Our AI for Healthcare Centre based at Imperial College London creates a unique environment for research and aims to focus on AI in healthcare which drives current AI research avenues such as interpretable AI, privacy-preserving learning, trust in AI, data-efficient learning, and safety in autonomy. Our healthcare application areas range from discovery research through diagnosis, imaging, precision medicine, clinical interventions, robotics, rehabilitation, public health, and long-term care.
Multi-disciplinarity is at the heart of all AI4Health research projects. PhD students work with a team of supervisors, each of whom brings complementary expertise to the project and the training programme. Research happens through high-quality projects with stretch and ambition in both AI and health research domains. The AI research is original in used methods and types of inputs or outputs tackled, and the majority of the projects have the potential to have an impact post-PhD.
Each CDT student has at least two supervisors – one AI supervisor and one Healthcare supervisor who are normally from two different Imperial College Faculties or Departments. The true cross-disciplinary nature of the projects is represented in the large pool of supervisors which currently comprises most Imperial College Engineering departments including Computing, Electrical and Electronic Engineering, Mathematics, Bioengineering, Mechanical Engineering, Dyson School of Design Engineering and all 8 departments of the Imperial College Faculty of Medicine. Additional healthcare supervisors can be from clinicians in the Imperial College Healthcare NHS Trust or Royal Marsden NHS Trust.
Clinical PhD Fellows
Sumeet Hindocha, MD
(PhD awarded 2023)
Project:
RadiotherapAIsER: Radiotherapy AI
to see Early Recurrence
Supervisors:
Eric Aboagye
Richard Lee
Adam Marcus, MD
(PhD awarded 2024)
Project:
ABCD: Acute Brain
CT Deep-learning
Supervisors:
Daniel Rueckert
Paul Bentley
Myura Nagendran, MD
Project:
Explainable AI in Critical Care
Supervisors:
Aldo Faisal
Anthony Gordon
Simon Williamson, MD
Project:
Developing neuromorphic AI-based closed-loop control brain stimulation to treat Alzheimer’s disease
Supervisors:
Pedro Mediano
Nir Grossman
Christopher Butle
Kavitha Vimalesvaran, MD
Project:
A real-time AI approach to improve the efficiency and quality of image acquisition
Supervisors:
Anil Bharath
Graham Cole
Sameer Zaman, MD
(PhD awarded 2023)
Project:
Machine learning to predict death from cardiac MRI images and reports
Supervisors:
Nick Linton
Darrel Francis
Samuel Channon-Wells, MD
Project:
Development of a new data driven molecular taxonomy of infectious and inflammatory disease
Supervisors:
Mauricio Barahona
Jethro Herberg
All our Doctoral Researchers
2019 Cohort
James Batten
Project:
Automating the co-registration between CTA-derived coronary artery geometry and X-ray Angiography
Supervisors:
Ben Glocker
Declan O’Regan
Heartflow
Digby Chappell
(PhD awarded 2024)
Project:
Using machine learning and augmented reality to improve prosthetic hand control
Supervisors:
Nicolas Rojas
Petar Kormushev
Fernando Bello
Sumeet Hindocha, MD
(PhD awarded 2023)
Project:
RadiotherapAIsER: Radiotherapy AI to see Early Recurrence
Supervisors:
Eric Aboagye
Richard Lee
Annalaura Lerede
(PhD awarded 2024)
Project:
Developing machine learning methods for deriving individually optimised markers of pathology for people with Multiple Sclerosis
Supervisors:
Adam Hampshire
Richard Nicholas
Rachel Lee Mekhtieva
Project:
Cross-Modal Deep Learning with Symbolic Reasoning for Patient-Centric Personalised and Precision Healthcare and Clinical Decision Support
Supervisors:
