The last two decades have seen rapid growth in artificial intelligence (AI) initiatives in health care settings, driven by the promises of improved treatment, quality, safety, and efficiency [1]. AI systems are computer algorithms that are able to mimic human intelligence to perform tasks. They are potentially capable of improving clinical decision-making. However, there is currently a lack of high-quality evidence of effectiveness, and an overoptimism regarding AI-based technologies in health care [2,3]. Many existing algorithms and applications fail to scale and migrate across settings [4], potentially leading to missed benefits or compromised patient safety.

Evidence from other sectors, such as finance and retail, may have limited applicability given the particular social, economic, technical processes, and legal challenges of health and social care settings [5]. Across the digital economy, AI has been successfully applied to historical data, for example, in financial forecasting [6] or retail marketing, where personalized advertisements have transformed consumer behavior [7]. These methods are harder to deploy in the more complex and sensitive settings of health and social care [5]. This is largely because developers and implementers focus on tool development and do not sufficiently draw on existing work to inform the conception and design of technologies, their use and optimization, and organizational strategies to implement them.

Theory-informed approaches to evaluation can help to ensure that technologies are effectively validated, implemented, and adopted. They can also help to ensure that systems do not result in unintended negative consequences, such as inappropriate or suboptimal care, exacerbated inequities, or clinician burnout [8]. Theories seek to explain complex relationships at an abstract level and can help to integrate a particular implementation with the empirical evidence base. As such, theory-informed evaluation frameworks can enable learning from experience, thus guiding developers, implementers, and evaluators through development, implementation, and optimization [9]. Ideally, the real-world experience gathered during this process is then used also to inform the refinement of evaluation frameworks.

Despite significant investments, there are currently only a few examples of the use of AI-based systems in health care and most systems are only beginning to be rolled out and embedded [10-12]. This is in contrast to the finance and retail sectors, where processes and products are standardized. To date, most activity has focused on diagnostic image-based systems and text or language processing, while complex precision medicine efforts are in very early stages of development. We here call for the increasing use of theory-informed approaches to evaluation to help ensure that developed systems can be adopted, scaled, and sustained within settings of use, and are safe and effective. Until now, this has not been done consistently, which has resulted in limited learning and limited ability to transfer learning across settings, as well as limited clinical and patient reassurance. If done appropriately, the implications for clinical settings are significant, as validated new knowledge can be disseminated and shared. This, in turn, obviates the need to learn through experience that can be painful, dangerous, and costly.

Unfortunately, despite increasing attention in research, the current application of theory-informed strategy and evaluation in AI practice is relatively limited in both health care and other sectors [13]. This may be due to a lack of understanding surrounding the theoretical literature (ie, why theories are useful in practice and how they may be used by different stakeholders), and the immediate focus of developers on demonstrating that technology works. Politically and managerially, there may be a drive to show modernization processes rather than making clinical and organizational decisions based on evidence-based outcomes. Where theories have been applied, these have been driven by business approaches to value creation in organizations [14], or by approaches designed to influence consumer behavior [15]. In these contexts, they have been strategically used to help address a particular stakeholder need (eg, how to maximize value through implementing AI in organizations and how to get consumers to accept AI technology). In health care, the range of stakeholders and associated needs however varies significantly from other sectors. While the managers and policymakers may focus on value and efficiency, patients are likely to be concerned about avoidable illness, and practitioners may focus on workloads and potential liability.

It is therefore often difficult to know what needs (and consequently what theory) to focus on and in what context. For example, while developers of technology now increasingly draw on cocreation with users to promote the adoption of AI, these approaches may not consider organizational drivers, workflow integration, multiplicity of stakeholders, or ethical considerations in implementation, thereby limiting the scalability of emerging applications.

Theory-informed approaches to evaluation in health care must be considered within their specific context, recognizing their relative positions and identifying which needs they address at various stages of the technology lifecycle. We aim to begin this journey by providing a conceptual overview of existing theory-informed frameworks that could usefully inform the development and implementation of AI-based technologies in health care. Despite some differences in technological properties and performance between AI- and non–AI-based technologies (Table 1) [16], many existing frameworks are likely to be applicable.

We here provide a conceptual overview of existing frameworks, focusing on practical applications of examples of existing theory-informed frameworks and their potential application to AI-based technologies in health care [49]. Frameworks were selected as examples illustrating these extracted categories. This work is not intended to be exhaustive but to provide a pragmatic introduction to the topic for nonspecialists [50,51].

To categorize frameworks in a meaningful way, we focused on their potential area of application and the particular interest or focus of various stakeholder groups who may need to draw on existing experience to inform their current efforts to develop, implement, and optimize AI-based technologies in health care settings.

A range of theory-informed evaluation frameworks for diverse kinds of HIT already exist [62]. Although not all of these may be relevant for AI-based applications, many aspects of existing frameworks are likely to apply. Exploring the transferability of these dimensions, therefore, needs to be a central component of work going forward [63].

Existing frameworks examine various aspects of technology design, implementation, adoption, and optimization. On the most basic level, they can be distinguished according to their focus, which then influences their application and context of use. A simplified overview of selected HIT evaluation frameworks and their potential application to AI is shown in Table 2. Frameworks with a technology focus can help to inform the conception and design of technologies through actively and iteratively involving end users, bridging the gap between technology development and application. This can, in turn, mitigate risks around nonadoption due to a lack of need or actionable system outputs. Frameworks with a user focus can help to ensure that systems are effectively embedded with adoption contexts and thereby mitigate the risk of systems not being used or not being used as intended. Finally, frameworks with an organizational focus can help to ensure that systems fit with existing organizational structures, and thereby help to ensure sustained use over time and across contexts.

We recommend that researchers, implementers, and strategic decision makers consider the use of existing theory-informed HIT evaluation frameworks before embarking on an AI-related initiative. This can help to mitigate emerging risks and maximize the chances of successful implementation, adoption, and scaling. To achieve this, existing and emerging guidelines for the evaluation of AI must promote the use of theory-informed evaluation frameworks.

Although many of the frameworks are well-known in the academic clinical informatics community, there is an urgent need to incorporate them into general AI design, implementation, and evaluation activities, as they can help to facilitate learning from experience and ensure building on the existing empirical evidence base. Unfortunately, this is currently not routinely done, perhaps reflecting disciplinary silos leading to lessons having to be learned the hard way. This, in turn, potentially compromises the safety, quality, and sustainability of applications. For example, although AI applications in radiology are now getting more established, the existing evidence base focuses on demonstrating effectiveness in proof-of-concept or specific clinical settings (the technology dimension in Table 2) [25]. Wider organizational and user factors are somewhat neglected, potentially threatening the wider sustainability and acceptability of such applications.