Xiao Liu MBChB PhD
I am a clinician scientist working on AI and digital health technologies. In particular, I work on the translation of scientific evidence into best practice across research, policy and regulation. In my clinical domain, I am an ophthalmologist in the UK NHS and continue to practice from time to time.Alastair Denniston and I co-lead the AI and Digital Health Policy and Research Group at University Hospitals Birmingham/University of Birmingham, where we focus on three specific aspects of AI in health:
Hi, I'm Xiao (Xiaoxuan) Liu. Xiao is pronounced "Shau".I am an ophthalmology doctor and a researcher at University Hospitals Birmingham NHS Foundation Trust and The University of Birmingham.I co-led the SPIRIT-AI & CONSORT-AI initiative in 2020: reporting standards for clinical trials evaluating AI systems - EQUATOR Network endorsed guidelines, which have since been adopted by international medical device regulators and medical journals including Nature Medicine and the Lancet. I am also on the steering group for DECIDE-AI, TRIPOD+AI and STARD-AI, reporting guidelines for early feasibility studies, prediction and prognostic models and diagnostic accuracy studies for AI, respectively.I am currently working on STANDING Together - a project on tackling bias in health datasets to ensure AI benefits all. STANDING Together provides guidance for documentation of datasets to promote transparency, as well as guidance on how to address these biases and mitigate risks to minorities
and underserved groups in the context of AI as a Medical Device.I have worked with a number of health policy institutions on their approach to evaluating AI in healthcare, including the WHO, MHRA, NICE, NHS England, BSI and the NHS AI Lab.I am a Deputy Editor at NEJM-AI. A new journal focused on identifying and evaluating state-of-the-art applications of artificial intelligence to clinical medicine.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.
SAIL 2023, 9-12th May 2023 - Panel: AI in Clinical TrialsWHO/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
Full list of publications here.The value of standards for health datasets in artificial intelligence-based applications Arora M, Alderman JE, Palmer, J, Ganapathi S, Laws E, ...Liu X. Nature Medicine 2023.What's fair is… fair? Presenting JustEFAB, an ethical framework for operationalizing medical ethics and social justice in the integration of clinical machine learning: JustEFAB McCradden M, Odusi O, Joshi S, Akron I, et al. FAccT 2023.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.Editorial: Surfacing best practices for AI software development and integration in healthcare Sendak M, Vidal D, Trujillo S, Singh K, Liu X and Balu S. Front. Digit. Health 2023.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.