Frances Conti-Ramsden

Fellowship Bio

Dr Frances Conti-Ramsden is a clinical academic in women’s health. A Chadburn Clinical Lecturer at King’s College London, O&G registrar at GSTT, and CMO of MEGI Health, she applies AI and real-world data to hypertensive pregnancy disorders with strong focus on maternal health equity.

Fellowship Project

Pregnancy complications are an early warning for a woman’s future health, yet the NHS has no defined pathway to follow up women after a high-risk pregnancy and follow-up is missed most often by those at highest risk. My work set out to close this postnatal gap using AI and real-world data, building a complete pathway: finding women who had a high-risk pregnancy, predicting who is most at risk, engaging them after birth with a supportive AI platform, and evaluating the whole approach in a clinical trial.

The pathway spanned my fellowship, academic and commercial roles. For the NHS fellowship I led the case-finding work, using CogStack NLP to identify women from routine maternity records and revealing ethnic disparities in hypertensive disorders of pregnancy. I used this data to build the foundation for antenatal risk prediction. As CMO of MEGI Health, and through the Innovate UK EMPOWER-BP project, I helped develop and validate the AI conversational platform that supports women after birth including co-design with women with lived experience, beta testing and safety benchmarking. I am now co-leading COMPASS, a funded pilot randomised controlled trial that will test the pathway.

The case-finding study analysed over 58,000 pregnancies, won first prize at the GSTT AI in Clinical Practice Conference 2026 and is being prepared for publication. The AI platform has been co-designed, beta-tested and safety-benchmarked. COMPASS has been funded through the EXIT-CVD Horizon Europe consortium, with recruitment expected in Q4 2027. Still to come: completing the antenatal prediction models, integrating case-finding into a hospital risk dashboard, and delivering COMPASS.

Fellowship Testimonial

This fellowship gave me the time and flexibility to grow from clinician towards my goal of being a leader in clinical AI for women’s health. I learned to build and evaluate AI alongside product and technical colleagues, to implement NLP across large, multimodal electronic health record datasets within secure NHS data environments, and to carry a single idea from research question, through product, to clinical trial. The greatest reward has been the multidisciplinary nature of the work, requiring fluid movement between academia and industry, and between clinical, technical and product teams. Alongside this, joining a network of fellows tackling health challenges with AI has been a constant source of ideas. I leave with technical fluency, a clear-eyed understanding of what it takes to move a model from a data lake to the front line, and a network spanning the NHS, academia and industry. As I step into my Chadburn Clinical Lectureship, I will build on all of this — combining clinical practice with clinical AI leadership to make maternal healthcare more scalable, equitable and ready for the technologies reshaping medicine.