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Update organisers, speakers, and call for presentations
Expanded the list of organisers and invited speakers for the BMVA Symposium on Advancing Medical Care with AI Agents. Added detailed information to the call for presentations, including symposium focus, topics of interest, and presentation formats. Clarified that the event is in-person only.
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date: 2026-05-27
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meeting-title: "BMVA Symposium on Advancing Medical Care with AI Agents"
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image: BMVAForYoutube.png
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organisers: Vivek Singh (Queen Mary University of London), Mostafa Kamal Sarker (Technovative Solutions Ltd)
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organisers: Dr Vivek Singh - (Barts Cancer Institute, Queen Mary University of London), Dr Md Mostafa Kamal Sarker, (Department of Oncology, University of Cambridge), Dr Pramit Saha, (Department of Engineering Science, University of Oxford), Dr Concetta Piazzese, (Barts Health NHS Trust), Venu Tammabatula - (Pulse AI Care Ltd)
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## Invited Speakers
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Coming Soon
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*
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* Prof. Domenec Puig, Universitat Rovira i Virgili Spain
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Expert in computer vision, image processing, pattern recognition, machine learning and intelligent robotics research as a full professor and principal investigator
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* Prof. Mihaela Van Der Schaar, University of Cambridge
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John Humphrey Plummer Professor of Machine Learning, AI, and Medicine at the University of Cambridge, where she is director of the Cambridge Centre for AI in Medicine (CCAIM), and a Chancellor's Professor of Electrical and Computer Engineering at the University of California, Los Angeles and is building next generation medical AI agents
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* Prof. Kyle Lam, Imperial College London
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NIHR Academic Clinical Fellow in General Surgery, developing LLM-based agentic systems in medicine and healthcare
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* Dr Ricky Gondhia, Pulse AI Care Ltd, UK
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Medical doctor and co-founder of Pulse AI, applying AI to improve cancer patient monitoring and outcomes.
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* Dr. Andrew Soltan, University of Oxford and NHS
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NIHR Academic Clinical Lecturer, Junior Research Fellow in Engineering, and Medical Oncology Specialty Registrar, leading the development of TrustedMDT, an agentic AI system to support multidisciplinary decision making in oncology
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* Dr Mobarak I Hoque, University of Manchester
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Associate Professor (Senior Lecturer) in Multimodal Agentic AI for Healthcare, Biomedical Data Science and AI, Division of Informatics, Imaging & Data Sciences
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* Dr. Harshita Sharma, Microsoft Research Lab - Cambridge
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Senior ML Researcher in the Biomedical Imaging team at Microsoft
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## Call for Presentations
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Coming Soon...
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The British Machine Vision Association (BMVA) is pleased to announce a one-day Technical Meeting entitled **Advancing Medical Care with AI Agents.** This symposium brings together researchers, clinicians, and industry practitioners to explore how artificial intelligence, machine learning, and computer vision are transforming medical care, with a particular focus on oncology and surgery as clinically rich, challenging, and emerging application domains.
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Early deep learning methods were primarily developed for perceptual tasks, and their capacity to support clinical decision workflows remains limited: they often cannot reliably articulate clinical logic, incorporate broader patient context, or explicitly coordinate temporal and multimodal signals. Today, the landscape has broadened with the rise of **agentic LLM systems** that can orchestrate multiple specialist models and tools over medical images, integrate multimodal clinical context, generate and refine explanations, critique intermediate results, and enable interactive, clinician-in-the-loop decision support. In this setting, LLMs provide a complementary paradigm that couples strong perception with **tool-mediated grounding, structured orchestration, and dialogue-based explanation.** Rather than replacing established imaging models, they can augment them by connecting outputs across tools, surfacing clinically relevant context, and supporting interactive, transparent workflows.
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Such recent advances in AI agents, systems capable of perception, reasoning, and decision support within complex clinical environments, are enabling the integration of multimodal data and supporting medical workflows at scale. While cancer care and surgery serve as the central motivating use cases, the methods and insights discussed are expected to generalise across a broad range of disease areas, including cancer, neurological, cardiovascular, and chronic conditions.
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The symposium will feature state-of-the-art research, real-world clinical deployments, and translational perspectives from academia, industry, and Barts Health NHS Trust hospitals. Submissions addressing both methodological advances and applied clinical impact are encouraged.
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**Topics of Interest include (but are not limited to):**
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* AI and computer vision for medical imaging (radiology, pathology, and computational imaging)
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* Deep learning methods for detection, segmentation, and classification in healthcare
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* AI agents for clinical decision support and workflow integration
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* Multimodal learning integrating imaging, clinical records, genomics, and biomedical data
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* Personalised and precision medicine, including treatment response prediction
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* AI-driven treatment planning and therapy optimisation
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* Artificial intelligence for drug discovery and translational medicine
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* Agent-based approaches for virtual screening and clinical trial optimisation
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* Responsible AI in healthcare: ethics, fairness, explainability, and robustness
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* Regulatory, validation, and deployment challenges in clinical settings
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* Real-world case studies of AI systems in cancer care and other disease domains
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The meeting aims to foster interdisciplinary dialogue and collaboration, supporting the responsible translation of AI technologies into routine clinical practice.
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Presentation Formats:
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* Talks
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* Posters
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* Demos
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Important: This is an in-person event, with no virtual attendance option. We kindly ask all
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**Important:** This is an in-person event, with no virtual attendance option. We kindly ask all
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presenters to join us at the British Computer Society on the day.
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**Presentations can be either published work, or ongoing research**.
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