Ethical Issues Raised by the Introduction of Artificial Companions to Older Adults with Cognitive Impairment: A Call for Interdisciplinary Collaborations

Identification of Ethical Issues and Practice Recommendations Regarding the Use of Robotic Coaching Solutions for Older Adults: Narrative Review

BACKGROUND

Technological advances in robotics, artificial intelligence, cognitive algorithms, and internet-based coaches have contributed to the development of devices capable of responding to some of the challenges resulting from demographic aging. Numerous studies have explored the use of robotic coaching solutions (RCSs) for supporting healthy behaviors in older adults and have shown their benefits regarding the quality of life and functional independence of older adults at home. However, the use of RCSs by individuals who are potentially vulnerable raises many ethical questions. Establishing an ethical framework to guide the development, use, and evaluation practices regarding RCSs for older adults seems highly pertinent.

OBJECTIVE

The objective of this paper was to highlight the ethical issues related to the use of RCSs for health care purposes among older adults and draft recommendations for researchers and health care professionals interested in using RCSs for older adults.

METHODS

We conducted a narrative review of the literature to identify publications including an analysis of the ethical dimension and recommendations regarding the use of RCSs for older adults. We used a qualitative analysis methodology inspired by a Health Technology Assessment model. We included all article types such as theoretical papers, research studies, and reviews dealing with ethical issues or recommendations for the implementation of these RCSs in a general population, particularly among older adults, in the health care sector and published after 2011 in either English or French. The review was performed between August and December 2021 using the PubMed, CINAHL, Embase, Scopus, Web of Science, IEEE Explore, SpringerLink, and PsycINFO databases. Selected publications were analyzed using the European Network of Health Technology Assessment Core Model (version 3.0) around 5 ethical topics: benefit-harm balance, autonomy, privacy, justice and equity, and legislation.

RESULTS

In the 25 publications analyzed, the most cited ethical concerns were the risk of accidents, lack of reliability, loss of control, risk of deception, risk of social isolation, data confidentiality, and liability in case of safety problems. Recommendations included collecting the opinion of target users, collecting their consent, and training professionals in the use of RCSs. Proper data management, anonymization, and encryption appeared to be essential to protect RCS users' personal data.

CONCLUSIONS

Our analysis supports the interest in using RCSs for older adults because of their potential contribution to individuals' quality of life and well-being. This analysis highlights many ethical issues linked to the use of RCSs for health-related goals. Future studies should consider the organizational consequences of the implementation of RCSs and the influence of cultural and socioeconomic specificities of the context of experimentation. We suggest implementing a scalable ethical and regulatory framework to accompany the development and implementation of RCSs for various aspects related to the technology, individual, or legal aspects.

Evaluating cognitive performance: Traditional methods vs. ChatGPT

Background

NLP models like ChatGPT promise to revolutionize text-based content delivery, particularly in medicine. Yet, doubts remain about ChatGPT's ability to reliably support evaluations of cognitive performance, warranting further investigation into its accuracy and comprehensiveness in this area.

Method

A cohort of 60 cognitively normal individuals and 30 stroke survivors underwent a comprehensive evaluation, covering memory, numerical processing, verbal fluency, and abstract thinking. Healthcare professionals and NLP models GPT-3.5 and GPT-4 conducted evaluations following established standards. Scores were compared, and efforts were made to refine scoring protocols and interaction methods to enhance ChatGPT's potential in these evaluations.

Result

Within the cohort of healthy participants, the utilization of GPT-3.5 revealed significant disparities in memory evaluation compared to both physician-led assessments and those conducted utilizing GPT-4 (P < 0.001). Furthermore, within the domain of memory evaluation, GPT-3.5 exhibited discrepancies in 8 out of 21 specific measures when compared to assessments conducted by physicians (P < 0.05). Additionally, GPT-3.5 demonstrated statistically significant deviations from physician assessments in speech evaluation (P = 0.009). Among participants with a history of stroke, GPT-3.5 exhibited differences solely in verbal assessment compared to physician-led evaluations (P = 0.002). Notably, through the implementation of optimized scoring methodologies and refinement of interaction protocols, partial mitigation of these disparities was achieved.

Conclusion

ChatGPT can produce evaluation outcomes comparable to traditional methods. Despite differences from physician evaluations, refinement of scoring algorithms and interaction protocols has improved alignment. ChatGPT performs well even in populations with specific conditions like stroke, suggesting its versatility. GPT-4 yields results closer to physician ratings, indicating potential for further enhancement. These findings highlight ChatGPT's importance as a supplementary tool, offering new avenues for information gathering in medical fields and guiding its ongoing development and application.

Current status, challenges and future prospects in computational psychiatry: a narrative review

BACKGROUND

Computational psychiatry is an area of scientific knowledge which lies at the intersection of neuroscience, psychiatry, and computer science. It employs mathematical models and computational simulations to shed light on the complexities inherent to mental disorders.

