Ranking of AI-Based Criteria in Health Tourism Using Fuzzy SWARA Method
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".