119 posters, 6 topics, 524 authors, 243 institutions

RCR Global AI Conference 2026

29-30 June, 2026 | QEII Centre, Westminster

Posters

Showing 119 results.

116

Deep Learning for Predicting Incident Heart Failure from Antecedent Risk Factors

Sashi Kiran Mahapatra, Christopher Hayward, Ramesh Nadarajah, Derek Magee

University of Leeds, Leeds Institute for Cardiovascular and Metabolic Medicine, Department of Cardiology, Leeds Teaching Hospitals NHS Trust

Poster Presenter: Sashi Kiran Mahapatra

117

A Comparative Evaluation of a Domain-Specific Large Language Model and GPT-4o for Radiology Report Summarization

Sunyi Zheng, Shui Liu, Jing Wang, Xiaonan Cui, Zhaoxiang Ye

Tianjin Medical University Cancer Institute & Hospital, Med Frontier Suppliers & Consultancy UK Limited, Tianjin University of Traditional Chinese Medicine

Poster Presenter: Sunyi Zheng

Thumbnail for <p>Predicting Pathological Complete Response to Neoadjuvant Chemoimmunotherapy in NSCLC Using Radiomics Features from Pre- and Post-Treatment CT Scans</p>

118

Predicting Pathological Complete Response to Neoadjuvant Chemoimmunotherapy in NSCLC Using Radiomics Features from Pre- and Post-Treatment CT Scans

Sunyi Zheng, Jing Wang, Shui Liu, Zhaoxiang Ye, Xiaonan Cui

Tianjin Medical University Cancer Institute & Hospital, Med Frontier Suppliers & Consultancy UK Limited, Tianjin University of Traditional Chinese Medicine, Tianjin Medical University Cancer Institute & Hospita...

Poster Presenter: Sunyi Zheng

119

Shifting Horizons: How Pakistani Radiologists in Pakistan and the UK View AI’s Impact on Radiology Practice

Ali Mansoor, Rabia Shaukat, Suneela Shaukat, Sadaf Arooj, Sarah Harrison

Eastern Medical Technology Services, Lahore General Hospital / Ameer ud Din Medical College/ Post Graduate Medical Institute, Eastern Medical Technology Services, Torbay and South Devon Hospital, UK, Jinnah Hos...

Poster Presenter: Rabia Shaukat

Thumbnail for <p>Comparative Assessment of Convolutional Neural Network Architectures for BI-RADS Classification Using a Local Mammography Dataset</p>

122

Comparative Assessment of Convolutional Neural Network Architectures for BI-RADS Classification Using a Local Mammography Dataset

Elham Keshavarz, Hamed Ghorani, Navid Zandi, Saghar Rasouli Nahad, Matin Noroozi, Reyhaneh Sadat Mortazavi

Shahid Beheshti University Of Medical Sciences, Tehran University Of Medical Sciences, Isfahan University Of Medical Sciences

Poster Presenter: Elham Keshavarz

Thumbnail for <p>Enhancing Diagnostic Accuracy in Neurology: A Deep Learning Framework for Distinguishing Multiple Sclerosis from Cerebral Small Vessel Disease on Routine Non-Contrast Brain MRI</p>

123

Enhancing Diagnostic Accuracy in Neurology: A Deep Learning Framework for Distinguishing Multiple Sclerosis from Cerebral Small Vessel Disease on Routine Non-Contrast Brain MRI

Elham Keshavarz, Kavoos Firouznia, Mehran Arab Ahmadi, Hassan Hashemi, Melika Boroomand-Saboor, Rassa Ghavami Modegh, Haned Dashti, Masoumeh Gity

Tehran University Of Medical Sciences, Shahid Beheshti University Of Medical Sciences, Department of Computer Engineering, Sharif University of Technology

Poster Presenter: Elham Keshavarz

Thumbnail for <p>The RADICAL study: A large mixed-methods prospective study evaluating a commercial chest x-ray algorithm for detection of urgent suspicion of cancer.</p>

124

The RADICAL study: A large mixed-methods prospective study evaluating a commercial chest x-ray algorithm for detection of urgent suspicion of cancer.

David Lowe

Digital Health Validation Lab

Poster Presenter: David Lowe

Thumbnail for <p>How reliably can AI-generated OAR contours be used for prostate radiotherapy without compromising clinical dose constraints?</p>

125

How reliably can AI-generated OAR contours be used for prostate radiotherapy without compromising clinical dose constraints?

Akriti Nanda, Daniel Sandys, Gemma Bell, Douglas Brand

University College Hospitals London

Poster Presenter: Akriti Nanda

Thumbnail for <p>Clinician-Led Development of a PACS-Native AI and Reporting Pipeline for Neonatal Cranial Ultrasound in Hypoxic–Ischaemic Encephalopathy to overcome interobserver variability, time and resource constrains.</p>

126

Clinician-Led Development of a PACS-Native AI and Reporting Pipeline for Neonatal Cranial Ultrasound in Hypoxic–Ischaemic Encephalopathy to overcome interobserver variability, time and resource constrains.

Robert English, Rayya Naffa', Shirou Mosoodi, Paige Miller, Sivakumar Manickam

Mid & South Essex NHS trust , Barts and the London, Mid And South Essex Foundation NHS Trust, North Hertfordshire Teaching NHS Trust, The Dudley Group NHS Foundation Trust

Poster Presenter: Robert English

RCR Global AI Conference 2026

Posters

<p>Deep Learning for Predicting Incident Heart Failure from Antecedent Risk Factors</p>

<p>A Comparative Evaluation of a Domain-Specific Large Language Model and GPT-4o for Radiology Report Summarization</p>

<p>Predicting Pathological Complete Response to Neoadjuvant Chemoimmunotherapy in NSCLC Using Radiomics Features from Pre- and Post-Treatment CT Scans</p>

<p>Shifting Horizons: How Pakistani Radiologists in Pakistan and the UK View AI’s Impact on Radiology Practice</p>

<p>Comparative Assessment of Convolutional Neural Network Architectures for BI-RADS Classification Using a Local Mammography Dataset</p>

<p>Enhancing Diagnostic Accuracy in Neurology: A Deep Learning Framework for Distinguishing Multiple Sclerosis from Cerebral Small Vessel Disease on Routine Non-Contrast Brain MRI</p>

<p>The RADICAL study: A large mixed-methods prospective study evaluating a commercial chest x-ray algorithm for detection of urgent suspicion of cancer.</p>

<p>How reliably can AI-generated OAR contours be used for prostate radiotherapy without compromising clinical dose constraints?</p>

<p>Clinician-Led Development of a PACS-Native AI and Reporting Pipeline for Neonatal Cranial Ultrasound in Hypoxic–Ischaemic Encephalopathy to overcome interobserver variability, time and resource constrains.</p>