Enhanced healthcare using generative AI for disabled people in Saudi Arabia
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".