Exploring Empathy with Digital Humans

Loveys, Kate; Sagar, Mark; Billinghurst, Mark; Saffaryazdi, Nastaran; Broadbent, Elizabeth

doi:10.1109/vrw55335.2022.00055

Cited by 10 publications

(2 citation statements)

References 24 publications

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“…Further research into traits and behaviors such as humor, self-disclosure, facial expressions, eye gaze, body posture, and gestures ( Johanson et al, 2021 ) could inform effective human-robot interaction and human-human interactions in healthcare ( Liu and Sundar, 2018 ). Cross-cultural research could inform ongoing development (in New Zealand) of an autonomous empathy system of a digital human to understand the challenges and opportunities for empathetic interactions ( Loveys et al, 2022 ).…”

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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“…In the process, virtual reality technology and equipment, the metaverse operating system, and the metaverse trusted collaboration network support the connection from the real world to the virtual world [32] . In the virtual world, digital life technologies, i.e., digital native, digital twin, and digital companion, are used to realize the parallel evolution of digital patients and Mr. Yang [33] .…”

Section: Medical Metaverse Casementioning

confidence: 99%

Metaverse for Healthcare: Technologies, Challenges, and Vision

Yang,

Zhou,

et al. 2023

Int. J. Crowd Sci.

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The continuous enhancement of living conditions imposes higher requirements for medical and healthcare services. Although improved to a certain extent, there still exist critical challenges in current medical pattern, such as the shortage of medical resources, inefficient medical treatment, and limited medical technology level. The metaverse can offer a novel mechanism to address these problems in traditional healthcare domain, and thus, to enhance the quality of medical services. Generally, the metaverse is a dynamic feedback system that facilitates the collaboration and coexistence between the virtual and physical worlds. By fostering collaboration and evolution between intelligent agents in the virtual world, knowledge of this interdependence can be reconstructed in the digital realm. This allows problems existed in the real world to be abstracted and represented in the digital space, where models can be established and computational experiments can be conducted. The outcomes obtained can dynamically guide or control the execution of strategies in the real world, with real-world execution results serving as dynamic data inputs to continually update the virtual world's model. In addition, this paper summarizes the current research status of different healthcare application scenarios for metaverse, highlights the challenges and vision, and aims to inspire further research in this field. KEYWORDSmetaverse; healthcare; application scenario; key technologies; challenges; vision T he continuous enhancement of living conditions has expedited novel and urgent requirements for medical and healthcare service industry. The realistic demands for the healthcare service industry have become higher and more diverse [1] . Although improved to a certain extent, there still exist critical challenges in current medical patterns. From a social perspective, firstly, there is a shortage of medical resources. In fact, overall medical resources are in short supply. For instance, the medical treatment is inefficient, and thus, the coverage of chronic disease diagnosis and treatment management is relatively low at this stage [2] . Secondly, the level of medical technology is limited. Specifically, the distribution of medical resources is uneven in different regions, and there are variations in medical technology and capabilities, which needs more perfect technology platforms and patterns to enhance the overall level [3] . Thirdly, the pressure on cost control is huge, especially the cost control of medical insurance funds is hard, which requires technical means to improve medical efficiency and reduce the waste of medical resources [4] . From an individual perspective, firstly, the communication between doctors and patients is relatively inefficient. In fact, it is not easy and straightforward for patients and their family members to understand the principles of the disease and the surgical process, which makes them fear the operation and have low compliance with the treatment [5] . Secondly, the surgical accuracy and safety need to be...

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