Currently submitted to: Journal of Medical Internet Research
Date Submitted: Jan 11, 2020
Open Peer Review Period: Jan 11, 2020 - Mar 7, 2020
(currently open for review)
Artificial Intelligence and Health Technology Assessment: Anticipating a New Level of Complexity
Artificial Intelligence is seen as a strategic lever to improve access, quality and efficiency of care and services, and to build learning and value-based health systems. Many studies examined the technical performance of AI within an experimental context. These studies provide limited insights into the issues that its use in a real-world context of care and services (RWCCS) raises. To help decision-makers address these issues in a systemic and holistic way, this article relies on the "Health Technology Assessment (HTA) core model" to contrast the expectations of the health sector towards the use of AI with the risks that should be mitigated for its responsible deployment. The analysis adopts the perspective of payors because of their central role in regulating, financing and reimbursing novel technologies. This article suggests that AI-based systems should be seen as a health system transformation lever, rather than a discrete set of technological devices. Their use could bring significant changes and impacts at several levels: technological, clinical, human and cognitive (patient and clinician), professional and organizational, economic, legal and ethical. The assessment of the "AI value proposition" should thus go beyond "technical performance" and "price" logics by performing a holistic analysis of value in a RWCCS. In order to guide AI developments, generate knowledge and draw lessons that can be translated into action, the right political, regulatory, organizational, clinical and technological conditions for innovation should be created as a first step.
Request queued. Please wait while the file is being generated. It may take some time.
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.