Artificial Intelligence in Healthcare : Clinical Applications and Economic Implications

Abstract

Artificial intelligence (AI) has gradually shifted from theoretical research in the mid-20th century to practical tools that now influence both clinical decisions and economic outcomes. This paper follows the development of AI applications in healthcare and considers their economic significance at different stages. Early diagnostic systems such as INTERNIST-1 and MYCIN provided initial proof of concept, while computer-aided detection (CAD) in the 2000s highlighted the potential role of AI in medical imaging. Although early CAD tools were limited by high false-positive rates and integration problems, progress in deep learning over the past decade has improved diagnostic accuracy, reduced unnecessary procedures, and lowered costs. Case studies, for example Google Health’ s breast cancer screening system, suggest meaningful reductions in misdiagnosis, improved workflow, and economic benefits downstream. Broader analyses in Europe also indicate that AI applications can generate annual savings in the range of €170–212 billion through better workforce efficiency, optimized resources, and improved outcomes. Despite these encouraging results, concerns remain about privacy, fairness, and implementation costs. Overall, current evidence points to AI as a potentially transformative force in healthcare, not only clinically but also economically, with the capacity to support long-term system sustainability.