Integrating AI, ML, and RPA for end-to-end digital transformation in healthcare

摘要

The amalgamation of Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) possesses significant potential for facilitating comprehensive digital transformation in healthcare. Nonetheless, disjointed initiatives, scaling issues, and restricted compatibility impede broad adoption. This article examines current frameworks and techniques, highlighting the cohesive integration of AI, ML, and RPA to optimize healthcare workflows, enhance real-time decision-making, and improve patient outcomes. The research emphasizes progress in predictive analytics and individualized treatment frameworks while examining RPA's function in automating repetitive procedures, including billing and patient data administration, to enhance operational efficiency and alleviate administrative constraints. A comparative study of existing research reveals differing levels of precision and accuracy in RPA implementations, with Ghulaxe Vivek (2024) attaining the highest performance metrics (94% accuracy, 85% precision), while other studies provide significant insights. Findings illustrate the need for integrated, scalable architectures that leverage the strengths of AI, ML, and RPA to facilitate digital transformation that is both sustainable and effective in health care.