Xiao Liu MBChB PhD

I am an ophthalmology doctor and clinician scientist working on AI health technologies. I am based at University Hospitals Birmingham/University of Birmingham, UK.My research on AI in healthcare focuses on three main areas:
1) clinical evaluation and regulation
2) safety, errors and harms
3) fairness and equity

About me

Hi, I'm Xiao (Xiaoxuan) Liu. Xiao is pronounced "Shau".I am an ophthalmology doctor in Birmingham, UK and an academic researcher at University Hospitals Birmingham NHS Foundation Trust and The University of Birmingham, in a research group working on AI & Digital Healthcare. Our goal is to ensure AI and digital health technologies are safe, effective and equitable.I co-led the SPIRIT-AI & CONSORT-AI initiative in 2020: reporting standards for clinical trials evaluating AI systems - these are EQUATOR Network endorsed guidelines, which are now adopted by international medical device regulators and medical journals including Nature Medicine and the Lancet.I am currently working on STANDING Together - a project on tackling bias in healthcare datasets to ensure AI benefits all.I work/have worked with a number of health policy institutions on their approach to evaluating AI in healthcare, including the MHRA, NICE, WHO, BSI and the NHS AI Lab.Prior to this, I completed my PhD on automated imaging-based methods for measuring inflammation in the eye, under the supervision of Alastair Denniston, Pearse Keane and David Moore.My work has been featured in Wired, The Guardian, BBC Radio, The New Scientist and other news outlets, and recognised as Top Notable Advances of 2019 and 2020 by Nature Medicine.


(Upcoming) WHO/ITU Focus Group for AI in Health conference - 21-24th March 2023, MIT/Harvard Cambridge - Workshop on “Deployment of AI technology in real-world settings”February 2023: British Institute of Radiology Women in Imaging - "First do no harm: a responsible approach to AI in health".June 2022: ACM FAccT CRAFT Session Panel on Communication Across Communities in Machine Learning Research and Practice.April 2022: Intelligent Health UK - STANDING together - "developing standards for datasets underpinning AI systems so they are diverse, inclusive and can work across all demographic groups".April 2022: University of Michigan Learning Health Sciences Collaboratory - "Medical AI - Raising the Bar on Evidence Standards".December 2021: ML4H - "Evidence Standards for AI in Healthcare" - available onlineNovember 2021: Royal Statistical Society - "Statistical literacy in health research in the age of machine learning and artificial intelligence".June 2021: CogX Panel - "Paving the future: Developing a pipeline for AI in health and social care" - available online


The Alan Turing Institute
Special Interest Group for Clinical AI - Co-lead
Data Science for Health Equity
Standards for Health Data Equity - Theme Lead

Selected Publications

Full list of publications here.Tackling bias in AI health datasets through the STANDING Together initiative
Ganapathi S, Palmer J, Alderman JE, Calvert M, Espinoza C, Gath J, Ghassemi M, Heller K, Mckay F, Karthikesalingam A, Kuku S, Mackintosh M, Manohar S, Mateen BA, Matin R, McCradden M, Oakden-Rayner L, Ordish J, Pearson R, Pfohl SR, Rostamzadeh N, Sapey E, Sebire N, Sounderajah V, Summers C, Treanor D, Denniston AK & Liu X
Nature Medicine 2022.
The Medical Algorithmic Audit
Liu X, Glocker B, McCradden MM, Ghassemi M, Denniston AK, Oakden-Rayner L.
The Lancet Digital Health 2022.
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK & The SPIRIT-AI and CONSORT-AI Working Group.
Nature Medicine 2020.
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Rivera SC, Liu X, Chan A-W, Denniston AK, Calvert MJ & The SPIRIT-AI and CONSORT-AI Working Group.
Nature Medicine 2020.
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Liu X, Faes L, Kale AU, Wagner SK, Fu DJ, Bruynseels A, Mahendiran T, Moraes G, Shamdas M, Kern C, Ledsam JR, Schmid MK, Balaskas K, Topol EJ, Bachmann LM, Keane PA, Denniston AK.
The Lancet Digital Health 2019.
Health data poverty: an assailable barrier to equitable digital health care
Ibrahim H, Liu X, Zariffa N, Morris AD, Denniston AK.
The Lancet Digital Health 2021.
A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability
Khan SM, Liu X, Nath S, Korot E, Faes L, Wagner SK, Keane PA, Sebire NJ, Burton MJ, Denniston AK.
The Lancet Digital Health 2021.
Characteristics of publicly available skin cancer image datasets: a systematic review
Wen D, Khan SM, Ji Xu A, Ibrahim H, Smith L, Caballero J, Zepeda L, de Blas Perez C, Denniston AK, Liu X, Matin RN.
The Lancet Digital Health 2022.