AI-Powered Transformation in Healthcare: Innovations, Implementation Challenges, and Future Perspectives

Artificial Intelligence (AI) is revolutionizing the healthcare industry by enhancing diagnostic accuracy, personalizing treatment plans, and improving patient outcomes. AI-driven applications, such as deep learning-based medical imaging, predictive analytics, and natural language processing for clinical documentation, are transforming traditional healthcare workflows. However, the integration of AI in healthcare is fraught with challenges, including data privacy concerns, algorithmic bias, regulatory constraints, and the interpretability of AI-driven decisions. Ensuring transparency and fairness in AI models remains a key hurdle in their widespread adoption. This paper aims to provide an in-depth analysis of AI applications in healthcare, explore emerging trends such as GPT-based models for medical documentation and federated learning for privacy-preserving AI, and discusses the ethical and technical challenges that must be addressed to harness AI’s full potential. We conclude with recommendations for future research directions, including improving AI explainability, developing robust regulatory frameworks, and enhancing dataset diversity to mitigate bias.