AIM

The aim of this narrative review is to offer insight into the current landscape of computational psychiatry, to discuss its significant challenges, as well as the potential opportunities for the fields growth.

METHODS

The authors have carried out a narrative review of the scientific literature published on the topic of computational psychiatry. The literature search was performed in the PubMed, eLibrary, PsycINFO, and Google Scholar databases. A descriptive analysis was used to summarize the published information on the theoretical and practical aspects of computational psychiatry.

RESULTS

The article relates the development of the scientific approach in computational psychiatry since the mid-1980s. The data on the practical application of computational psychiatry in modeling psychiatric disorders and explaining the mechanisms of how psychopathological symptomatology develops (in schizophrenia, attention-deficit/hyperactivity disorder, autism spectrum disorder, anxiety disorders, obsessive-compulsive disorder, substance use disorders) are summarized. Challenges, limitations, and the prospects of computational psychiatry are discussed.

CONCLUSION

The capacity of current computational technologies in psychiatry has reached a stage where its integration into psychiatric practice is not just feasible but urgently needed. The hurdles that now need to be addressed are no longer rooted in technological advancement, but in ethics, education, and understanding.

Mapping emerging technologies in aged care: results from an in-depth online research

BACKGROUND

Emerging Technologies (ETs) have recently acquired great relevance in elderly care. The exceptional experience with SARS-CoV-2 pandemic has emphasized the usefulness of ETs in the assistance and remote monitoring of older adults. Technological devices have also contributed to the preservation of social interactions, thus reducing isolation and loneliness. The general purpose of this work is to provide a comprehensive and updated overview of the technologies currently employed in elderly care. This objective was achieved firstly, by mapping and classifying the ETs currently available on the market and, secondly, by assessing the impact of such ETs on elderly care, exploring the ethical values promoted, as well as potential ethical threats.

METHODS

An in-depth search was carried out on Google search engine, by using specific keywords (e.g. technology, monitoring techniques, ambient intelligence; elderly, older adults; care and assistance). Three hundred and twenty-eight technologies were originally identified. Then, based on a predetermined set of inclusion-exclusion criteria, two hundreds and twenty-two technologies were selected.

RESULTS

A comprehensive database was elaborated, where the two hundred and twenty-two ETs selected were classified as follows: category; developmental stage; companies and/or partners; functions; location of development; time of development; impact on elderly care; target; website. From an in-depth qualitative analysis, some ethically-related contents and themes emerged, namely: questions related to safety, independence and active aging, connectedness, empowerment and dignity, cost and efficiency. Although not reported by developers, a close analysis of website contents highlights that positive values are often associated with potential risks, notably privacy threats, deception, dehumanization of care.

CONCLUSIONS

Research findings may ultimately lead to a better understanding of the impact of ETs on elderly people.

Companion robots to mitigate loneliness among older adults: Perceptions of benefit and possible deception

Objective

Given growing interest in companion robots to mitigate loneliness, large-scale studies are needed to understand peoples' perspectives on the use of robots to combat loneliness and attendant ethical issues. This study examines opinions about artificial companion (AC) robots regarding deception with dementia and impact on loneliness.

Methods

Data are from a survey of 825 members of the OHSU Research via Internet Technology and Experience cohort (response rate = 45%). Sixty percent (n = 496) of the age diverse sample (range = 25-88; M = 64; SD = 13.17) is over 64, allowing us to compare across age and consider current and future older adults. Ordinal logistic regressions examined relationships between age, health, and other socio-demographic characteristics and perceptions of impact on loneliness and comfort with deception.

Results

Most participants (68.7%) did not think an AC robot would make them feel less lonely and felt somewhat-to-very uncomfortable (69.3%) with the idea of being allowed to believe that an artificial companion is human. In adjusted models, one additional year of age was associated with lower likelihood of perceived benefit of reducing loneliness [Odds Ratio (OR) = 0.98; (0.97-0.99), p = 0.003] and lower comfort with deception [OR = 0.99; (0.97-1.00), p = 0.044]. Being female was associated with lower likelihood of comfort with deception [OR = 0.68; (0.50-0.93), p = 0.014] and high confidence using computers with greater comfort [OR = 2.18; (1.42-3.38), p < 0.001].

Discussion

There was not strong support for AC robots to mitigate loneliness. Most participants were uncomfortable with this form of deception, indicating need for design solutions for those who want to avoid this possibility, as well as greater attentiveness to desirability and comfort across age and gender.

