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

Yang, Hsuan-Chia; Rahmanti, Annisa Ristya; Huang, Chih-Wei; Li, Yu-Chuan

doi:10.2196/29506

Cited by 22 publications

(6 citation statements)

References 41 publications

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“…There is a sense of urgency in this literature to teach health professionals essential digital skills and overhaul curricula ( Konstantinidis et al, 2022 ) as well as to introduce AI technologies in educational environments in safe and effective ways that address risks and responsibilities ( Combs and Combs, 2019 ). Such as the opportunities and implications of using standardized virtual patients (VPs) ( Gavarkovs, 2019 ), patient clinical scenarios ( Yang et al, 2022 ), and digital simulations ( Patel et al, 2020 ).…”

Section: Resultsmentioning

confidence: 99%

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

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BackgroundAdvances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.ObjectivesThe aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?Materials and methodsA systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011–2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.ResultsSearches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan–Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.ConclusionThere is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.ImplicationsIn a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring.

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Section: Resultsmentioning

confidence: 99%

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

et al. 2023

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“…Some other technical attempts to deidentify individuals on video footages while preserving their dynamic facial attributes in real-time have been made (e.g., “face-swap”). 35 , 48 , 49 Such approaches need to be empirically tested for their utility, reliability, and efficiency for easy implementation in research and clinical practices.…”

Section: Discussionmentioning

confidence: 99%

Open video data sharing in developmental science and clinical practice

Marschik¹,

Kulvičius²,

Flügge³

et al. 2023

iScience

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“…This means that the Emotionality dimension is also positively correlated with the emotional intelligence needed to recognize deepfakes. Yang et al (2022) emphasized the pivotal role of emotional intelligence in improving artificial intelligence technology so that it becomes a useful deepfake in the context of clinical encounters. By knowing that deepfakes themselves are increasingly being prepared with elements of emotional intelligence, then recognizing deepfakes also requires a better one; and this intelligence can actually be found in people with higher Emotionality.…”

Section: Discussionmentioning

confidence: 99%

Prediction of self-efficacy in recognizing deepfakes based on personality traits 

Abraham

Putra

Prayoga³

et al. 2022

F1000Res

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Background: While deepfake technology is still relatively new, concerns are increasing as they are getting harder to spot. The first question we need to ask is how good humans are at recognizing deepfakes - realistic-looking videos or images that show people doing or saying things that they never actually did or said generated by an artificial intelligence-based technology. Research has shown that an individual’s self-efficacy correlates with their ability to detect deepfakes. Previous studies suggest that one of the most fundamental predictors of self-efficacy are personality traits. In this study, we ask the question: how can people’s personality traits influence their efficacy in recognizing deepfakes? Methods: Predictive correlational design with a multiple linear regression data analysis technique was used in this study. The participants of this study were 200 Indonesian young adults. Results: The results showed that only traits of Honesty-humility and Agreeableness were able to predict the efficacy, in the negative and positive directions, respectively. Meanwhile, traits of Emotionality, Extraversion, Conscientiousness, and Openness cannot predict it. Conclusion: Self-efficacy in spotting deepfakes can be predicted by certain personality traits.

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

Cited by 22 publications

References 41 publications

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Open video data sharing in developmental science and clinical practice

Prediction of self-efficacy in recognizing deepfakes based on personality traits

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