Artificial intelligence (AI) is reshaping modern healthcare by enhancing diagnostic precision, optimizing clinical workflows, and supporting personalized treatment decisions. As healthcare systems continue to face rising demand, resource constraints, and disparities in care, AI offers scalable solutions across clinical, administrative, and research domains. This paper examines the dual potential of AI to improve health outcomes or, if poorly implemented, reinforce existing inequities. Building on general healthcare applications, the paper emphasizes the growing use of AI in mental health—a field historically limited by stigma, resource shortages, and accessibility barriers. A case study on AI-based depression care illustrates how digital tools can analyze behavioral data and deliver real-time, individualized support. The study also highlights critical issues such as data sensitivity, algorithmic accuracy, and consent. Finally, the paper outlines future directions for responsible AI integration, stressing the need for inclusive design, interdisciplinary collaboration, and ethical safeguards to ensure trustworthy and equitable use of AI in healthcare.