ENHANCING PATIENT CARE THROUGH DIGITAL HEALTH INNOVATION

The Covid-19 pandemic highlighted the urgent need for intelligent healthcare systems due to the shortage of doctors, nurses, and facilities.Smart healthcare using AI/ML, edge/fog/cloud computing, and interactive systems offers promising solutions.This paper proposes an Intelligent Human-Machine Interactive Healthcare System focused on Speech Recognition to detect physical and psychological discomfort.Speech, being the most natural communication mode, is processed using the Hidden Markov Model for realistic prediction.Challenges, ongoing projects, and future directions are also discussed.Artificial Intelligence (AI) has the potential to revolutionize healthcare by improving patient care, disease management, and reducing costs.Applications include predictive analytics, medical image analysis, drug discovery, clinical decision-making, and AI chatbots as virtual assistants.This research also examines challenges such as ethical concerns, data privacy, and algorithmic bias.AI integration can enhance efficiency in healthcare delivery.Ultimately, AI can transform the future of healthcare with improved outcomes.Recent advancements in mobile health devices support monitoring of mental health symptoms like stress.This work proposes an ML-based system that detects physiological stress signals using sensors and predictive models.Machine learning techniques enhance stress detection accuracy despite computational constraints.Simplified algorithms like decision trees provide lightweight and effective solutions.The system achieves high accuracy (96.6%) in identifying stress and supports proactive strategies for mental health management.