Enhanced healthcare using generative AI for disabled people in Saudi Arabia

Abstract

Saudi Arabia’s Vision 2030 prioritizes advances in healthcare to improve accessibility, improve medical services, and support people with disabilities. Despite the adoption of telemedicine and AI-driven healthcare solutions, disabled and elderly people continue to face challenges in accessing real-time medical services, receiving accurate diagnoses and independently navigate healthcare facilities. Current healthcare systems often struggle with delays, lack of personalization, and inefficiencies in medical data processing, limiting their effectiveness in providing inclusive and responsive healthcare. To address these challenges, this paper proposes an AI-powered healthcare framework that integrates Generative Artificial Intelligence (GAI), Reinforcement Learning from Human Feedback (RLHF), and the Analytic Network Process (ANP). RLHF enables AI models to learn and adapt based on real-time user feedback, ensuring a personalized and interactive healthcare experience. Meanwhile, ANP optimizes decision-making processes, allowing for faster, more accurate medical service delivery by considering multiple healthcare factors. This combined approach improves remote consultations, intelligent diagnostics, and seamless real-time interactions, significantly improving accessibility to healthcare for disabled individuals. The proposed framework is evaluated against existing AI-driven healthcare models. Results demonstrate that the system outperforms traditional methods, providing a faster, more reliable, and patient-centered healthcare experience. By combining GAI, RLHF, and ANP, this research offers a practical solution to improve healthcare accessibility for disabled individuals, aligning with the goals of Saudi Arabia’s Vision 2030.