AI-powered radiology report simplification in Arabic: A prospective evaluation of patient-perceived understandability...

Radiology reports are written for clinicians, leaving the majority of patients unable to understand their own imaging findings. Large language models (LLMs) offer a means to simplify reports for patient-facing use; however, most implementations rely on cloud-based platforms that raise data privacy and regulatory concerns. Arabic-speaking populations - over 400 million people worldwide - remain substantially underserved in medical AI research, with no prospectively evaluated AI Arabic radiology report simplification system previously reported.

This study aimed to conduct a preliminary prospective feasibility and safety evaluation of patient-perceived understandability outcomes and radiologist-assessed clinical safety of an on-site, institutionally governed AI system that generates simplified radiology reports in plain English and Arabic for Arabic-speaking outpatients.

In this prospective single-center observational study conducted at King Faisal Specialist Hospital and Research Centre - Jeddah (KFSHRC-J) in January 2026, 98 adult outpatients (mean age 50.0 ± 14.9 years; 50 men, 48 women; IRB #2251488) reviewed three versions of their own radiology report in randomized order: a traditional radiologist report, an AI-simplified English report, and an AI-simplified Arabic report. The system used a two-stage pipeline: Qwen3-14B-FP8 with structured prompt engineering for English simplification (Stage 1), and a fine-tuned Hala-1.2B model for Arabic translation (Stage 2; 8,319 training samples). Patient-perceived understandability was measured on a 5-point Likert scale (1 = Very Easy; 5 = Very Difficult). Three board-certified radiologists independently assessed clinical safety using a standardized seven-domain rubric; inter-rater agreement was quantified using ICC(2,k).

AI-simplified Arabic reports produced a median perceived understandability score of 1 (IQR 1-2) versus 5 (IQR 3-5) for traditional reports (p < 0.001; rank-biserial r = 0.97), with 94.9% (93/98) of patients preferring the Arabic simplified version. Safety evaluation demonstrated 96.9% (95/98) of reports safe for patient release, with a hallucination rate of 3.1% (n = 3; 1 unsafe, 2 safe by consensus). Inter-rater reliability was moderate by conventional thresholds (ICC 0.48-0.55) due to a ceiling effect from uniformly high safety scores (mean fidelity 4.8/5.0; 90.8% highest rating; SD = 0.3), with 91.2% absolute agreement on binary safety classification and Fleiss' kappa = 0.71 for safety.

In this preliminary prospective feasibility evaluation, a locally deployed, clinician- and AI team-built system significantly improved patient-perceived understandability of radiology reports for Arabic-speaking patients, with a radiologist-assessed safety profile that is promising but requires further validation before broad clinical deployment. The hallucination rate of 3.1% (1.0% unsafe) is lower than published pooled benchmarks, though direct comparisons are limited by methodological heterogeneity across studies (pooled 7.2% across 38 simplification studies; 1.12% for fine-tuned models). To our knowledge, this is among the first prospectively evaluated AI systems for Arabic radiology report simplification, highlighting the potential of on-site AI deployment as a safe, privacy-preserving approach for multilingual radiology communication, warranting further investigation of its contribution to health equity.