Dr Xiaoxuan Liu MBChB, PhD

Department of Applied Health Sciences
Associate Professor in AI and Digital Health Technologies
125th Anniversary Fellow

Contact details

Telephone: +44 (0)121 371 6905
Email: x.liu.8@bham.ac.uk
Twitter: @DrXiaoLiu

Address: Department of Applied Health Sciences
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

View Dr Liu's personal website

Dr Xiaoxuan (Xiao, pronounced "Shau") Liu is an Associate Professor in AI and Digital Health Technologies and an Honorary Clinician Scientist at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust. She is the AI theme lead for the Birmingham Health Partners Centre for Regulatory Science and Innovation.

Dr Liu was appointed a 125^th Anniversary Fellow at the University of Birmingham in August 2024 following her significant number of contributions to reports on AI and health policy. Before this, she was an ophthalmology doctor in the NHS and a Health Scientist at Apple.

Dr Liu and Professor Alastair Denniston co-lead the AI & Digital Health Group, a research and policy group focused on responsible innovation of AI health technologies. The group’s work seeks to ensure AI technologies are safe, effective and equitable, and benefits patients and society. The group’s work includes:

Improving scientific standards - developing internationally adopted reporting guidelines for clinical trials of AI health technologies: SPIRIT-AI and CONSORT-AI; and contributing to other AI reporting standards including TRIPOD+AI, STARD-AI, and DECIDE-AI.
Improving evidence - building evidence standards for digital health technologies for NICE (DHT-ESF), in collaboration with Imperial College London and the Alan Turing Institute.
Improving safety - developing tools for assessing safety of AI-enabled medical devices: the medical algorithmic audit, and through working directly with medical device regulators such as the MHRA.
Improving diversity and representation within the data used in AI - tackling bias in health datasets to mitigate AI-driven health inequalities through STANDING Together.

The group works in collaboration with academic, industry and policy institutions around the world, bringing diverse and interdisciplinary teams together to build best practices that can be translated internationally.

Qualifications

PhD, University of Birmingham, 2017-2021
MBChB, University of Birmingham, 2010-2015

Biography

Xiao studied medicine at the University of Birmingham College of Medical and Dental Sciences and graduated in 2015. She was a foundation doctor at University Hospitals Birmingham NHS Foundation Trust and Sandwell and West Birmingham Hospitals NHS Trust. She completed her PhD at the University of Birmingham between 2017-2021 (“Quantifying Ocular Inflammation In Uveitis Using Optical Coherence Tomography”), where she led the clinical validation of an automated optical coherence tomography imaging technique for detecting and quantifying inflammation in the eye. She has been an ophthalmology specialty doctor in the West Midlands deanery and a senior clinician scientist in AI and Digital Health at University of Birmingham since 2020.

As of August 2024, Dr Liu has been appointed as a 125th Anniversary Fellow at the University of Birmingham following her significant number of contributions to reports on AI and health policy, WHO, G7, DHSC, and the NHS AI lab.

Research

Xiao’s research interests are focused on the translation of scientific evidence for AI and digital health technologies into best practice across research, policy and regulation. Specific areas of research include:

Improving scientific standards
- Leading the AI reporting standards for clinical trial protocols and clinical trial reports: SPIRIT-AI and CONSORT-AI;
- Contributing to other AI reporting standards including TRIPOD+AI, STARD-AI, and DECIDE-AI.
Improving evidence
- Creating the evidence standards for digital health technologies for NICE (DHT-ESF), in collaboration with Imperial College London and the Alan Turing Institute.
Improving safety
- Developing tools for assessing safety of AI-enabled medical devices: the medical algorithmic audit
- Working directly with medical device regulators such as the MHRA
- Working directly with NHS trusts to enable safe governance and implementation of AI
Improving health equity
- Tackling bias in health datasets to mitigate AI-driven health inequalities through STANDING Together.

Publications

Google Scholar Publications

Ganapathi, S., Palmer, J., Alderman, J.E. et al. Tackling bias in AI health datasets through the STANDING Together initiative. Nat Med 28, 2232–2233 (2022).

Sendak M, Vidal D, Trujillo S, Singh K, Liu X and Balu S (2023) Editorial: Surfacing best practices for AI software development and integration in healthcare. Front. Digit. Health 5:1150875.

