The Socioeconomic Impact of Artificial Intelligence Applications in Diagnostic Medical Thermography: A Comparative Analysis with Mammography in Breast Cancer Detection and Other Diseases Early Detection

Abstract

This study aims to assess the socioeconomic impact of artificial intelligence (AI) applications in diagnostic medical thermography, specifically focusing on breast cancer detection and comparing it with mammography. The research also considers ethical considerations relevant to the implementation of AI in medical imaging. Methods: A literature review analyzed AI’s socioeconomic implications in thermography and mammography. Key indicators were examined, and a theoretical model was proposed to assess the impact of independent, mediating, and dependent variables. Results: The analysis of existing studies revealed both potential benefits and ethical challenges associated with AI-supported thermography in breast cancer detection. The developed theoretical model provided a comprehensive framework for understanding the complex interactions between different components and the identified socioeconomic indicators. However, notable research gaps were identified, including the need for direct comparative studies between thermography and mammography and a limited focus on other diseases beyond breast cancer. Implications: Findings indicates AI-supported thermography can improve early breast cancer detection and reduce healthcare costs. Integrating thermography with AI enables personalized diagnosis and enhances healthcare access equity. Further empirical evidence is needed to validate outcomes and address research gaps. Recommendations include increased research investment, method integration, clear guidelines, regulations, and continuous socioeconomic impact evaluation. Conclusion: This study analyzes AI’s socioeconomic impact in diagnostic medical thermography, focusing on breast cancer detection. The theoretical model provides valuable insights into component relationships and their influence on socioeconomic indicators. The findings highlight potential benefits and ethical considerations, such as reducing 10–20% of breast cancer costs. Future research should validate the model, address gaps, and explore AI’s impact on thermography for other diseases. These findings inform decision-making and policy development, ensuring ethical and effective AI implementation in medical imaging, ultimately benefiting healthcare systems and patients.