AI can entrench and disseminate practice models specific to particular contexts (organizations or health systems) and not necessarily accepted or used in others (tropism) [38]. For example, clinicians in some countries stopped using IBM Watson for Oncology because it reflected US specificity in cancer treatment [1,47].

To use AI in their decision making, clinicians should understand how it makes decisions in the first place [38,45,48]. They need the evidence to support a given conclusion to be able to carry out the necessary verifications or even corrections [14]: Why this decision (what information or image—or part of the image—tipped the final decision of the AI)? Why not another option (or choice)? When may I consider that the decision is correct? When should I accept this decision? How can I correct the error when it occurs?

AI should provide clinically added value for the patient. In a real-world context of care and services, much information, decisions, and diagnoses could intersect (eg, symptom assessment, laboratory tests, and radiology). At present, it is difficult to distinguish the effect of an AI-based decision from the overall preventive and/or therapeutic strategy of patient care [49,50].

Another clinical issue is determining the level of accuracy of AI for diagnosis and recommendations. In practice, decisions physicians make could diverge or even contradict each other in many situations. The gold standard is not always easy to define in a process that involves complex judgments [38,51,52]. In this case, should the standard reflect that of the lead clinician (or clinicians) in the organization? Or the one accepted by the majority of clinicians? Or the one reported in similar contexts? Some authors believe that for technologies that aim to provide pragmatic solutions under suboptimal conditions, AI performance should correspond to clinically acceptable practice in a given context and not necessarily to recommended practices [32]. This last point is likely to be problematic, particularly in a context where health systems are trying to overcome the challenge of practice variations to be able to provide equitable and quality services for all citizens.

AI could affect the nature and quality of the clinician-patient relationship and their expectations for care and follow-up [53,54]. The loss of human contact could lead to increased isolation of some people (replacement of health care providers) [1]. Some patients may feel able to control and manage their disease, with passive surveillance and/or less contact with the clinician, whereas others may feel overwhelmed by additional responsibilities [55]. AI may also create unrealistic expectations in some patients regarding clinical outcomes, which could have a negative impact on their care and service experience [56]. In addition, some AI-based decisions could be perceived as a restriction on the patient's right to make a free and informed decision [1,53]. Cultural and social aspects could play an important role in how patients will respond to AI and therefore how effective it can prove in practice [57]. Hence, it is important to know on which basis one may define the target population that can benefit from it [58]. In this regard, the question of social acceptability (acceptable risk and public confidence) also needs to be considered, which goes beyond the simple question of the effectiveness and usability of AI [59].

For clinicians, the challenge is to integrate AI into the EHR and clinical routine with minimal effort while respecting their decision-making autonomy [24]. Nonintuitive technologies could encumber workflows and become a burden for clinicians without improving service delivery [30,60]. Otherwise, the ability of AI to combine data from the scientific literature with learning from practice data could generate a repository of clinical practices (clinical mind), which could give AI an unwanted power or authority [35]. In some situations, AI may reduce the clinician's ability to take into account patient values and preferences. In contrast, some clinicians may develop absolute confidence and become dependent on AI, thus relinquishing their responsibility to verify or double-check its decisions [1].

In short, if clinicians feel overloaded and workflows become more complex, AI may be rejected because of self-perceived inefficacy and performance, alert fatigue, cognitive overload, and disruption of interpersonal communication routines [54,61-63].

Global appreciation of the added value of AI should take into account the nature and magnitude of the professional and organizational changes required for its use [6]. For example, the FDA has approved an AI application used for diabetic retinopathy screening, which may be used in primary care clinics [11]. As in some countries, the screening procedure is performed by an ophthalmologist (specialist), some questions arise: How will this technology fit into patient care and services trajectory? How will it be integrated into the clinical-administrative processes of organizations and the health system? If used at the primary care level, will general practitioners, nurses, or optometrists be allowed to supervise the AI? If so, under what conditions? What will be the impact on professional jurisdictions (regulated activities, remuneration, and training)? What changes will result in terms of service organization and clinical-administrative workflows (waiting time at primary care level, primary care, and specialized services relationships)?

Thus, AI could lead to a redistribution of work between different professional scopes of practice and highlight the need for other clinical, administrative, and technical skills and expertise. This will require clarifying new rules and processes (clinical and administrative), negotiating and reframing professional jurisdictions, responsibilities, and privileges associated with them and reassessing the number of positions needed and the new skills required to work (with) and/or perform other tasks that accompany its use. This will have to take into consideration how new roles in terms of skills in informatics and data science and the ability to liaise may be introduced within clinical teams [64].

