Andrew Soltan
PhD, MB BChir, MRCP
NIHR Academic Clinical Lecturer
- NIHR Academic Clinical Lecturer, Computational Health Informatics Lab (Department of Engineering Sciences)
- Medical Oncology Specialist Registrar, Oxford University Hospitals NHS Foundation Trust
I am an NIHR Academic Clinical Lecturer across the Departments of Oncology and Engineering Sciences (Computational Health Informatics Lab), and Specialty Registrar in Medical Oncology at Oxford University Hospitals NHS Foundation Trust. I am also the Dissertation Lead for the MSc in Applied Digital Health at the University of Oxford.
My research develops federated techniques and transformer-architecture models for routinely collected healthcare data; aiming to develop, evaluate and deploy clinical tools for Cancer care pathways.
During my NIHR Academic Clinical Fellowship, my research specialism was developing AI-enabled screening, diagnostic and prognostic tools using routinely collected healthcare data. Working with Professor David Clifton's Computational Health Informatics group, I led development and evaluation of an AI screening test for COVID-19 in emergency departments, based upon blood tests and vital signs recorded within 1h of a patient arriving in hospital. The CURIAL AI test was piloted in Oxford's John Radcliffe Hospital Emergency Department in early-2021, and described in an accompanying The Lancet Digital Health commentary as "an elegant breakthrough to enhance the clinical decision-making process in the age of artificial intelligence". Complimentary work has explored model-level approaches to reduce bias in AI predictions.
To support confidential development of AI models within the NHS, I developed a new platform for rapidly-deployable and readily scalable federated learning, using inexpensive micro-computing devices. The CURIAL-Federated platform was piloted across 4 NHS Trusts in 2022 to train and evaluate a Covid-19 screening test, with participating hospitals retaining custody of their data at all times, and published in The Lancet Digital Health in February 2024.
Alongside my clinical and research interests, I lead the 10-week practical dissertation module for the MSc in Applied Digital Health. The dissertation placements offer students an immersive practical experience and chance to develop a specialism within the digital health research and translational pipeline, while expanding their professional networks.
I graduated with distinction from the University of Cambridge's medical school, moving to Oxford in 2018 for postgraduate training within NIHR funded joint academic and clinical training posts.
Recent publications
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Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation
Preprint
Kuo R. et al, (2025)
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High-performance automated abstract screening with large language model ensembles
Journal article
Sanghera R. et al, (2025), Journal of the American Medical Informatics Association : JAMIA
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Tracking cortical entrainment to stages of optic-flow processing.
Journal article
Wingfield C. et al, (2025), Vision Res, 226
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Publisher Correction: Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare.
Journal article
Thakur A. et al, (2024), NPJ Digit Med, 7
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Knowledge abstraction and filtering based federated learning over heterogeneous data views in healthcare.
Journal article
Thakur A. et al, (2024), NPJ Digit Med, 7
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Comparative evaluation of large-language models and purpose-built software for medical record de-identification
Preprint
Kuo R. et al, (2024)
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Generalizability assessment of AI models across hospitals in a low-middle and high income country.
Journal article
Yang J. et al, (2024), Nat Commun, 15
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Addressing label noise for electronic health records: insights from computer vision for tabular data.
Journal article
Yang J. et al, (2024), BMC Med Inform Decis Mak, 24
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From data to diagnosis: skin cancer image datasets for artificial intelligence.
Journal article
Wen D. et al, (2024), Clin Exp Dermatol, 49, 675 - 685
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Mitigating machine learning bias between high income and low-middle income countries for enhanced model fairness and generalizability.
Journal article
Yang J. et al, (2024), Sci Rep, 14
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Mitigating Machine Learning Bias Between High Income and Low-Middle Income Countries for Enhanced Model Fairness and Generalizability
Preprint
Yang J. et al, (2024)
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A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals.
Journal article
Soltan AAS. et al, (2024), Lancet Digit Health, 6, e93 - e104
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Deep reinforcement learning for multi-class imbalanced training: applications in healthcare.
Journal article
Yang J. et al, (2024), Mach Learn, 113, 2655 - 2674
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Federated Learning For Heterogeneous Electronic Health Records Utilising Augmented Temporal Graph Attention Networks
Conference paper
Molaei S. et al, (2024), Proceedings of Machine Learning Research, 238, 1342 - 1350
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Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
Journal article
YANG J. et al, (2023), Nature Machine Intelligence