This study aims to investigate ethical consideration into the data quality management and biases for fairness in artificial intelligence (AI) algorithms. It looks at how biases in AI systems could make it difficult to maintain data quality control in medical travel and tourism.
The systematic review of the literature from January 2019 to March 2025 (72 out of 925 articles) considers a variety of keyword combinations used for data quality control in AI systems. The process of systematic literature review aids in identifying studies on data quality management for medical tourism and travel.
The results of the study show that data quality visibility should be increased, data rectification should be done consistently and data momentum should be managed. It indicates that audience behavior in medical travel and tourism is significantly impacted by AI-powered digital branding and marketing.
The research is limited to the single source of methodology, and it is limited in terms of articles collected from Jan 2019 to March 2025. It can be extended to the last decade for more insights and issues in AI use for medical travel and tourism.
Practical applications include efficient patient processing for international medical travel and tourism, as well as automated AI machine learning. Future research investigations should consistently segment data evaluation to overcome the limited regulatory compliance of data quality management.
Patients (medical tourism) are primary elements in healthy social settings that can be facilitated through AI’s efficient use and maintaining the quality of the data management.
This study aims to address the main ethical concerns about the quality of AI data, data use monitoring and ethical support for data decisions and implementations inside the AI framework. The usage of digital branding, mobile app development, digital audience behavior, digital marketing principles and artificial intelligence in marketing are some of the important concepts used to accomplish the research goals.
