The Environmental Costs of Artificial Intelligence for Healthcare.

Healthcare has emerged as a key setting where expectations are rising for the potential benefits of artificial intelligence (AI), encompassing a range of technologies of varying utility and benefit. This paper argues that, even as the development of AI for healthcare has been pushed forward by a range of public and private actors, insufficient attention has been paid to a key contradiction at the center of AI for healthcare: that its pursuit to improve health is necessarily accompanied by environmental costs which pose risks to human and environmental health-costs which are not necessarily directly borne by those benefiting from the technologies. This perspective paper begins by examining the purported promise of AI in healthcare, contrasting this with the environmental costs which arise across the AI lifecycle, to highlight this contradiction inherent in the pursuit of AI. Its advancement-including in healthcare-is often described through deterministic language that presents it as inevitable. Yet, this paper argues that there is need for recognition of the environmental harm which this pursuit can lead to. Given recent initiatives to incorporate stakeholder involvement into decision-making around AI, the paper closes with a call for an expanded conception of stakeholders in AI for healthcare, to include consideration of those who may be indirectly affected by its development and deployment.

Autism and emotion recognition technologies in the workplace.

LAY ABSTRACT: Technologies using artificial intelligence to recognize people's emotional states are increasingly being developed under the name of emotional recognition technologies. Emotion recognition technologies claim to identify people's emotional states based on data, like facial expressions. This is despite research providing counterevidence that emotion recognition technologies are founded on bad science and that it is not possible to correctly identify people's emotions in this way. The use of emotion recognition technologies is widespread, and they can be harmful when they are used in the workplace, especially for autistic workers. Although previous research has shown that the origins of emotion recognition technologies relied on autistic people, there has been little research on the impact of emotion recognition technologies on autistic people when it is used in the workplace. Through a review of recent academic studies, this article looks at the development and implementation processes of emotion recognition technologies to show how autistic people in particular may be disadvantaged or harmed by the development and use of the technologies. This article closes with a call for more research on autistic people's perception of the technologies and their impact, with involvement from diverse participants.

Moving beyond Technical Issues to Stakeholder Involvement: Key Areas for Consideration in the Development of Human-Centred and Trusted AI in Healthcare.

Discussion around the increasing use of AI in healthcare tends to focus on the technical aspects of the technology rather than the socio-technical issues associated with implementation. In this paper, we argue for the development of a sustained societal dialogue between stakeholders around the use of AI in healthcare. We contend that a more human-centred approach to AI implementation in healthcare is needed which is inclusive of the views of a range of stakeholders. We identify four key areas to support stakeholder involvement that would enhance the development, implementation, and evaluation of AI in healthcare leading to greater levels of trust. These are as follows: (1) aligning AI development practices with social values, (2) appropriate and proportionate involvement of stakeholders, (3) understanding the importance of building trust in AI, (4) embedding stakeholder-driven governance to support these activities.

The ethics of advancing artificial intelligence in healthcare: analyzing ethical considerations for Japan's innovative AI hospital system.

Public and private investments into developing digital health technologies-including artificial intelligence (AI)-are intensifying globally. Japan is a key case study given major governmental investments, in part through a Cross-Ministerial Strategic Innovation Promotion Program (SIP) for an "Innovative AI Hospital System." Yet, there has been little critical examination of the SIP Research Plan, particularly from an ethics approach. This paper reports on an analysis of the Plan to identify the extent to which it addressed ethical considerations set out in the World Health Organization's 2021 Guidance on the Ethics and Governance of Artificial Intelligence for Health. A coding framework was created based on the six ethical principles proposed in the Guidance and was used as the basis for a content analysis. 101 references to aspects of the framework were identified in the Plan, but attention to the ethical principles was found to be uneven, ranging from the strongest focus on the potential benefits of AI to healthcare professionals and patients (n = 44; Principle 2), to no consideration of the need for responsive or sustainable AI (n = 0; Principle 6). Ultimately, the findings show that the Plan reflects insufficient consideration of the ethical issues that arise from developing and implementing AI for healthcare purposes. This case study is used to argue that, given the ethical complexity of the use of digital health technologies, consideration of the full range of ethical concerns put forward by the WHO must urgently be made visible in future plans for AI in healthcare.

Perspectives on artificial intelligence in healthcare from a Patient and Public Involvement Panel in Japan: an exploratory study.

Patients and members of the public are the end users of healthcare, but little is known about their views on the use of artificial intelligence (AI) in healthcare, particularly in the Japanese context. This paper reports on an exploratory two-part workshop conducted with members of a Patient and Public Involvement Panel in Japan, which was designed to identify their expectations and concerns about the use of AI in healthcare broadly. 55 expectations and 52 concerns were elicited from workshop participants, who were then asked to cluster and title these expectations and concerns. Thematic content analysis was used to identify 12 major themes from this data. Participants had notable expectations around improved hospital administration, improved quality of care and patient experience, and positive changes in roles and relationships, and reductions in costs and disparities. These were counterbalanced by concerns about problematic changes to healthcare and a potential loss of autonomy, as well as risks around accountability and data management, and the possible emergence of new disparities. The findings reflect participants' expectations for AI as a possible solution for long-standing issues in healthcare, though their overall balanced view of AI mirrors findings reported in other contexts. Thus, this paper offers initial, novel insights into perspectives on AI in healthcare from the Japanese context. Moreover, the findings are used to argue for the importance of involving patient and public stakeholders in deliberation on AI in healthcare.

Patient involvement in priority-setting for medical research: A mini review of initiatives in the rare disease field.

Patient involvement (PI) in determining medical research priorities is an important way to ensure that limited research funds are allocated to best serve patients. As a disease area for which research funds are limited, we see a particular utility for PI in priority-setting for medical research on rare diseases. In this review, we argue that PI initiatives are an important form of evidence for policymaking. We conducted a study to identify the extent to which PI initiatives are being conducted in the rare disease field, the features of such initiatives, the trends in the priorities elicited, and the extent to which translation into policy is reported in the academic literature. Here, we report the results of this exploratory review of the English-language literature gathered through online databases and search engines, with the aim of identifying journal articles published prior to December 2020, describing PI initiatives focused on determining priorities for medical research funding in the rare disease field. We identified seven recently-published articles and found that the majority made use of structured methodologies to ensure the robustness of the evidence produced, but found little reported practical implementation or concrete plans for implementation of the results of the initiatives. We conclude that priority-setting initiatives are meaningful mechanisms for involving patients in determining research directions. However, we highlight the importance of translation into policy as a necessary next step to fully utilize the results and move beyond well-intentioned exercises. Finally, we draw attention to the benefits of involving patients throughout this process.