Finally, today, most AI applications are developed to perform a single task or a set of very specific tasks (eg, diagnosing only diabetic retinopathy and macular edema) [65]. They are unusable for other diagnoses for which they are not trained (eg, nondiabetic retinopathy lesions and eye melanoma) and are unable, at least for the moment, to replace a complete clinical examination [66]. Payers will thus be tasked to determine whether AI provides sufficient added value in relation to the nature and magnitude of the clinical, cognitive, professional, and organizational changes it could generate.

To adapt an AI to a local environment, considerable investments and expenditures may be necessary. The evolution of AI in a real-world context of care and services, by integrating large amounts of data of various types and sources, requires additional resources to ensure its proper functioning and stability: continuous performance tests, software and data quality tests, infrastructure and equipment upgrades, human expertise, and training [3,67]. However, many health organizations do not have a secure and scalable technological and data infrastructure as well as adequate human resources to ensure proper collection of the data necessary for the training and adaptation of AI to their local population and clinical environment [17]. The literature on AI’s promises as well as innovation policies that support its development downplays the capital-intensive requirements that are required to properly deploy AI, compared with the day-to-day work of managers in organizations.

In health systems where activity-based financing is the basis for funding health organizations, some clinicians tend to enter the highest paying codes for each clinical activity (ie, the most complex case of an intervention) to increase performance and maximize revenue. An AI application trained on data from these organizations (EHR with invoicing or reimbursement data) could amplify biases inherent in such practices that do not necessarily reflect the actual clinical condition [23,44,68]. The replication and entrenchment at a large scale of these biases could result in significant costs for patients, clinicians, organizations, and the health systems [35].

Similarly, some AI applications may be too cautious, resulting in an increase in requests for unnecessary testing and treatment, leading to overdiagnosis or overprescription [69]. Their recommendations, which are not necessarily associated with improved patient outcomes, could lead to increased costs and expenses for patients and the health system.

Many AI technologies are still considered today as decision-making support tools for clinicians. It could then be argued that the legal responsibility for the decision still rests with the clinician. However, with the growing performance of AI, clinicians may be increasingly influenced and may more easily accept AI decisions, even when there is clinical ambiguity. Determining the clinician's degree of responsibility becomes more complex [30]. The challenge here is to distinguish between several situations: When is it considered a decision-making support tool? When is it considered a decision-making tool? This distinction is key in defining who is legally responsible in the event of an error or a malfunction (professional misconduct) [30,51,70].

For example, if the clinical decision is based on an erroneous clinical recommendation from the AI (delayed or erroneous treatment), who will be held responsible? Is it the technology developer, technology provider, clinician, organization, or do they all share responsibility (and how)? In some jurisdictions, to confirm professional misconduct, it is necessary to prove that the standard of care was not followed. This standard is blurred when AI comes into play [2]. In addition, the likely consequence if the clinician does not comply with the recommendations of an AI and if this leads to an error must be anticipated [2]. It could be argued that the responsibility should rest with the human controller of AI, but such a responsibility becomes difficult to clarify when autonomous technologies are used [57]. In this regard, standards may shift over time: “What happens if medical practice reaches a point where AI becomes part of the standard of care?” Medical insurers and regulators will have to be able to distinguish errors inherent in the tool from those resulting from misuse by the clinician, the organization, or even the patient, an issue exacerbated by the black box of AI [51,71].

To generate a complete picture of the patient, AI will need access to data from different organizations (hospitals and insurers) [45]. The risk of disclosing sensitive information about patients or certain populations is real [45]. For example, some AI applications can reidentify an individual from only three different data sources [25,38,72]. In the same vein, the issue of consent is becoming more complex, as patients will be asked to authorize the use of increasingly large and diversified amounts of data about them: medical records, audio, videos, and socioeconomic data [58]. Problems could arise if the patient only consents to sharing parts of his or her data. Usually, confidentiality means that the clinician can withhold certain information—at the patient's request (or not)—and avoid entering it into the EHR. Incomplete data make AI less efficient and does not allow patients to benefit from the best possible services. AI may not be fully operational in a real-world context of care and services if specific restrictions on data access and use are applied [38].

Protection and confidentiality requirements imply the obligation to know several things: the origin of the data, how consent was obtained, and authorization to use and/or reuse the data for training and in a real-world context of care and services. As the data may come from different sources and contexts, different conditions and precautions will need to be considered [73]. Regulators will need to determine who owns the data and, in the context of public-private partnerships, who is responsible for its collection, use, transmission to third parties, and under what conditions [17]. As the answers will vary according to the nature of the data, the jurisdictions, and the purpose of use, the task at hand is sizable [73]. Finally, payers will have to recognize that the ethical implications of AI affect, directly or indirectly, all the other dimensions discussed earlier.

