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Viewpoints and Perspectives (508) New Methods (932) Artificial Intelligence (1329) Ethics, Privacy, and Legal Issues (471) Clinical Communication, Electronic Consultation and Telehealth (575) Email Communication, Web-Based Communication, Secure Messaging (157) Digital Health, Telehealth and e-Innovation in Clinical Settings (387) Methodological Innovations (23)

Published on in Vol 24, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29506, first published .
How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

Authors of this article:

Hsuan-Chia Yang1, 2, 3, 4 Author Orcid Image ;   Annisa Ristya Rahmanti1, 2, 5 Author Orcid Image ;   Chih-Wei Huang2 Author Orcid Image ;   Yu-Chuan Jack Li1, 2, 3, 6 Author Orcid Image
Article Authors Cited by (17) Tweetations (9) Metrics

Journals

  1. Abraham J, Putra H, Prayoga T, Warnars H, Manurung R, Nainggolan T. Prediction of self-efficacy in recognizing deepfakes based on personality traits. F1000Research 2022;11:1529 View
  2. Leone M. The Spiral of Digital Falsehood in Deepfakes. International Journal for the Semiotics of Law - Revue internationale de Sémiotique juridique 2023;36(2):385 View
  3. Morrow E, Zidaru T, Ross F, Mason C, Patel K, Ream M, Stockley R. Artificial intelligence technologies and compassion in healthcare: A systematic scoping review. Frontiers in Psychology 2023;13 View
  4. Huang C, Wu B, Nguyen P, Wang H, Kao C, Lee P, Rahmanti A, Hsu J, Yang H, Li Y. Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy. Computer Methods and Programs in Biomedicine 2023;233:107480 View
  5. Abraham J, Putra H, Prayoga T, Warnars H, Manurung R, Nainggolan T. Prediction of self-efficacy in recognizing deepfakes based on personality traits. F1000Research 2023;11:1529 View
  6. Abraham J, Putra H, Prayoga T, Warnars H, Manurung R, Nainggolan T. Prediction of self-efficacy in recognizing deepfakes based on personality traits. F1000Research 2023;11:1529 View
  7. Limoncin E, Gaudio B, Sansone A, Jannini T, Ciocca G, Mollaioli D, Jannini E. The role of metaverse on sexual health of people with disabilities: narrative review. International Journal of Impotence Research 2023 View
  8. Renier L, Shubham K, Vijay R, Mishra S, Kleinlogel E, Jayagopi D, Schmid Mast M. A deepfake-based study on facial expressiveness and social outcomes. Scientific Reports 2024;14(1) View
  9. Walczyna T, Piotrowski Z. Fast Fake: Easy-to-Train Face Swap Model. Applied Sciences 2024;14(5):2149 View
  10. Cheng X. Refining CycleGAN with attention mechanisms and age-Aware training for realistic Deepfakes. Heliyon 2024;10(16):e36665 View
  11. Li C, Hou J, Chou C, Shih Y, Dufau S, Lin P, McGonigal A, Yu H. Face swapping in seizure videos for patient deidentification. Epilepsy Research 2024;207:107453 View
  12. Navarro Martínez O, Fernández-García D, Cuartero Monteagudo N, Forero-Rincón O. Possible Health Benefits and Risks of DeepFake Videos: A Qualitative Study in Nursing Students. Nursing Reports 2024;14(4):2746 View
  13. Wu J, Tsai Y, Chen Y, Hsiao F, Hsu C, Lin Y, Liao L. Digital transformation of mental health therapy by integrating digitalized cognitive behavioral therapy and eye movement desensitization and reprocessing. Medical & Biological Engineering & Computing 2024 View

Books/Policy Documents

  1. Alharbi K, Semwal S. Proceedings of the Future Technologies Conference (FTC) 2023, Volume 4. View
  2. Bai X, Haque T, Mohan S, Cai Y, Jeong B, Halász Á, Das S. Engineering Applications of Neural Networks. View