Biography
Anmol Arora (MA, MB, BChir, AFHEA) is an Academic Clinical Fellow at University College London and Honorary Researcher at the University of Cambridge. He has an intercalated degree in Management Studies from the Cambridge Judge Business School. His research focuses on the use of artificial intelligence and machine learning in healthcare. He holds honorary research affiliations with NHS England and Improvement and Moorfields Eye Hospital, London. His previous research experience includes work with Yale University School of Medicine, Yale University School of Public Health, University College London, Cambridge University Institute of Public Health as well as leading roles in national research collaboratives. As an Honorary Analyst within the central NHS Data and Analytics team, Anmol led novel machine learning analysis of national mental health data using the NHS National Commissioning Data Repository. Anmol is Chair of the HDR UK Impact Committee, Co-Chair of the DARE UK Synthetic Data Working Group and is also a public member of several NIHR panels, including the Artificial Intelligence in Health and Care Award Panel.
Research
Artificial Intelligence, Machine Learning, Generative AI, Systems Design, Healthcare Innovation, Synthetic Data
Publications
A Arora, A Wright, M Cheng, Z Khwaja, M Seah (2021). Innovation Pathways in the NHS: An Introductory Review. Therapeutic Innovation and Regulatory Science. doi.org/10.1007/s43441-021-00304-w
A Arora et al (2023). Understanding the value of standards for health datasets. Nature Medicine. doi.org/10.1038/s41591-023-02608-w
A Arora, A Arora (2022). Synthetic patient data in healthcare: A widening legal loophole. The Lancet. doi.org/10.1016/S0140-6736(22)00232-X A Arora et al (2022). Assessment of machine learning algorithms in national data to classify the risk of self-harm among young adults in hospital: a retrospective study. International Journal of Medical Informatics. doi.org/10.1016/j.ijmedinf.2023.105164
A Arora, A Arora (2022). Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthcare Journal. doi.org/10.7861/fhj.2022-0013
J Gath et al. (2024). Exploring patient and public participation in the STANDING Together initiative for healthcare artificial intelligence (AI) in healthcare. Nature Medicine. doi.org/10.1038/s41591-024-03200-6
A Arora et al. (2024). Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING): A Data Governance Risk Tool. Plos One. dx.doi.org/10.1371/journal.pone.0309308
A Arora & T Lawton (2024). Artificial intelligence in the National Health Service: Moving from Ideation to Implementation. Future Healthcare Journal. doi.org/10.1016/j.fhj.2024.100183
A Arora et al. (2025). The urgent need to accelerate synthetic data privacy frameworks for medical research. The Lancet Digital Health. doi.org/10.1016/s2589-7500(24)00196-1
Alderman et al (2024). Tackling algorithmic bias and promoting transparency in health data: the STANDING Together consensus recommendations. The Lancet Digital Health. doi.org/10.1016/s2589-7500(24)00224-3
Abgrall et al. (2024). Synthetic Data and Health Privacy. JAMA. doi.org/10.1001/jama.2024.25821
Teaching and Supervisions
Supervisor of Undergraduate Physiology at Trinity College, Cambridge (HOM1A)
AI, Tech and Simulation Lead of the PHEG Clinical Educators' Research Group
