Dr Antonio de Marvao February Seminar Series
February 10, 2026
We were pleased to welcome Dr Antonio de Marvao - Clinical Senior Lecturer at King's College London, and Consultant Cardiologist and Obstetric Physician at GSTT and KCH - who delivered his talk “Detecting the Rare, Managing the Common: AI-Driven Cardiovascular Care Using EHR Data"
as part of our Seminar Series.
Abstract: Cardiovascular disease encompasses rare inherited conditions and highly prevalent disorders such as hypertension and cardiometabolic disease. Despite differing epidemiology, both require accurate, dynamic and scalable risk stratification. Electronic health records provide longitudinal, multimodal data at population scale. However, their heterogeneity and fragmentation demand advanced artificial intelligence methods to generate clinically actionable insight. Approximately 70 to 80 percent of NHS data exists in unstructured free text, rendering much of the clinically relevant signal inaccessible to conventional analytics without natural language processing or large language models.
To address this challenge, we have been developing an AI-enabled framework for real-world cardiovascular risk prediction using integrated EHR data. The approach brings together structured clinical variables, imaging outputs and free-text documentation within secure hospital environments. Natural language processing and large language models are used to transform narrative records into computable features, while chain-of-thought reasoning architectures extract guideline-defined risk parameters directly from routine documentation. This enables automated calculation of established risk scores and dynamic longitudinal reassessment within an agentic workflow. Local, open-source models are evaluated across parameter scales to ensure an appropriate balance between accuracy, safety and computational efficiency for clinical deployment.
In inherited cardiac conditions, this approach enables automated extraction of echocardiographic and clinical features required for sudden cardiac death risk prediction, reducing manual burden and supporting real-time monitoring. The same principles extend to hypertensive disorders of pregnancy, facilitating earlier detection, structured surveillance and stratification of long-term cardiovascular risk.
Integration of high-resolution EHR-derived phenotypes with genomic and multi-omics datasets further supports progression from risk prediction to biological insight and therapeutic target discovery. Applied rigorously, AI methodologies operating on routine healthcare data provide a scalable foundation for precision cardiovascular care across the life course.
Seminar Series Event: “Detecting the Rare, Managing the Common: AI-Driven Cardiovascular Care Using EHR Data"
Date and Time:
Tuesday 24 February 2026, 15:30 – 16.30 hrs (GMT)
Location:
IoPPN Seminar 1 & 2, Denmark Hill Campus
Attendance:
Mandatory for all DRIVE-Health students; a calendar invitation has already been sent.
Registration:
Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend.
Biography
Antonio de Marvao is a Clinical Senior Lecturer at KCL, and a Consultant Cardiologist and Obstetric Physician at GSTT and KCH, specialising in inherited cardiac conditions, maternal cardiology, and hypertensive disorders of pregnancy. His research sits at the intersection of electronic health records (EHR) derived phenotyping, genomics/multi-omics, and cardiovascular imaging, using machine learning to improve risk prediction modelling and personalise care, across the reproductive continuum - from pregnancy to postpartum - and long-term cardiovascular prevention.
He leads work within the NHS England Genomic AI Network, applying natural language processing, large language models and multimodal EHR integration to identify patients with inherited cardiovascular disease, streamline specialist review, and improve access to genetic testing and family screening. In parallel, his group also uses AI and EHR data to better define and detect hypertensive disorders of pregnancy at scale, quantify disparities, and enable earlier, more targeted intervention.
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Bridging Academia and Industry: Turning Health Data into Health Solutions Please join us for the EPSRC DRIVE‑Health CDT Summer Symposium & Partner Showcase 2026 , a two‑day event dedicated to bridging academia and industry and accelerating the journey from health data research to real‑world solutions. This year’s theme, “Bridging Academia and Industry: Turning Health Data into Health Solutions", brings together our vibrant community of students, King’s academics, industry partners, clinicians and policymakers to explore how collaboration can unlock new insights, innovation and impact across health and care. Event details 📅 11 & 12 June 2026 📍 Science Gallery London, Guy’s Campus, King’s College London, Great Maze Pond, London SE1 9GU Timings: ⏰ 10:00–16:00 each day ☕ Refreshments available from 09:30 👉 Please be seated by 09:50 for a prompt 10:00 start Please secure your place here: Register Now Programme highlights KEYNOTE ✨ Dr Luis Garcia‑Gancedo , Executive Director & Head of Digital Medicine – Respiratory, Immunology & Inflammation, GSK INTERACTIVE DEBATE ✨ Student‑led interactive debate exploring AI within the context of the event theme: “Bridging Academia and Industry: Turning Health Data into Health Solutions.” PANEL DISCUSSION & POSTER JUDGING ✨ “What does industry actually need from health data PhDs - and how can industry partner with academia for maximum impact?” Panelists confirmed so far: Dr Laura Acqualagna , Director of AI/ML Engineering, GSK R&D Dr Chris Callaghan , Consultant Transplant Surgeon, Guy’s Hospital Dr Nina Sesto , CEO & Co‑Founder, MEGI Health Dr Srinivasan Vairavan , Director of Data Science & Digital Health, JNJ Innovative Medicine R&D, and Visiting Adjunct Faculty, King’s College London Dr Nicolas Huber , Director, King’s Innovation Catalyst PLUS ✨ Student lightning talks (across both days) ✨ 3‑minute Student Spotlight Slides (across both days) ✨ Poster showcase & networking session (Friday) ✨ Prizes for outstanding contributions (Friday) Please secure your place here: Register Now If you are no longer able to attend the event, please email drive-health-cdt@kcl.ac.uk so that we can reallocate to our waiting list.
We are looking forward to welcoming Professor Honghan Wu, Professor of Health Informatics and AI at the University of Glasgow, who will deliver his talk “Large language model and Radiology: how to facilitate human and AI collaboration? " as part of our Seminar Series. Abstract: In this upcoming talk, Professor Honghan Wu explores the essential shift from viewing AI as a potential replacement for radiologists to recognizing it as a critical collaborative partner. Moving beyond basic tasks like detection and triage, the presentation highlights how AI can address practical clinical "pain points," such as reducing automated protocoling time by up to 60% and decreasing the time spent communicating with providers and patients by 30%. Professor Wu will present recent research on using knowledge-retrieval and Large Language Models for clinical report error correction and generation. The session concludes with an examination of the real-world deployment lifecycle, discussing the challenges of monitoring the over 700 FDA-cleared radiology AI devices currently in practice Seminar Series Event : “Large language model and Radiology: how to facilitate human and AI collaboration?" Date and Time: Thursday 25 June 2026, 15:00 – 16.00 hrs (BST) Location: Large Committee Room, Hodgkin Building, Guy's Campus Attendance: Mandatory for all DRIVE-Health students; a calendar invitation has already been sent. Registration: Alumni and wider King's College London research community all welcome - please email drive-health-cdt@kcl.ac.uk to let us know if you would like to attend. Biography Honghan Wu is a Professor of Health Informatics and AI, based in the School of Health and Wellbeing of the University of Glasgow, where he leads the research theme of data science and AI. Prof Wu is a co-director of Health Data Research Scotland. He also is an honorary professor at Hong Kong University, an honorary associate professor at Institute of Health Informatics, UCL, and a former Turing Fellow of The Alan Turing Institute, UK's national institute for data science and artificial intelligence. Prof Wu holds a PhD in Computing Science. His current research focuses on machine learning, natural language processing, knowledge graph and their applications in medicine.
