Staff Profile

Biography

Alaa Alahmadi is a Lecturer in Computing at the School of Computing, based in the Interdisciplinary Computing and Complex BioSystems (ICOS) research group. She has also held an honorary position at the University of Manchester since 2021.

She completed her MSc and PhD at the University of Manchester (2016-2021), working on data visualisation across multiple clinical trials (with AstraZeneca) during her MSc, and then devising, developing and evaluating novel ECG monitoring approaches during her PhD. Her research enables the general public for the first time to accurately interpret an electrocardiogram (ECG) and intuitively monitor a life-threatening condition that can be caused by many commonly prescribed medications (clinically known as drug-induced long QT syndrome). She developed novel methods combining explainable artificial intelligence and science-of-perception-based novel data visualisation approaches, collaborating with The Christie NHS Foundation Trust, Cancer Research UK, and AstraZeneca. 

Her research has been recognised with numerous accolades, including the MIT Innovators Under 35 Award, the IET Healthcare Technology Awards (Highly Commended), and the Parliamentary and Scientific Committee STEM for Britain Awards (only Computer Science finalist). She also won multiple university awards, including the University of Manchester Outstanding Doctoral Paper in Computer Science (2019), Outstanding Doctoral Thesis in Computer Science (Runner Up, 2022), and UK-SACB Saudi Excellence Doctoral Research Awards in 2019, 2020 and 2021. She has since undertaken postdoctoral work, securing funding for two research grants, and presented her work through an invited talk at the ISCE 2023 conference in Palm Springs, USA, one of the biggest international annual meetings on computerized electrocardiology, bringing together computer scientists, clinicians, and FDA, supported by top ECG companies, including Philips and GE Healthcare.

Research Interests

Alaa Alahmadi’s work lies at the intersection of Artificial Intelligence, Cognitive Science, Engineering, Medicine, and Healthcare Technology, by effectively and efficiently mimicking living intelligent systems to create innovative and advanced technologies. She has a particular interest in cardiac monitoring, primarily in drug-induced proarrhythmia, and brain-heart interaction, within the context of preventing sudden unexpected cardiac death. Her research applies cognitively informed AI methods to real-world medical data, especially physiological and biomedical signals, to investigate how humans and intelligent systems jointly interpret dynamic signals. Through this work, she aims to advance smart human–machine interfaces and devices in clinical settings and improve patient care and health outcomes.

Memberships of committees and professional bodies

Member of the Institution of Engineering and Technology (IET) (MIET)

Associate Member of the Royal Society of Medicine

• Articles

  • L Hughes-Noehrer, Alahmadi A, Channer L, Rahim A, Body R, Jay C. Attitudes of clinicians to a ‘human-like’ explainable AI based on pseudo-colouring of ECGs that exposes life-threatening anomalies. Journal of Electrocardiology 2024.
  • A, Alahmadi, A, Davies, M, Vigo, C, Jay. Personalized, intuitive & visual QT-prolongation monitoring using patient-specific QTc threshold with pseudo-coloring and explainable AI. Journal of Electrocardiology 2023.
  • Sqalli, MT, Al-Thani, D, Elshazly, MB, Al-Hijji, M, Alahmadi, A, Houssaini, YS. Understanding Cardiology Practitioners’ Interpretations of Electrocardiograms: An Eye-Tracking Study. 2022.
  • Alahmadi, A, Davies, A, Royle, J, Goodwin, L, Cresswell, K, Arain, Z, Vigo, M, Jay, C. An explainable algorithm for detecting drug-induced QT-prolongation at risk of torsades de pointes (TdP) regardless of heart rate and T-wave morphology. 2021.
  • Alahmadi, A, Davies, A, Vigo, M, Jay, C. Pseudo-colouring an ECG enables lay people to detect QT-interval prolongation regardless of heart rate. 2020. In Preparation.
  • Alahmadi, A, Davies, A, Vigo, M, Jay, C. Can laypeople identify a drug-induced QT interval prolongation? A psychophysical and eye-tracking experiment examining the ability of nonexperts to interpret an ECG. 2019. In Preparation.

• Book Chapter

  • Alahmadi, A, Davies, A, Vigo, M, Dempsey, K, Jay, C. Human-Machine Perception of Complex Signal Data. 2022. In Preparation.

• Conference Proceedings (inc. Abstracts)

  • Sqalli MT, Al-Thani D, Elshazly MB, Al-Hijji M, Alahmadi A, Houssaini YS. An Eye-Tracking Based Machine Learning Model Towards the Prediction. In: International Conference on Medical Imaging and Computer-Aided Diagnosis. 2023.
  • Alahmadi, A, Davies, A, Royle, J, Vigo, M, Jay, C. Evaluating the impact of pseudo-colour and coordinate system on the detection of medication-induced ECG changes. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13). 2019.