Enhanced healthcare using generative AI for disabled people in Saudi Arabia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it