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The World Health Organization and other institutions are considering Artificial Intelligence (AI) as a technology that can potentially address some health system gaps, especially the reduction of global health inequalities in low- and middle-income countries (LMICs). However, because most AI-based health applications are developed and implemented in high-income countries, their use in LMICs contexts is recent and there is a lack of robust local evaluations to guide decision-making in low-resource settings. After discussing the potential benefits as well as the risks and challenges raised by AI-based health care, we propose five building blocks to guide the development and implementation of more responsible, sustainable, and inclusive AI health care technologies in LMICs.

The COVID-19 pandemic has accelerated the deployment of telehealth services in many countries around the world. It also revealed many barriers and challenges to the use of digital health technologies in health organisations and systems that have persisted for decades. One of these barriers is what is known as the ‘wrong pocket’ problem – where an organisation or sector makes expenditures and investments to address a given problem, but the benefits (return on investment) are captured by another organisation or sector (the wrong pocket). This problem is the origin of many difficulties in public policies and programmes (e.g. education, environment, justice and public health), especially in terms of sustainability and scaling-up of technology and innovation. In this essay/perspective, we address the wrong pocket problem in the context of a major telehealth project in Canada. We show how the problem of sharing investments and expenses, as well as the redistribution of economies among the different stakeholders involved, may have threatened the sustainability and scaling-up of this project, even though it has demonstrated the clinical utility and contributed to improving the health of populations. In conclusion, the wrong pocket problem may be decisive in the reduced take-up, and potential failure, of certain telehealth programmes and policies. It is not enough for a telehealth service to be clinically relevant and ‘efficient’, it must also be mutually beneficial to the various stakeholders involved, particularly in terms of the equitable sharing of costs and benefits (return on investment) associated with the implementation of this new service model. Finally, the wrong pocket concept offers a helpful lens for studying the success, sustainability, and scale-up of digital transformations in health organisations and systems. This needs to be considered in future research and evaluations in the field.

PurposeArtificial intelligence (AI) raises many expectations regarding its ability to profoundly transform health care delivery. There is an abundant literature on the technical performance of AI applications in many clinical fields (e.g. radiology, ophthalmology). This article aims to bring forward the importance of studying organizational readiness to integrate AI into health care delivery.Design/methodology/approachThe reflection is based on our experience in digital health technologies, diffusion of innovations and healthcare organizations and systems. It provides insights into why and how organizational readiness should be carefully considered.FindingsAs an important step to ensure successful integration of AI and avoid unnecessary investments and costly failures, better consideration should be given to: (1) Needs and added-value assessment; (2) Workplace readiness: stakeholder acceptance and engagement; (3) Technology-organization alignment assessment and (4) Business plan: financing and investments. In summary, decision-makers and technology promoters should better address the complexity of AI and understand the systemic challenges raised by its implementation in healthcare organizations and systems.Originality/valueFew studies have focused on the organizational issues raised by the integration of AI into clinical routine. The current context is marked by a perplexing gap between the willingness of decision-makers and technology promoters to capitalize on AI applications to improve health care delivery and the reality on the ground, where it is difficult to initiate the changes needed to realize their full benefits while avoiding their negative impacts.

While the transition toward digitalized health care and service delivery challenges many publicly and privately funded health systems, patients are already producing a phenomenal amount of data on their health and lifestyle through their personal use of mobile technologies. To extract value from such user-generated data, a new insurance model is emerging called Pay-As-You-Live (PAYL). This model differs from other insurance models by offering to support clients in the management of their health in a more interactive yet directive manner. Despite significant promises for clients, there are critical issues that remain unaddressed, especially as PAYL models can significantly disrupt current collective insurance models and question the social contract in so-called universal and public health systems. In this paper, we discuss the following issues of concern: the quantification of health-related behavior, the burden of proof of compliance, client data privacy, and the potential threat to health insurance models based on risk mutualization. We explore how more responsible health insurance models in the digital health era could be developed, particularly by drawing from the Responsible Innovation in Health framework.