Andrew Soltan
MB BChir MA (Cantab) MA (Oxon) MRCP
Clinical Artificial Intelligence Registrar & 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".
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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From Data to Diagnosis: Skin Cancer Image Datasets for Artificial Intelligence.
Journal article
Wen D. et al, (2024), Clin Exp Dermatol
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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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Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
Journal article
YANG J. et al, (2023), Nature Machine Intelligence
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Deep Reinforcement Learning for Multi-class Imbalanced Training: Applications in Healthcare
Journal article
YANG J. et al, (2023), Machine Learning
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Machine learning generalizability across healthcare settings: insights from multi-site COVID-19 screening
Journal article
YANG J. et al, (2023), npj Digital Medicine
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A scalable federated learning solution for emergency care using low cost microcomputing: Privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals
Journal article
Soltan A. et al, (2023), The Lancet. Digital Health
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Addressing Label Noise for Electronic Health Records: Insights from Computer Vision for Tabular Data
Preprint
Yang J. et al, (2023)
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Generalizability Assessment of AI Models Across Hospitals: A Comparative Study in Low-Middle Income and High Income Countries
Preprint
Yang J. et al, (2023)
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Geometrically-aggregated training samples: Leveraging summary statistics to enable healthcare data democratization
Preprint
Yang J. et al, (2023)
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Deep Reinforcement Learning for Multi-class Imbalanced Training
Preprint
Yang J. et al, (2022)
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Privacy-aware Early Detection of COVID-19 through Adversarial Training
Preprint
Rohanian O. et al, (2022)
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Algorithmic Fairness and Bias Mitigation for Clinical Machine Learning: A New Utility for Deep Reinforcement Learning
Preprint
Yang J. et al, (2022)