Abstract

This paper explores the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing healthcare diagnostics and treatment plans, with a focus on radiology, mental health, and chronic disease management. AI's integration into radiology improves diagnostic accuracy by enabling the detection of subtle anomalies in medical images, thus facilitating early disease detection and minimizing human error. In mental health, AI-driven predictive modeling allows for the early identification of at-risk individuals, enabling timely interventions and personalized treatment plans that improve patient outcomes. The paper also examines AI’s impact on chronic disease management, highlighting its use in predictive analytics to forecast disease progression and develop individualized treatment strategies, optimizing patient care. The scalability of AI solutions in resource-limited settings is also explored, demonstrating how AI can bridge healthcare gaps in underserved regions by automating processes and supporting healthcare workers with decision-making tools. Finally, the paper suggests future research directions, including the integration of AI with emerging technologies like blockchain and the Internet of Things (IoT) to further optimize healthcare delivery, especially in low-resource environments.