the European Union’s Horizon 2020 research and innovation programme(825162)
Szakterületek:
Mesterséges intelligencia és döntéstámogatás
The aim of this paper is to identify the barriers that are specifically relevant to
the use of Artificial Intelligence (AI)-based evidence in Central and Eastern European
(CEE) Health Technology Assessment (HTA) systems. The study relied on two main parallel
sources to identify barriers to use AI methodologies in HTA in CEE, including a scoping
literature review and iterative focus group meetings with HTx team members. Most of
the other selected articles discussed AI from a clinical perspective (n = 25), and
the rest are from regulatory perspective (n = 13), and transfer of knowledge point
of view (n = 3). Clinical areas studied are quite diverse—from pediatric, diabetes,
diagnostic radiology, gynecology, oncology, surgery, psychiatry, cardiology, infection
diseases, and oncology. Out of all 38 articles, 25 (66%) describe the AI method and
the rest are more focused on the utilization barriers of different health care services
and programs. The potential barriers could be classified as data related, methodological,
technological, regulatory and policy related, and human factor related. Some of the
barriers are quite similar, especially concerning the technologies. Studies focusing
on the AI usage for HTA decision making are scarce. AI and augmented decision making
tools are a novel science, and we are in the process of adapting it to existing needs.
HTA as a process requires multiple steps, multiple evaluations which rely on heterogenous
data. Therefore, the observed range of barriers come as a no surprise, and experts
in the field need to give their opinion on the most important barriers in order to
develop recommendations to overcome them and to disseminate the practical application
of these tools.
