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How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?
We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933806/ https://www.ncbi.nlm.nih.gov/pubmed/35254278 http://dx.doi.org/10.2196/29506 |
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author | Yang, Hsuan-Chia Rahmanti, Annisa Ristya Huang, Chih-Wei Li, Yu-Chuan Jack |
author_facet | Yang, Hsuan-Chia Rahmanti, Annisa Ristya Huang, Chih-Wei Li, Yu-Chuan Jack |
author_sort | Yang, Hsuan-Chia |
collection | PubMed |
description | We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients’ clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face—one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment. |
format | Online Article Text |
id | pubmed-8933806 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-89338062022-03-20 How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? Yang, Hsuan-Chia Rahmanti, Annisa Ristya Huang, Chih-Wei Li, Yu-Chuan Jack J Med Internet Res Viewpoint We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients’ clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face—one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment. JMIR Publications 2022-03-04 /pmc/articles/PMC8933806/ /pubmed/35254278 http://dx.doi.org/10.2196/29506 Text en ©Hsuan-Chia Yang, Annisa Ristya Rahmanti, Chih-Wei Huang, Yu-Chuan Jack Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.03.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Viewpoint Yang, Hsuan-Chia Rahmanti, Annisa Ristya Huang, Chih-Wei Li, Yu-Chuan Jack How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? |
title | How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? |
title_full | How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? |
title_fullStr | How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? |
title_full_unstemmed | How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? |
title_short | How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes? |
title_sort | how can research on artificial empathy be enhanced by applying deepfakes? |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8933806/ https://www.ncbi.nlm.nih.gov/pubmed/35254278 http://dx.doi.org/10.2196/29506 |
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