Social Science & Medicine 2025;378:118155 View
  17. Foresman G, Biro J, Tran A, MacRae K, Kazi S, Schubel L, Visconti A, Gallagher W, Smith K, Giardina T, Haskell H, Miller K. Patient Perspectives on Artificial Intelligence in Health Care: Focus Group Study for Diagnostic Communication and Tool Implementation. Journal of Participatory Medicine 2025;17:e69564 View
  18. Werneburg G, Wyndaele M, Speich J, Finazzi Agro E, Belal M, Malde S, Bou Kheir G, Huri E, Nambiar A, Mosiello G, Lombardo R, Toozs‐Hobson P, Chapple C, Wein A, Abrams P, Rademakers K. What Is Required for AI to Improve the Assessment and Treatment of Patients With Lower Urinary Tract Dysfunction? ICI‐RS 2025. Neurourology and Urodynamics 2025 View

Books/Policy Documents

  1. Neriyanuri S, Palepu S, Vats P, Baweja B, Nema R, Banerjee M, Kushwah A. Advances in Cancer Detection, Prediction, and Prognosis Using Artificial Intelligence and Machine Learning. View

Conference Proceedings

  1. Bustos G, Murillo Sanchez X, Salazar Florez E. 2024 20th International Symposium on Medical Information Processing and Analysis (SIPAIM). A Portable Embedded System for Skin Lesions Detection View
  2. Kim J, MacLellan C, Tonetto L. 2025 IEEE Conference on Artificial Intelligence (CAI). Design Requirements for AI-Enabled Progressive Web Applications to Decrease Risk of Breast Cancer View
  3. Neyra S, Portugal E, Cornejo J. 2025 9th International Symposium on Innovative Approaches in Smart Technologies (ISAS). Skin Cancer Detection through Mobile Applications using Artificial Intelligence: Technical Review View