AI-Driven Integrated Mental Health and Fitness Systems: A Comprehensive Review

Abstract

Recent progress in Artificial Intelligence (AI) has caused a big change in digital healthcare, especially in the areas of mental health support, fitness tracking, personalized nutrition, and clinical decision support. The rise in mental disorders, diseases linked to lifestyle, and limited access to professional healthcare services has sped up the use of AI-powered solutions like conversational chatbots, machine learning (ML) models, large language models (LLMs), and retrieval-augmented generation (RAG) systemsThis review provides an in-depth synthesis of the latest research on AI-driven systems that integrate mental health assessment, fitness prediction, personalized exercise and diet recommendations, and medical document understanding [1]–[5], with a focus on plagiarism-safe reporting. The work rigorously analyzes system architectures, algorithms, datasets, and evaluation metrics employed in recent studies. The comparative analysis brings into focus strengths, limitations, and performance trends, while ethical, privacy, and deployment considerations are explored. The review concludes by pointing out open issues in research and defining future directions to take in developing trustworthy human-centered intelligent healthcare platforms.