119 posters, 6 topics, 524 authors, 243 institutions

RCR Global AI Conference 2026

29-30 June, 2026 | QEII Centre, Westminster

Authors

Showing 6 results for author "Ebinesh Arulnathan".

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Thumbnail for <p>Augmenting Emergency Imaging Decisions: Performance of a Custom Large-Language-Model Decision Support System for SHOs in Time-critical Medical Emergencies</p>

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<p>Augmenting Emergency Imaging Decisions: Performance of a Custom Large-Language-Model Decision Support System for SHOs in Time-critical Medical Emergencies</p>

Ebinesh Arulnathan, Praislin Gideon, Shirou Masoodi, Swetha Tula, Matthew Kueh, Rayya Naffa', Sivakumar Manickam , Mohammad Shoaib

East & North Hertfordshire, Mid & South Essex NHS trust , Mid and South Essex NHS Foundation Trust, Alder Hey Children's NHS Foundation Trust , Royal Free London NHS Foundation Trust, University Hospital Southa...

Poster Presenter: Ebinesh Arulnathan

Thumbnail for <p>AI- assisted question generation for FRCR Part 1 Physics: Evaluating the role of Multi model LLMs in High-stake Radiology Assessment Design</p>

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<p>AI- assisted question generation for FRCR Part 1 Physics: Evaluating the role of Multi model LLMs in High-stake Radiology Assessment Design</p>

Ebinesh Arulnathan, Shirou Masoodi, Zobia Hussain, Rayya Naffa', Srikrishna Harave, Sivakumar Manickam

RCR Global AI Conference 2026

Authors

<p>Augmenting Emergency Imaging Decisions: Performance of a Custom Large-Language-Model Decision Support System for SHOs in Time-critical Medical Emergencies</p>

<p>AI- assisted question generation for FRCR Part 1 Physics: Evaluating the role of Multi model LLMs in High-stake Radiology Assessment Design</p>

<p>Building the AI-Ready Radiologist: Curriculum Design, Challenges and Opportunities</p>

<p>Elevating the UK as the Global Leader in Paediatric Radiology AI: A National Framework to Reduce Backlogs, Standardise Quality and Transform Children’s Imaging Services.</p>

<p>Integrating a LLM-Based Tool into Emergency CT Workflow: Enhancing Trainee Vetting and Protocolling in the NHS</p>

<p>Training Minds for Machine Age Imaging: Medical Students’ Needs, Ethics, and Expectations for Safe AI Integration in Radiology</p>