THE ECONOMICS OF INTEGRATING ARTIFICIAL INTELLIGENCE WITH TRADITIONAL AND MODERN MEDICINE: OPPORTUNITIES, CHALLENGES, AND FUTURE HEALTHCARE IMPACTS – AN EMPIRICAL ASSESSMENT
This empirical assessment explores the transformative role of Artificial Intelligence (AI) in integrating traditional and modern healthcare systems to enhance diagnostics, treatment, and resource management. Valued at USD 15.1 billion in 2022, the global AI healthcare market is projected to reach USD 188 billion by 2030, with significant impacts observed in countries like India and the US, improving radiology accuracy, predictive analytics, and reducing costs. Econometric studies reveal that increased AI adoption correlates with reduced preventable mortality (by up to 0.45%) and improved patient satisfaction, alongside substantial economic benefits, including billions in savings and productivity gains. The integration of traditional knowledge systems, such as Ayurveda, with digital intelligence fosters inclusive growth and cultural resilience. Despite these opportunities, challenges persist in data privacy, algorithmic bias, regulatory delays, and financial constraints, especially in low-resource settings. Addressing these barriers through robust policies, ethical frameworks, and innovative models is essential for sustainable, equitable healthcare. Econometric models demonstrate that strategic AI implementation can optimize cost-effectiveness, improve health outcomes, and expand access, particularly in underserved regions. Ultimately, AI does not replace but augments traditional and modern medicine, driving an integrated, patient-centered, and sustainable healthcare ecosystem. Future models focusing on preventive analytics, workflow automation, and virtual care, supported by rigorous econometrics, will shape the next phase of AI-enabled medicine, ensuring equitable and environmentally responsible healthcare delivery worldwide. The study explores urgent and evolving challenges that are becoming more critical in today’s rapidly shifting and interconnected world, underscoring their relevance in the global context.
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-01-01
- DOI
- https://doi.org/10.5281/zenodo.16901720
- Primary Topic
- Water Quality and Resources Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00