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RCR Global AI Conference 2026

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119 posters, 6 topics, 524 authors, 243 institutions

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RCR Global AI Conference 2026

29-30 June, 2026 | QEII Centre, Westminster

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215

<p>Human-centred design for responsible artificial intelligence in feeding tube assessment on chest radiographs</p>

Preview of <p>Human-centred design for responsible artificial intelligence in feeding tube assessment on chest radiographs</p>

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Poster Presenter

Abhijith Mohan Rajamohan

Authors

Anja Thieme, Abhijith Mohan Rajamohan, Benjamin Cooper, Heather Groombridge , Robert Simister, Barney Wong, Nicholas Woznitza, Mark A Pinnock, Maria T Wetscherek, Cecily Morrison, Hannah Richardson, Fernando P?�rez-Garc?�a, Stephanie L Hyland, Shruthi Bannur, Daniel Coelho de Castro, Kenza Bouzid, Anton Schwaighofer,

Part of Topic

Risk management and compliance: projects around governance, compliance and/or ethics and societal impact

Human-centred design for responsible artificial intelligence in feeding tube assessment on chest radiographs

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Mercy P Ranjit,

Harshita Sharma,

Matthew P Lungren,

Javier Alvarez-Valle,

Affiliations

University Hospitals Bristol and Weston NHS Foundation Trust, Health Futures, Microsoft Research, Cambridge, United Kingdom of Great Britain and Northern Ireland, Imaging Department, University College LondonHospitals NHS Foundation Trust, London, United Kingdom of Great Britain and Northern Ireland, Stroke, University College London Hospitals NHS FoundationTrust, London, United Kingdom of Great Britain and Northern Ireland, Computing Science, University College London, London, United Kingdom of GreatBritain and Northern Ireland, Radiology, Cambridge University, Cambridge, United Kingdom of Great Britain andNorthern Ireland and Radiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UnitedKingdom of Great Britain and Northern Ireland, Human Computer Interaction, Microsoft Research, Cambridge, United Kingdom ofGreat Britain and Northern Ireland, Health Futures, MicrosoftResearch, Cambridge, United Kingdom of Great Britain and Northern Ireland, Microsoft Research India, Bengaluru, India, Health Futures, Microsoft Research, Cambridge, United Kingdom of Great Britain andNorthern Ireland, Health Futures, Microsoft Research, Mountain View, California, USA, Radiology,University of California San Francisco, San Francisco, California, USA and Medical School, StanfordUniversity, Stanford, California, USA, Health Futures, Microsoft Research, Cambridge,United Kingdom of Great Britain and Northern Ireland, Institute of Health Informatics, University College London, London, United Kingdom ofGreat Britain and Northern Ireland, Satsuma Lab, UCL Centre for Medical Imaging Computing, University College London,London, United Kingdom of Great Britain and Northern Ireland and Respiratory, University College London,London, United Kingdom of Great Britain, Imaging Department, University College London Hospitals NHS Foundation Trust,London, United Kingdom of Great Britain and Northern Ireland and Allied and Public Health, Canterbury

Other ePosters in

Topic

147<p>Rejecting AI Legal Personhood: Targeted No-Fault Compensation for AI-...

158<p>Reader-dependent impact of AI on chest radiograph lung cancer managem...

171<p>Assessing staff perspectives on governance, trust, and bias in AI for...

183<p>Crossing the valley of death: A multi-disciplinary approach for succe...

206<p>From Algorithm to Accountability: A Governance Framework for Safe Dep...

ePosters in

Topics

AI Education and research: examples of proof of concept or AI in development, technical advances, teaching approaches or pre-clinical testing(41)AI vigilance ??? Post-implementation monitoring, real world performance evaluation, health economic evaluation(6)AI vigilance – Post-implementation monitoring, real world performance evaluation, health economic evaluation(7)General / other that does not fit into the above(3)Risk management and compliance: projects around governance, compliance and/or ethics and societal impact(13)Transforming practice and leadership: pilot or test data on implementation of AI into clinical practice, clinical feedback or patient perspectives

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