The Living Alone with Cognitive Impairment Project's Policy Advisory Group on Long-Term Services and Supports: Setting a Research Equity Agenda

(1) Background: A United States national policy advisory group (PAG) was convened to identify barriers and facilitators to expand formal long-term services and support (LTSS) for people living alone with cognitive impairment (PLACI), with a focus on equitable access among diverse older adults. The PAG's insights will inform the research activities of the Living Alone with Cognitive Impairment Project, which is aimed at ensuring the equitable treatment of PLACI. (2) Methods: The PAG identified barriers and facilitators of providing effective and culturally relevant LTSS to PLACI via one-on-one meetings with researchers, followed by professionally facilitated discussions among themselves. (3) Results: The PAG identified three factors that were relevant to providing effective and culturally relevant LTSS to PLACI: (i) better characterization of PLACI, (ii) leveraging the diagnosis of cognitive impairment, and (iii) expanding and enhancing services. For each factor, the PAG identified barriers and facilitators, as well as directions for future research. (4) Conclusions: The barriers and facilitators the PAG identified inform an equity research agenda that will help inform policy change.

Health Professionals’ Experience Using an Azure Voice-Bot to Examine Cognitive Impairment (WAY2AGE)

Virtual Assistants (VA) are a new groundbreaking tool for screening cognitive impairment by healthcare professionals. By providing the volume of data needed in healthcare guidance, better treatment monitoring and optimization of costs are expected. One of the first steps in the development of these items is the experience of the healthcare professionals in their use. The general goal of the current project, WAY2AGE, is to examine healthcare professionals’ experience in using an Azure voice-bot for screening cognitive impairment. In this way, back-end services, such as the ChatBot, Speech Service and databases, are provided by the cloud platform Azure (Paas) for a pilot study. Most of the underlying scripts are implemented in Python, Net, JavaScript and open software. A sample of 30 healthcare workers volunteered to participate by answering a list of question in a survey set-up, following the example provided in the previous literature. Based on the current results, WAY2AGE was evaluated very positively in several categories. The main challenge of WAY2AGE is the articulation problems of some older people, which can lead to errors in the transcription of audio to text that will be addressed in the second phase. Following an analysis of the perception of a group of thirty health professionals on its usability, potential limitations and opportunities for future research are discussed.

Toward Responsible Artificial Intelligence in Long-Term Care: A Scoping Review on Practical Approaches

BACKGROUND AND OBJECTIVES

Artificial intelligence (AI) is widely positioned to become a key element of intelligent technologies used in the long-term care (LTC) for older adults. The increasing relevance and adoption of AI has encouraged debate over the societal and ethical implications of introducing and scaling AI. This scoping review investigates how the design and implementation of AI technologies in LTC is addressed responsibly: so-called responsible innovation (RI).

RESEARCH DESIGN AND METHODS

We conducted a systematic literature search in 5 electronic databases using concepts related to LTC, AI, and RI. We then performed a descriptive and thematic analysis to map the key concepts, types of evidence, and gaps in the literature.

RESULTS

After reviewing 3,339 papers, 25 papers were identified that met our inclusion criteria. From this literature, we extracted 3 overarching themes: user-oriented AI innovation; framing AI as a solution to RI issues; and context-sensitivity. Our results provide an overview of measures taken and recommendations provided to address responsible AI innovation in LTC.

DISCUSSION AND IMPLICATIONS

The review underlines the importance of the context of use when addressing responsible AI innovation in LTC. However, limited empirical evidence actually details how responsible AI innovation is addressed in context. Therefore, we recommend expanding empirical studies on RI at the level of specific AI technologies and their local contexts of use. Also, we call for more specific frameworks for responsible AI innovation in LTC to flexibly guide researchers and innovators. Future frameworks should clearly distinguish between RI processes and outcomes.

Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review

Background

Artificial intelligence (AI) holds the promise of supporting nurses’ clinical decision-making in complex care situations or conducting tasks that are remote from direct patient interaction, such as documentation processes. There has been an increase in the research and development of AI applications for nursing care, but there is a persistent lack of an extensive overview covering the evidence base for promising application scenarios.

Objective

This study synthesizes literature on application scenarios for AI in nursing care settings as well as highlights adjacent aspects in the ethical, legal, and social discourse surrounding the application of AI in nursing care.

Methods

Following a rapid review design, PubMed, CINAHL, Association for Computing Machinery Digital Library, Institute of Electrical and Electronics Engineers Xplore, Digital Bibliography & Library Project, and Association for Information Systems Library, as well as the libraries of leading AI conferences, were searched in June 2020. Publications of original quantitative and qualitative research, systematic reviews, discussion papers, and essays on the ethical, legal, and social implications published in English were included. Eligible studies were analyzed on the basis of predetermined selection criteria.