Liu X, Glocker B, McCradden MM, Ghassemi M, Denniston AK, Oakden-Rayner L. The medical algorithmic audit. The Lancet Digital Health 2022.

Liu, X., Cruz Rivera, S., Moher, D. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med 26, 1364–1374 (2020).

Cruz Rivera, S., Liu, X., Chan, A. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat Med 26, 1351–1363 (2020).

Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. The Lancet Digital Health. Published online September 9, 2020.

Cruz Rivera S, Liu X, Chan A-W, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. The Lancet Digital Health. Published online September 9, 2020.

Liu X, Rivera SC, Moher D, Calvert MJ, Denniston AK. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI Extension. BMJ. 2020;370.

Rivera SC, Liu X, Chan A-W, Denniston AK, Calvert MJ. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI Extension. BMJ. 2020;370.

McNally TW, Liu X, Beese S, Keane PA, Moore DJ, Denniston AK. Instrument-based tests for quantifying aqueous humour protein levels in uveitis: a systematic review protocol. Syst Rev. 2019;8(1):287.

Liu X, Kale AU, Capewell N, et al. Optical coherence tomography (OCT) in unconscious and systemically unwell patients using a mobile OCT device: a pilot study. BMJ Open. 2019;9(11):e030882.

Liu X, Faes L, Kale AU, et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health. September 2019. doi:10.1016/S2589-7500(19)30123-2

CONSORT-AI and SPIRIT-AI Steering Group. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med. September 2019. doi:10.1038/s41591-019-0603-3

Liu X, Faes L, Calvert MJ, Denniston AK, CONSORT/SPIRIT-AI Extension Group. Extension of the CONSORT and SPIRIT statements. Lancet. September 2019. doi:10.1016/S0140-6736(19)31819-7

Faes L, Wagner SK, Fu DJ, et al. Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study. The Lancet Digital Health. 2019;1(5):e232-e242.

Liu X, Solebo AL, Faes L, et al. Instrument-based Tests for Measuring Anterior Chamber Cells in Uveitis: A Systematic Review. Ocular Immunology and Inflammation. 2019:1-12. doi:10.1080/09273948.2019.1640883

Liu X, Kelly SR, Montesano G, et al. Evaluating the Impact of Uveitis on Visual Field Progression Using Large Scale Real-World Data. Am J Ophthalmol. June 2019. doi:10.1016/j.ajo.2019.06.004

Ometto G, Moghul I, Montesano G, et al. ReLayer: a Free, Online Tool for Extracting Retinal Thickness From Cross-Platform OCT Images. Transl Vis Sci Technol. 2019;8(3):25.

Bailie HN, Liu X, Bruynseels A, Denniston AK, Shah P, Sii F. The Uveitis Patient Passport: A Self-Care Tool. Ocul Immunol Inflamm 2019:1–6. doi:10.1080/09273948.2019.1569240.

Liu X, Solebo AL, Keane PA, Moore DJ, Denniston AK. Instrument-based tests for measuring anterior chamber cells in uveitis: a systematic review protocol. Syst Rev 2019;8:30. doi:10.1186/s13643-019-0946-3.

Liu X, Keane PA, Denniston AK. Time to regenerate: the doctor in the age of artificial intelligence. J R Soc Med. 2018;111:113–6. Available from: http://journals.sagepub.com/doi/10.1177/0141076818762648.

Damato EM, Dawson S, Liu X, Mukherjee C, Horsburgh J, Denniston AK, et al. A retrospective cohort study of patients treated with anti-tuberculous therapy for presumed ocular tuberculosis. J Ophthalmic Inflamm Infect. 2017;7:23. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29204932

Montesano G, Way CM, Ometto G, Ibrahim H, Jones PR, Carmichael R, et al. Optimizing OCT acquisition parameters for assessments of vitreous haze for application in uveitis. Sci Rep. Nature Publishing Group; 2018. Available from: http://www.nature.com/articles/s41598-018-20092-y

Liu X, Sii F, Horsburgh J, Shah P. Anuric acute kidney injury due to low dose oral acetazolamide with hypercrystalluria. Clin Experiment Ophthalmol. Wiley/Blackwell (10.1111); 2017;45:927–9. Available from: http://doi.wiley.com/10.1111/ceo.12980

Liu X, Calvert PA, Arif S, Keane PA, Denniston AK. Patent foramen ovale presenting as visual loss. JRSM open. SAGE Publications; 2016;8:2054270416669302. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28203381