Dr Petroula Laiou hosts our May Seminar Series
April 9, 2025
Thanks to Dr Petroula Laiou from King's College London,
for delivering our May Seminar Series with her talk,
"Bridging the Gap: Turning Academic Research into Clinical Innovation".
Petroula shared her journey of translating cutting-edge academic research into a mission-driven MedTech company. The spinout is pioneering a novel approach to forecasting and preventing seizures in people with drug-resistant epilepsy - an innovation rooted in years of interdisciplinary work at the intersection of clinical neuroscience, signal processing, and artificial intelligence.
Dr. Laiou took the audience through the full translational pathway: from identifying an unmet clinical need, designing and analysing first-in-human studies, and developing a seizure prediction algorithm, to securing translational funding, navigating the intellectual property landscape, and filing an international patent (PCT/GB2024/052456).
She reflected on key lessons learned during her time in the King’s MedTech Accelerator Programme - where the team won the Best Innovation award - and share insights on building bridges between academia and industry, shaping a commercialization strategy, and transitioning from researcher to entrepreneur.
The talk also highlighted the challenges and rewards of launching a spinout in the healthcare sector and offer practical advice for PhD students and early-career researchers considering the entrepreneurial route.
Seminar Series Event: "Bridging the Gap: Turning Academic Research into Clinical Innovation"
Date and Time:
Wednesday 7 May 2025, 15:00 – 16.00 hrs (BST)
Location: The Lorna Wing Room, SGDP Building, Denmark Hill Campus, London, SE5 8AF
Attendance:
Mandatory for all DRIVE-Health students, therefore please accept the calendar invitation.
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.
Dr. Petroula Laiou is a Research Fellow in Predictive Modelling and Clinical Neuroscience at King’s College London. With a background in mathematics, computational physics, and a PhD in signal analysis, her research bridges computer science, neuroscience, and machine learning. Her work focuses on developing predictive models and digital biomarkers for neurological and psychiatric disorders, including epilepsy and depression.
Dr. Laiou led the development of a novel seizure forecasting algorithm using intracranial EEG and cortical responses to electrical stimulation—research that led to the filing of an international patent (PCT/GB2024/052456). She is the recipient of multiple research grants, including an MRC award as Principal Investigator, and her translational work was recognised by the King’s MedTech Accelerator Programme, where her team won the Best Innovation award.
She has authored over 40 peer-reviewed publications, presented at major international conferences, and actively contributes to interdisciplinary collaborations across academia, hospitals, and industry.
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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 November 2026, 15:00 – 16.00 hrs (GMT) Location: Venue to be confirmed. 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.
We are looking forward to welcoming Dr. Bettina Moltrecht and Thomas Wood to introduce Harmony Meta , a groundbreaking platform developed over the past year to bridge the gap between disparate study catalogues and registers. While traditional data discovery relies on exact keyword matching, Harmony Meta utilizes Large Language Models and vector indexing to allow for semantic searching across 5.5 million variables . Abstract: This session will demonstrate how researchers can now locate longitudinal data using approximate synonyms—for instance, a search for "dyslexia" will successfully retrieve variables related to "difficulty reading." The platform indexes nearly every major longitudinal study ever conducted in the UK, including the Millennium Cohort Study , the 1970 British Cohort Study , and Born in Bradford . The presenters will discuss the technical backend of converting millions of variables into vectors and the practical implications for harmonizing data across different cohorts to identify population mental health trends. Try the Tool: https://harmonydata.ac.uk/search Seminar Series Event : " Harmony Meta: Using AI to Unlock 5.5 Million Variables in UK Longitudinal Studies" Date and Time: Thursday 24 September 2026, 15:00 – 16.00 (BST) Location: Venue to be confirmed. 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. Biographies Dr. Bettina Moltrecht Dr. Bettina Moltrecht is a mental health researcher based at University College London (UCL) and Anna Freud a UK-based mental health charity for children and families. Bettina combines a clinical, tech and research background, and has been co-leading the Harmony project with the aim to enhance population mental health research. Bettina is co-founder of UCL's Digital Mental Health Hub, and is co-investigator on various clinical trials to evaluate mental health interventions in the NHS. Thomas Wood Thomas Wood is the founder of Fast Data Science and the lead developer for the Harmony Meta backend. He holds a Master’s in Physics from Durham University and a Master’s in Computer Speech, Text and Internet Technology from the University of Cambridge. With over a decade of experience in machine learning and NLP, Thomas has consulted for the NHS, Tesco, and Boehringer Ingelheim. He also works as an expert witness and is working on NLP solutions for clinical trials, and generative AI solutions for legal question answering. Note on Funding and Partners: Harmony Meta was funded by the ESRC and developed in collaboration with Population Research UK (PRUK), the UCL Centre for Longitudinal Studies, DATAMIND UK, The Alan Turing Institute, and UK Research and Innovation.
