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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a b s t r a c tEmotions-aware applications are getting a lot of attention as a way to improve the user experience, and also thanks to increasingly affordable Brain-Computer Interfaces (BCI). Thus, projects collecting emotionrelated data are proliferating, like social networks sentiment analysis or tracking students' engagement to reduce Massive Online Open Courses (MOOCs) drop out rates. All them require a common way to represent emotions so it can be more easily integrated, shared and reused by applications improving user experience. Due to the complexity of this data, our proposal is to use rich semantic models based on ontology. EmotionsOnto is a generic ontology for describing emotions and their detection and expression systems taking contextual and multimodal elements into account. The ontology has been applied in the context of EmoCS, a project that collaboratively collects emotion common sense and models it using the EmotionsOnto and other ontologies. Currently, emotion input is provided manually by users. However, experiments are being conduced to automatically measure users's emotional states using Brain-Computer Interfaces.

This paper describes a new generation of computational intelligence founded on the ancient idea of compassion called Artificial Compassion. The creation of Artificial Compassion is the result of two coinciding historical developments. The first is the increasing discoveries of human sciences in new fields like neuroendocrinology and psychoneuroimmunology. This provides the spark for Artificial Compassion. For example, we once thought with certainty that our brain is fixed for life but neuropsychology and a device called the fMRI have shown it is “plastic”. It changes constantly throughout our lives in response to our experiences. Remarkably, we also now know it is changed for the better through positive emotions like compassion, kindness and happiness. So, too, are the immune, endocrine, genetic, cardio and neural systems influenced and changed by our emotional experiences. This new perspective on emotions and plasticity validates much of ancient wisdom in medical systems outside the west. Long held assumptions about emotion are unsuitable for humanity. The second development is ‘machine rub off’. We are in symbiotic relation with our devices today and we are plastic. We are changed by our interactions but many people have computer rage. We need Artificial Compassion to replace computer rage with positive plasticity.

The author provides first steps toward building a software agent/robot with compassionate intelligence. She approaches this goal with an example software agent, EM-2. She also gives a generalized software requirements guide for anyone wishing to pursue other means of building compassionate intelligence into an AI system. The purpose of EM-2 is not to build an agent with a state of mind that mimics empathy or consciousness, but rather to create practical applications of AI systems with knowledge and reasoning methods that positively take into account the feelings and state of self and others during decision making, action, or problem solving. To program EM-2 the author re-purposes code and architectural ideas from collaborative multi-agent systems and affective common sense reasoning with new concepts and philosophies from the human arts and sciences relating to compassion. EM-2 has predicates and an agent architecture based on a meta-cognition mental process that was used on India's worst prisoners to cultivate compassion for others, Vipassana or mindfulness. She describes and presents code snippets for common sense based affective inference and the I-TMS, an Irrational Truth Maintenance System, that maintains consistency in agent memory as feelings change over time, and provides a machine theoretic description of the consistency issues of combining affect and logic. The author summarizes the growing body of new biological, cognitive and immune discoveries about compassion and the consequences of these discoveries for programmers working with human-level AI and hybrid human-robot systems.