Results

The titles and abstracts of 7016 publications and 704 full texts were screened, and 292 publications were included. Hospitals were the most prominent study setting, followed by independent living at home; fewer application scenarios were identified for nursing homes or home care. Most studies used machine learning algorithms, whereas expert or hybrid systems were entailed in less than every 10th publication. The application context of focusing on image and signal processing with tracking, monitoring, or the classification of activity and health followed by care coordination and communication, as well as fall detection, was the main purpose of AI applications. Few studies have reported the effects of AI applications on clinical or organizational outcomes, lacking particularly in data gathered outside laboratory conditions. In addition to technological requirements, the reporting and inclusion of certain requirements capture more overarching topics, such as data privacy, safety, and technology acceptance. Ethical, legal, and social implications reflect the discourse on technology use in health care but have mostly not been discussed in meaningful and potentially encompassing detail.

Conclusions

The results highlight the potential for the application of AI systems in different nursing care settings. Considering the lack of findings on the effectiveness and application of AI systems in real-world scenarios, future research should reflect on a more nursing care–specific perspective toward objectives, outcomes, and benefits. We identify that, crucially, an advancement in technological-societal discourse that surrounds the ethical and legal implications of AI applications in nursing care is a necessary next step. Further, we outline the need for greater participation among all of the stakeholders involved.

Companion robots for older adults: Rodgers' evolutionary concept analysis approach

This study aims to analyze the concept of companion robots for older adults from the perspective of nursing. This study employed a concept analysis. The literature from July 2011 to June 2021 was sought from databases using specific keywords. Any quantitative or qualitative study published in English or Korean focusing on companion robots for older adults was included in the study. Rodgers' evolutionary concept analysis was used to clarify the antecedents, attributes, and consequences. Seventy-five eligible articles were studied. The findings were categorized into antecedents, attributes, and consequences. Companion robot antecedents were classified into individual factors, attitude toward robots, and caregiver and social factors. The defining attributes included human-robot interaction, function, features, structure, cost, and management of the robot being a companion. Consequences were categorized into user, caregiver, and health related. Companion robots are designed to enhance well-being, quality of life, and independence by providing service and companionship and assisting daily life. This mainly includes cognitive and social support, mobility support, relaxation, health monitoring, and self-care support through human-robot interaction. The attributes, antecedents, and consequences of companion robots identified in this study can inform future decision making and interventions by caregivers for aging in place.

In principle obstacles for empathic AI: why we can't replace human empathy in healthcare

What are the limits of the use of artificial intelligence (AI) in the relational aspects of medical and nursing care? There has been a lot of recent work and applications showing the promise and efficiency of AI in clinical medicine, both at the research and treatment levels. Many of the obstacles discussed in the literature are technical in character, regarding how to improve and optimize current practices in clinical medicine and also how to develop better data bases for optimal parameter adjustments and predictive algorithms. This paper argues that there are also in principle obstacles to the application of AI in clinical medicine and care where empathy is important, and that these problems cannot be solved with any of the technical and theoretical approaches that shape the current application of AI in specific areas of clinical medicine in which care for patients is fundamental. This is important, because it generates specific risks that may be overlooked otherwise, and it justifies the necessity of human monitoring and emotional intervention in clinical medicine. Consequently, difficult issues concerning moral and legal responsibility may ensue if these in principle problems are ignored.

Domain Experts on Dementia-Care Technologies: Mitigating Risk in Design and Implementation

There is an urgent need to learn how to appropriately integrate technologies into dementia care. The aims of this Delphi study were to project which technologies will be most prevalent in dementia care in five years, articulate potential benefits and risks, and identify specific options to mitigate risks. Participants were also asked to identify technologies that are most likely to cause value tensions and thus most warrant a conversation with an older person with mild dementia when families are deciding about their use. Twenty-one interdisciplinary domain experts from academia and industry in aging and technology in the U.S. and Canada participated in a two-round online survey using the Delphi approach with an 84% response rate and no attrition between rounds. Rankings were analyzed using frequency counts and written-in responses were thematically analyzed. Twelve technology categories were identified along with a detailed list of risks and benefits for each. Suggestions to mitigate the most commonly raised risks are categorized as follows: intervene during design, make specific technical choices, build in choice and control, require data transparency, place restrictions on data use and ensure security, enable informed consent, and proactively educate users. This study provides information that is needed to navigate person-centered technology use in dementia care. The specific recommendations participants offered are relevant to designers, clinicians, researchers, ethicists, and policy makers and require proactive engagement from design through implementation.

Addressing the Ethics of Telepresence Applications Through End-User Engagement

Portacolone et al.'s Ethics Review highlights the ethical challenges associated with the implementation of telepresence devices and applications in the context of aging and dementia. In this response, we review ethical considerations as they relate to specific modalities of telepresence, with an emphasis on the continuum of potential interaction agents, from known individuals to fully automated and intelligent interlocutors. We further discuss areas in need of empirical evidence to inform regulatory efforts in telepresence. We close with a call for meaningful end-user engagement at all stages of technology development.