Andrew Soltan
MB BChir MA (Cantab) MA (Oxon) MRCP
Clinical Artificial Intelligence Researcher & Specialty Registrar in Medical Oncology
- Specialty Registrar in Medical Oncology, Oxford University Hospitals NHS Foundation Trust
- Clinical Artificial Intelligence Registrar, Departments of Engineering Science & Oncology
- NIHR Academic Clinical Fellow
I am a Specialty Registrar in Medical Oncology at Oxford University Hospitals NHS Foundation Trust, and Clinical Artificial Intelligence Researcher and 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.
Recent publications
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High-performance automated abstract screening with large language model ensembles
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.
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.
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.
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
Kuo R. et al, (2024)
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Generalizability assessment of AI models across hospitals in a low-middle and high income country.
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.
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.
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.
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
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.
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.
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
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
YANG J. et al, (2023), Nature Machine Intelligence
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Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening
YANG J. et al, (2023), npj Digital Medicine