Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method

Health tourism, as a dynamic and rapidly growing sector of the tourism industry, plays a fundamental role in strengthening national economies, increasing international interactions and improving the quality of healthcare services. By integrating healthcare, wellness and recreational services, this field has become one of the key drivers for attracting foreign tourists. The emergence of artificial intelligence (AI) as a transformative technology offers unparalleled potential to optimize health tourism services. Using AI in trip planning, improving user experience and predicting the needs of health tourists has gained significant importance. This study aims to identify and rank AI-based criteria in health tourism. By reviewing and analysing previous studies, key criteria in health tourism influenced by AI were identified. Subsequently, these criteria were evaluated and ranked using Fuzzy SWARA method. The ranking results indicate that “healthcare service quality (C11)”, “competence and reputation of physicians (C12)”, “hospital equipment and facilities (C13)”, “political stability and security (C41)” and “access to medical information (C14)” were ranked first to fifth, respectively. These findings highlight the crucial role of AI in enhancing service quality and improving the experience of health tourists. The results of this study can be beneficial for policymakers and stakeholders in the health tourism sector for better planning and attracting more tourists.

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Publication Details

Journal
Computer and decision making.
Published
2025-03-30
DOI
https://doi.org/10.59543/comdem.v2i.13795
Citations
21
Primary Topic
E-commerce and Technology Innovations
Type
article
Field-Weighted Citation Impact
51.39
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article

Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method

Mahmoudreza Entezami, Sayeh Khamoushi, Danial Bazmandeh, Sadaf Raoufi et al.
21 citations
Computer and decision making.
E-commerce and Technology Innovations
51.39
article

Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method

Mahmoudreza Entezami, Sayeh Khamoushi, Danial Bazmandeh, Sadaf Raoufi, Sepideh Basirat
article en
21 citations

Abstract

Health tourism, as a dynamic and rapidly growing sector of the tourism industry, plays a fundamental role in strengthening national economies, increasing international interactions and improving the quality of healthcare services. By integrating healthcare, wellness and recreational services, this field has become one of the key drivers for attracting foreign tourists. The emergence of artificial intelligence (AI) as a transformative technology offers unparalleled potential to optimize health tourism services. Using AI in trip planning, improving user experience and predicting the needs of health tourists has gained significant importance. This study aims to identify and rank AI-based criteria in health tourism. By reviewing and analysing previous studies, key criteria in health tourism influenced by AI were identified. Subsequently, these criteria were evaluated and ranked using Fuzzy SWARA method. The ranking results indicate that “healthcare service quality (C11)”, “competence and reputation of physicians (C12)”, “hospital equipment and facilities (C13)”, “political stability and security (C41)” and “access to medical information (C14)” were ranked first to fifth, respectively. These findings highlight the crucial role of AI in enhancing service quality and improving the experience of health tourists. The results of this study can be beneficial for policymakers and stakeholders in the health tourism sector for better planning and attracting more tourists.

Computer and decision making.Vol. 2
University of Arizona (US), Maharishi International University (US), University of the Potomac (US), Concordia University (CA)
Openalex Percentile: Top 0%
E-commerce and Technology Innovations
51.39
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Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method — Mahmoudreza Entezami, Sayeh Khamoushi, et al. · Computer and decision making. (2025) | TGRS Research Map | TGRS