What Makes Artificial Intelligence Exceptional in Health Technology Assessment?

Jean-Christophe Bélisle-Pipon,Isabelle Ganache,I Glenn Cohen,Mireille Goetghebeur,Marie-Christine Roy,Vincent Couture

doi:10.3389/frai.2021.736697

Abstract

The application of artificial intelligence (AI) may revolutionize the healthcare system, leading to enhance efficiency by automatizing routine tasks and decreasing health-related costs, broadening access to healthcare delivery, targeting more precisely patient needs, and assisting clinicians in their decision-making. For these benefits to materialize, governments and health authorities must regulate AI, and conduct appropriate health technology assessment (HTA). Many authors have highlighted that AI health technologies (AIHT) challenge traditional evaluation and regulatory processes. To inform and support HTA organizations and regulators in adapting their processes to AIHTs, we conducted a systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation. Our research question was: What makes artificial intelligence exceptional in HTA? The current body of literature appears to portray AIHTs as being exceptional to HTA. This exceptionalism is expressed along 5 dimensions: 1) AIHT’s distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA. Thus, AIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large. As AI implementation by governments and health organizations carries risks of generating new, and amplifying existing, challenges, there are strong arguments for taking into consideration the exceptional aspects of AIHTs, especially as their impacts on the healthcare system will be far greater than that of drugs and medical devices. As AIHTs begin to be increasingly introduced into the health care sector, there is a window of opportunity for HTA agencies and scholars to consider AIHTs’ exceptionalism and to work towards only deploying clinically, economically, socially acceptable AIHTs in the health care system.

Highlights

Health technology assessment (HTA) is key to the introduction of artificial intelligence (AI) applications in health
We focus on the theme “exceptionalism of AI in HTA”
The “exceptionalism” of AI health technologies (AIHT) can be broken down into five main aspects: 1) AIHT’s distinctive features; 2) their systemic impacts on health care and the health sector; 3) the increased expectations towards AI in health; 4) the new ethical, social and legal challenges that arise from deploying AI in the health sector; and 5) the new evaluative constraints that AI poses to HTA

Summary

Introduction

Health technology assessment (HTA) is key to the introduction of artificial intelligence (AI) applications in health. While regulatory assessment often is conducted by supranational (e.g., European Medicines Agency, EMA) and national (US FDA, Health Canada) regulators, HTA is mostly conducted at regional, provincial or state-based level and represents the main gateway for a health technology (e.g., drugs, vaccines, medical devices) to be widely administered to patients (Vreman et al, 2020; Wang et al, 2018). A health technology that is positively evaluated by a health regulator or an HTA agency signals significant support for its use, causing clinicians, patients, hospital administrators and third-party payers (such as public or private health insurers) to consider deploying and reimbursing this technology in their health care system or setting (Allen et al, 2017; Wild, Stricka, and Patera 2017). AI systems require to be trained on and use vast amounts of (potentially sensitive) data (about patients, research participants, clinicians, managers, health care systems, etc.) that raise issues of privacy, (cyber)security, informed consent, data stewardship and control over data usages (Wang and Preininger 2019; Dash et al, 2019; Sun and Medaglia 2019; Bartoletti 2019)

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