The purpose of this viewpoint paper is to provide a structured roadmap of the issues surrounding the integration of AI into health care organizations and systems. To the best of our knowledge, this is one of the few papers that offers a multidimensional and holistic analysis on the subject [7]. It contributes to current knowledge by providing a necessary basis for reflections, exchanges, and knowledge sharing among the various stakeholders concerned with AI in health care.

In light of the issues we identified, it becomes clear that regulatory and decision-making organizations as well as HTA agencies are facing unprecedented complexity: evaluating and approving so-called disruptive technologies, especially AI, requires taking several issues into consideration altogether. Many studies have reported significant technical performance of AI technologies, but very few have adopted a holistic standpoint that can situate their impacts and associated changes and transformations in health systems. Technical studies are rarely adapted to the complexity surrounding AI applications, as they overlook the context-dependent changes or adjustments the implementation and use of technology requires (variations, clinical and organizational interactions, and interdependencies) [74]. According to the frame problem [62,75], which highlights the difficulty for AI, beyond the specific tasks it masters, to update its set of axioms to capture the context in which it is implemented and used (eg, patient preferences, environment and social support, clinical history, personality/cultural characteristics and values that influence clinical outcomes, and empathy in medicine), the complexity inherent in the use of AI applications in the real-world context of care and services may seem difficult to overcome [62].

For informed decision making, there is a real need for evaluations that address AI as a lever of health system transformation. Given the magnitude of the implications it could have at all levels, the evaluation of AI’s value proposition should go beyond its technical performance and cost logic to incorporate its global value based on a holistic analysis in a real-world context of care and services. In this vein, technology brings value when its use in a real-world context of care and services contributes to the aims of the health system and aligns with the values of society. Global value appreciation could be based on the quintuple aim: (1) better quality and experience of care and services for patients; (2) a better state of health and well-being for the entire population; (3) reducing costs for responsible and sustainable resource management; (4) a better quality of work and satisfaction of health care providers; and (5) equity and inclusion to avoid exacerbating health disparities in the population [76]. From this perspective, further research on the evaluation of AI should no longer be limited to a technological approach, which only demonstrates quality from an engineering point of view and costs—motivated mainly by a logic of short-term savings—but should broaden its horizons to include the dimensions this paper underscored [77,78].

Real-world evaluations could be a major asset in informing AI decision making. In the context of uncertainty, iterative and reflective evaluation approaches should be developed to encourage dialog and collaboration among all relevant stakeholders (eg, payers, health care providers, technology providers, regulators, citizens/patients, academic researchers, and evaluation agencies) [63,78,79]. In addition, an early dialog between these stakeholders is needed to identify the evidence required to inform decision making [63,78]. This approach would also help AI providers to better understand the expectations of the health system [78]. This change implies that HTA should play an active role as a mediator and facilitator of transparent dialog between different stakeholders who are implicated throughout the technology’s life cycle [78,80].

Decision making for innovative technologies is inherently complex, in particular because of visions, perceptions, and objectives that may differ between the stakeholders involved: risk sharing is essential to strive to find a balance between uncertainty and added value [81]. In this regard, “major radical innovations never bring new technologies into the world in a fully developed form” but “appear in a crude and embryonic state with only a few specific uses” [81]. It is their use in a real-world context of care and services, through a process of learning by doing (improving users’ skills) and learning by using (improving users’ knowledge), which makes it possible to appreciate their global value [81]. With the complexity associated with AI, value appreciation becomes even more complex, challenging the traditional methodological foundations that are the basis for decision making about innovative technologies [82]. This also presents a unique opportunity for HTA to evolve and adapt (evaluative framework and contextualized data), particularly in view of the importance of contexts in the appreciation of the value of innovative technologies [83,84]. It is necessary for HTA scholars and practitioners to explore and exploit other avenues, complementary to traditional methods, to collect data and information that can better inform AI-related decisions [85].

Finally, this new context implies mechanisms for continuous collective learning and sharing of lessons. To do so, there is a need for learning and flexible health organizations and systems that are able to adjust and operate under uncertainty. In this regard, creating the political, regulatory, organizational, clinical, and technological conditions necessary for proper innovation is the first step. This requires building trust to ensure stakeholder engagement to guide AI developments, rapidly generate knowledge in a real-world context of care and services, and draw lessons to translate them into action.