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Related Concept Videos

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Articles linked to this work by shared authors, journal, and citation graph.

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JADE-Plus: A Multimodal Agentic Retrieval-Augmented Generation Large Language Framework for Diagnostic Support in Jawbone Lesions: Development and Technical Validation Study.

Journal of imaging informatics in medicine·2026
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Impact of AI-assisted decision support on radiological diagnosis of jawbone lesions.

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Related Experiment Video

Updated: Mar 25, 2026

Systematic Assessment of Mammalian Skull Specimens for Dental and Temporomandibular Joint Pathology
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JADE: jawbone lesion diagnosis and decision supporting system.

Soroush Baseri Saadi1,2, Jonas Ver Berne1,3, Rocharles Cavalcante Fontenele1,4

  • 1OMFS-IMPATH Research Group, Department of Imaging and Pathology, Catholic University Leuven, Leuven 3000, Belgium.

Dento Maxillo Facial Radiology
|March 23, 2026
PubMed
Summary

Retrieval-augmented generation (RAG) systems like JADE enhance diagnostic accuracy for jawbone lesions compared to standalone large language models (LLMs). This study demonstrates RAG

Keywords:
cloud-based applicationsdifferential diagnosishybrid retrievaljawbone lesionslarge language modelsretrieval-augmented generation

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Area of Science:

  • Dentomaxillofacial Radiology
  • Artificial Intelligence in Medicine
  • Diagnostic Assistance Systems

Background:

  • Large language models (LLMs) show potential in medical diagnostics but require enhanced reasoning capabilities.
  • Jawbone lesion assessment presents complex diagnostic challenges.
  • Current diagnostic systems may lack the adaptability and comprehensive reasoning of advanced AI.

Purpose of the Study:

  • To develop and evaluate JADE, a novel retrieval-augmented generation (RAG) system for diagnostic assistance in jawbone lesion assessment.
  • To compare the diagnostic accuracy and stability of JADE against standalone LLMs and a supervised learning system (ORAD).
  • To assess the impact of RAG on LLM performance in a specialized radiological domain.

Main Methods:

  • JADE was built as a cloud-based RAG system, integrating an oral radiology database with LLM backbones.
  • Structured clinical data was used for hybrid semantic and keyword-based retrieval to augment LLM prompts.
  • Performance was evaluated on 25 validation cases, comparing RAG-LLMs (GPT-5, Claude Sonnet 4.5, DeepSeek-R1, Gemini 2.5 Flash) against their standalone versions and ORAD.
  • Diagnostic accuracy was analyzed using Cochran's Q test with post-hoc McNemar's tests and Bonferroni correction.

Main Results:

  • RAG-enhanced GPT-5 achieved the highest diagnostic accuracy (20/25), outperforming standalone LLMs (9-13/25) and ORAD (17/25).
  • Significant improvement was noted for GPT-5 when integrated with RAG (p=0.002).
  • RAG configurations demonstrated superior intra-model stability compared to standalone LLMs, with RAG-GPT-5 achieving 0.90±0.11 stability.

Conclusions:

  • JADE, a RAG system, significantly improved diagnostic accuracy and stability for jawbone lesion assessment compared to standalone LLMs.
  • This study marks the first application of RAG in dentomaxillofacial radiology, highlighting its value for enhancing AI diagnostic capabilities.
  • RAG integration shows promise for advancing AI-assisted diagnostic tools in specialized medical fields.