Ms Katarzyna Powezka MD (Clin), MRCS, PGCert (Med Ed)

• PGCert in Mecical Education, University of Warwick, 2026
• Membership of the Royal College of Surgeons of England (MRCS), 2018
• Certificate of Completion of Training in General Surgery, Poland, 2014
• Doctor of Medicine Degree (Clinical), Karol Marcinkowski University of Medical Sciences, Poznan, Poland, 2004
• Professional memberships: Royal College of Surgeons of Edinburgh; British Society of Endovascular Therapy; European Society for Vascular Surgery

Katarzyna Powezka is an Academic Clinical Lecturer in Vascular Surgery at University of Birmingham and a Specialty Registrar in Vascular Surgery at University Hospitals Birmingham NHS Foundation Trust. She graduated in medicine from Karol Marcinkowski University of Medical Sciences in Poznan, Poland, in 2004 and subsequently completed postgraduate training in general surgery in Poland, receiving a Certificate of Completion of Training in 2014.

After moving to the UK, she worked in general surgery, urology and vascular surgery at Wexham Park Hospital, Imperial College Healthcare NHS Trust and Pennine Acute Hospitals NHS Trust before entering higher specialist training in vascular surgery in Scotland. She later undertook postgraduate doctoral research at the University of Birmingham and worked as a Clinical Research Fellow at University Hospitals Birmingham. Her academic work has focused increasingly on the application of artificial intelligence to routinely collected clinical data, particularly the use of machine learning and natural language processing to identify vascular disease from unstructured radiology reports.

Her research interests include acute aortic syndromes, artificial intelligence and natural language processing in vascular surgery, clinical outcomes, surgical human factors and the use of routinely collected healthcare data to improve patient identification and clinical pathways. She has contributed to national and international multicentre vascular studies and has presented her work at meetings including the Charing Cross Symposium, European Society for Vascular Surgery, Paris Vascular Insights, BSET and VSASM.

Alongside her research and clinical training, she has an active interest in medical education. She completed a PGCert in Medical Education at the University of Warwick in 2026 and has experience teaching medical students and junior doctors, examining OSCEs and organising regional endovascular simulation training.

Artificial intelligence, NLP and vascular data science

Katarzyna’s research focuses on the development and clinical translation of artificial intelligence methods using routinely collected healthcare data. Her doctoral research developed machine-learning and natural language processing (NLP) approaches to identify patients with Acute Aortic Syndromes from unstructured radiology reports, compare NLP-based case identification with administrative coding, and examine associated clinical outcomes, including short- and mid-term mortality. A central aim of this work is to move beyond retrospective model development towards clinically useful tools that can support systematic case finding, earlier specialist referral, cohort identification and recruitment to research. She also contributes to collaborative NLP and machine-learning projects investigating the identification of mesenteric aneurysms and venous disease from unstructured clinical text.

Clinical outcomes and collaborative vascular research

Her wider research spans aortic and peripheral vascular disease, clinical outcomes, systematic reviews and large multicentre collaborative studies. She has contributed to the SWHSI-2 randomised controlled trial, the VERN COVER study examining the impact of COVID-19 on vascular services, and international studies of acute limb ischaemia. Her collaborative work has also included diabetic foot disease, deep vein thrombosis and outcomes following vascular and endovascular interventions. Across these projects, her interest is in using clinically relevant data to improve patient identification, pathways of care and the evidence base for vascular practice.

Surgical human factors and physiological signals

Earlier research explored surgical team dynamics and the use of physiological signals, including heart-rate variability and autonomic measures, to investigate teamwork, pain and procedural duration. This work led to peer-reviewed publications on operating-team familiarity, physiological synchrony and pain prediction in vascular surgery. Her developing academic programme brings these quantitative and data-driven interests together, with a focus on clinically useful applications of AI and routinely collected health data in vascular disease.