119 posters, 6 topics, 524 authors, 243 institutions

RCR Global AI Conference 2026

29-30 June, 2026 | QEII Centre, Westminster

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Showing 119 results.

Thumbnail for Variation in performance of different versions of a commercial machine learning-based tool for the longitudinal analysis of multiple sclerosis plaques on brain MRI

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Variation in performance of different versions of a commercial machine learning-based tool for the longitudinal analysis of multiple sclerosis plaques on brain MRI

John Chen, Shinnosuke Kitaoka, Samia Nesar, Mateus Esmeraldo, Fulvio Zaccagna, Tilak Das

Mid and South Essex NHS Foundation Trust, Cambridge University Hospitals NHS Foundation Trust, Cambridge University Hospitals NHS Foundation Trust

Poster Presenter: Tilak Das

$Thumbnail for <p>Embedding fracture-detection AI into diabetic foot pathways: podiatrist-supported workflow optimisation and real-world model evolution in NHS</p>$

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<p>Embedding fracture-detection AI into diabetic foot pathways: podiatrist-supported workflow optimisation and real-world model evolution in NHS</p>

Sarojini David, Aarthi Surendran, Victoria Whitehead, Helen Hughes, Gian-marco Strappelli , Prashanth Vas

Guy's and St Thomas' NHS Foundation Trust, University Hospital Lewisham, Lewisham and Greenwich NHS Trust

RCR Global AI Conference 2026

Posters by Code

Variation in performance of different versions of a commercial machine learning-based tool for the longitudinal analysis of multiple sclerosis plaques on brain MRI

<p>Embedding fracture-detection AI into diabetic foot pathways: podiatrist-supported workflow optimisation and real-world model evolution in NHS</p>

<p>Hidden Costs: Will Clinical AI in the NHS Sink Before It Can Swim?</p>

<p>LLM-based detection of voice recognition errors in radiology reports: development, validation and impact of personalised feedback</p>

<p>Who Waits Longer? A Systematic Review of Ethnic Disparities in AI-Driven Triage and Patient Prioritisation</p>

<p>Automated Uterine Fibroid Mapping on MRI Using Deep Learning: Development and Proof of Concept</p>

<p>When private companies develop AI Radiology screening tools, do they have an obligation to share profits with data donors? </p>

<p>Clinician Perceptions of AI-Assisted Abdominal Ultrasound</p>

<p>Whose data, whose ethics? A normative and empirical inquiry into use of patient data for development of AI in radiology</p>