Professor
David Clifton
MEng DPhil (Oxon)
Royal Academy of Engineering Chair of Clinical Machine Learning
Email:
Tel: 01865 617708
College: Reuben College
Location: Institute of Biomedical Engineering, Old Road Campus Research Building, Oxford OX3 7DQ

Professor David Clifton is the Royal Academy of Engineering Chair of Clinical Machine Learning at the University of Oxford, and leads the Computational Health Informatics (CHI) Lab which focuses on “AI for Healthcare”.  He is also NIHR Research Professor, appointed as the first non-medical scientist to the NIHR’s “flagship chair”.

He is OCC Fellow in AI & ML at Reuben College, a Research Fellow of the Royal Academy of Engineering, Fellow of the Alan Turing Institute, Visiting Chair in AI for Health at the University of Manchester, and a Fellow of Fudan University, China.

He studied Information Engineering at Oxford’s Department of Engineering Science, supervised by Professor Lionel Tarassenko CBE. His previous research resulted in patented systems for jet-engine health monitoring, used with the engines of the Airbus A380, the Boeing 787 “Dreamliner”, and the Eurofighter Typhoon. Since 2008, he has focused mostly on the development of AI-based methods for healthcare. His research has been commercialised via university spin-out companies OBS Medical, Oxehealth, Biobeats, and Sensyne Health, in addition to collaboration with multinational industrial bodies.

In 2018, the CHI Lab opened its second site, in the Oxford University-owned research labs in Suzhou (China), which focuses on open research in “Digital Health” using public data.  In 2019, the Wellcome Trust’s first “Flagship Centre” was announced, which joins CHI Lab to the Oxford University Clinical Research Unit in Vietnam, focused on AI for healthcare in low-income countries.  In 2021, the Oxford-CityU Centre for Cardiovascular Engineering was opened in Hong Kong, of which he is co-director.  In 2022, the Pandemic Sciences Institute opened at Oxford, of which he is an investigator and where CHI Lab provides its AI theme.

His research has won over 40 awards; he is a Grand Challenge awardee from the UK Engineering and Physical Sciences Research Council, which is an EPSRC Fellowship that provides long-term strategic support for nine “future leaders in healthcare.” He was joint winner of the inaugural “Vice-Chancellor’s Innovation Prize”, which identifies the best interdisciplinary research across the entirety of the University of Oxford.  He was the recipient of the IEEE Early Career Award in 2022, given to one engineer annually for achievements within the first ten years of their academic career.  He has previously taught widely across Oxford undergraduate and graduate courses in mathematics, statistics, and machine learning.

Research in the Computational Health Information (CHI) Laboratory focuses on the development of real in-hospital and in-home systems for AI-driven interventions that are used in practice. Translation into low- and middle-income countries (LMICs) is a parallel research theme.  The lab has a focus on non-imaging AI methods: time-series analysis, natural language processing, -omics data, and sensor informatics.

Please see our lab research page for details.

CHI Lab offers a wide range of complex, real-world projects in AI for healthcare.  Please contact Professor Clifton for enquiries.

Most doctoral students in the CHI Lab hold highly competitive scholarships (Rhodes, Clarendon, EU, etc.) for which Professor Clifton is happy to offer advice.

A Multimodal Large Language Modelling Deep Learning Framework for the Future Pandemic
Liu F,  Zhu T,  Wu X,  Yang B,  You C,  Wang C,  Lu L,  Liu Z,  Zheng Y,  Sun X,  Yang Y,  Clifton D,  et al. (2024)
A scalable federated learning solution for secondary care using low-cost microcomputing: privacy-preserving development and evaluation of a COVID-19 screening test in UK hospitals
Soltan AS,  Thakur A,  Yang J,  Chauhan A,  D'Cruz LG,  Dickson P,  Soltan MA,  Thickett DR,  Eyre DW,  Zhu T,  Clifton DA,  et al. (2024)
Predicting future hospital antimicrobial resistance prevalence using machine learning
Vihta K-D,  Pritchard E,  Pouwels KB,  Hopkins S,  Guy RL,  Henderson K,  Chudasama D,  Hope R,  Muller-Pebody B,  Walker AS,  Clifton D,  Eyre DW,  et al. (2024)
Acceptance and User Experiences of a Wearable Device for the Management of Hospitalized Patients in COVID-19-Designated Wards in Ho Chi Minh City, Vietnam: Action Learning Project.
Luu AP,  Nguyen TT,  Cao VTC,  Ha THD,  Chung LTT,  Truong TN,  Nguyen Le Nhu T,  Dao KB,  Nguyen HV,  Khanh PNQ,  Le KTT,  Tran LHB,  Nhat PTH,  Tran DM,  Lam YM,  Thwaites CL,  Mcknight J,  Vinh Chau NV,  Van Nuil JI,  Vietnam ICU Translational Applications Laboratory (VITAL) ,  et al. (2024)
Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers
Pollock KG,  Dickerson C,  Kainth M,  Lawton S,  Hurst M,  Sugrue DM,  Arden C,  Davies DW,  Martin A-C,  Sandler B,  Gordon J,  Farooqui U,  Clifton D,  Mallen C,  Rogers J,  Hill NR,  Camm AJ,  Cohen AT,  et al. (2024)
Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach
Zankin A,  Clifton D,  Crook D,  Earle S,  Fowler P,  Giberto Cruz A,  Hoosdally S,  Knaggs J,  Hunt M,  Kouchaki S,  Lachapelle A,  Peto T,  Rodger G,  Roohi A,  Thwaites G,  Walker AS,  Wilson D,  Yang Y,  et al. (2024)
Tetanus severity classification in low-middle income countries through ECG wearable sensors and a 1D-vision transformer
Lu P,  Wang Z,  Ha Thi HD,  Hai HB,  Thwaites L,  Clifton DA,  et al. (2024)
Digital health technologies and machine learning augment patient reported outcomes to remotely characterise rheumatoid arthritis.
Creagh AP,  Hamy V,  Yuan H,  Mertes G,  Tomlinson R,  Chen W-H,  Williams R,  Llop C,  Yee C,  Duh MS,  Doherty A,  Garcia-Gancedo L,  Clifton DA,  et al. (2024)
Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification
Lu L,  Zhu T,  Ribeiro AH,  Clifton L,  Zhao E,  Zhou J,  Ribeiro ALP,  Zhang Y-T,  Clifton DA,  et al. (2024)
Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification
Lu L,  Zhu T,  Ribeiro AH,  Clifton L,  Zhao E,  Zhou J,  Ribeiro ALP,  Zhang Y-T,  Clifton DA,  et al. (2024)