Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries
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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.
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A recent literature review identified technological support and a lack of experts in AI in medicine as the main barriers to the integration of AI into medical education, as reported by students from Central and Eastern European countries.
“Similarly, a recent literature review identified technological support and a lack of experts in AI in medicine as the main barriers to the integration of AI into medical education, as reported by students from Central and Eastern European countries [38].”
From Shaping the future: perspectives on the Integration of Artificial Intelligence in health profession education: a multi-country survey (2024) · cites this paper as [38]
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“Barriers could be then addressed and classified as data, methodologi- cal, technological, regulatory-, policy, and human-related factors [38].”
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- Authors
- Tachkov, K., Zemplenyi, A., Kamusheva, M., Dimitrova, M., Siirtola, P., Pontén, J., Nemeth, B., Kalo, Z., & Petrova, G.
- Affiliations
- Medical University of Sofia (BG)
- Syreon Research Institute (Hungary) (HU)
- University of Oulu (FI)
- Corresponding author
- Petrova, Guenka
- ORCID
- Venue
- Frontiers in Public Health
- Publisher
- Frontiers Media SA
- Published
- 2022-07-14
- Volume
- 10
- Pages
- 921226-921226
- ISSN
- 2296-2565 (ISSN-L)
- PMC
- PMC9330148
- OpenAlex
- W4285386115
- Cited by
- 49
- References
- 55
- Citations by year
- 2026: 7 · 2025: 16 · 2024: 13 · 2023: 13
- FWCI
- 3.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Public Health (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (825162)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, health technology assessment, AI adoption barriers, Central and Eastern Europe, health policy, regulatory challenges, data quality, AI decision support
- MeSH terms
- Artificial Intelligence, Child, Humans, Technology Assessment, Biomedical
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