What question did this study set out to answer?

To establish and analyze a bilingual human-in-the-loop evaluation framework assessing clinical accuracy, safety, and hallucination detection in Arabic and English medical large language models.

August 19, 2026Open Access

Human-in-the-loop Evaluation of medical large language models: A bilingual framework for clinical accuracy,saftey, hallucination detection , ARABIC-ENGLISH medical AI

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To establish and analyze a bilingual human-in-the-loop evaluation framework assessing clinical accuracy, safety, and hallucination detection in Arabic and English medical large language models.
Designed a bilingual assessment framework incorporating expert clinician oversight to evaluate large language model outputs across healthcare tasks.
Assessed model capabilities in medical question answering, clinical documentation, medical translation, patient education, and workflow support.
Identified that linguistic fluency in model outputs does not correlate reliably with clinical accuracy, completeness, or context-specific medical safety.
Demonstrated the necessity of active clinician-guided validation to detect hallucinations and prevent potential harm in bilingual healthcare AI deployments.

Implication

Evaluation framework demonstrates clinical accuracy and hallucination risks in bilingual medical AI models, highlighting the necessity of human oversight in healthcare workflows.

Abstract

Large Language Models (LLMs) are increasingly being applied to healthcare-related tasks, including medical question answering, clinical documentation, medical translation, patient education, information retrieval, and healthcare workflow support. Despite their impressive linguistic capabilities, fluent model outputs cannot be assumed to be clinically accurate, safe, complete, or appropriate for a specific medical context.

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Cite This Study

HASAN AHMED RASHAM (2026) studied this question.

synapsesocial.com/papers/6a8563c403308d306e2d7137 https://doi.org/10.5281/zenodo.21985883

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Implication

Evaluation framework demonstrates clinical accuracy and hallucination risks in bilingual medical AI models, highlighting the necessity of human oversight in healthcare workflows.

Human-in-the-loop Evaluation of medical large language models: A bilingual framework for clinical accuracy,saftey, hallucination detection , ARABIC-ENGLISH medical AI

Key Points

Implication

Abstract

Discussion

Cite This Study

Human-in-the-loop Evaluation of medical large language models: A bilingual framework for clinical accuracy,saftey, hallucination detection , ARABIC-ENGLISH medical AI

Key Points

Implication

Abstract

Discussion

Cite This Study

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