Alessandra Russo/Dalal Alrajeh
Brendan Delaney
Benjamin Post, MD
Project:
AI-based identification of serious acute illness risk from routine primary care data
Supervisors:
Aldo Faisal
Stephen Brett
Margherita Rosnati
(PhD awarded 2024)
Project:
Predicting outcomes following traumatic brain injury (TBI) using deep learning
Supervisors:
Ben Glocker
David Sharp
Ruby Sedgwick
(PhD awarded 2024)
Project:
Using Bayesian inference to guide the experimental design and interpretation of a novel amplified molecular assay
Supervisors:
Ruth Misener
Mark van der Wilk
Molly Stevens
Reneira Seeamber
(PhD awarded 2024)
Project:
To develop a method to perform self-directed back exercises using novel wearable sensors, haptics and interactive mobile exercise-games
Supervisors:
Ravi Vaidyanathan
Paul Bentley
Alina-Irina Serban
(PhD awarded 2024)
Project:
Improving Life Satisfaction in Independent Living: An Intelligent IoT approach
Supervisors:
Rafael Calvo
David Sharp
Kavitha Vimalesvaran, MD
Project:
A real-time AI approach to improve the efficiency and quality of image acquisition
Supervisors:
Anil Bharath
Graham Cole
Sameer Zaman, MD
(PhD awarded 2023)
Project:
Machine learning to predict death from cardiac MRI images and reports
Supervisors:
Nick Linton
Darrel Francis
2020 Cohort
Ioannis Afentakis
Project:
Machine Learning Algorithms for Diabetes Management
Supervisors:
Pantelis Georgiou
Nick Oliver
Asem M. Alaaeldin Abdelaziz
Project:
Data Science for Cancer Pathway Analysis
Supervisors:
Mauricio Barahona
Erik Mayer
Nur Aizaan Binti Anwar, MD
Project:
Using longitudinal speech samples to predict disease recurrence in patients with brain tumours
Supervisors:
Lucia Specia
Matthew Williams
Doga Basaran
Project:
Characterisation of Multiple Sclerosis Lesions with Image Modality Transfer and Graph-Based Learning
Supervisors:
Wenjia Bai
Paul Matthews
William Bolton
Project:
Addressing multi-morbidity in the NHS: Optimising antibiotic therapy in obesity using intelligent, personalised clinical decision support systems
Supervisors:
Pantelis Georgiou
Alison Holmes
Xavier Cadet
Project:
Multi-modal machine learning of large-scale genetic, biologic, and brain imaging data for identification of therapeutic targets and early prediction biomarkers for dementia
Supervisors:
Hamed Haddadi
Sara Ahmadi Abhari
William Dudley
Project:
Human not monkey robotics: Neuro-inspired object manipulation with dextrous hands
Supervisors:
Aldo Faisal
David Franklin
Ariane Duverdier
Project:
AI-based design of personalised treatment strategies for eczema and allergic march
Supervisors:
Reiko Tanaka
Adnan Custovic
Stefano Falini, MD
Project:
Understanding and Embracing Uncertainty in Clinical Decision Making
Supervisors:
Lucia Specia
Anthony Gordon
Paul Festor
Project:
Learning clinical decision support systems in acute care settings
Supervisors:
Aldo Faisal
Matthieu Komorowski
Christoforos Galazis
Project:
Prediction of stroke and heart failure risk using dynamic CINE MRI and deep learning of cardiac structure and function
Supervisors:
Anil Bharath
Marta Varela Anjari
Agnese Grison
Project:
Wearable Interfaces for the 21st Century: Subcutaneous high-density electrode arrays to connect with the human spinal cord
Supervisors:
Dario Farina
Stephen Wang
Annika Guez
Project:
Sensory Motor Interface for Lower extremity Exoskeletons (SMILE)
Supervisors:
Ravi Vaidyanathan
Klaus Drechsler
Megan Hutchings
Project:
Unsupervised Machine Learning for the Inference and Rehabilitation of Cortical Injury in Stroke Patients
Supervisors:
Dario Farina
Paul Bentley
Adam Marcus, MD
Project:
ABCD: Acute Brain CT Deep-learning
Supervisors:
Daniel Rueckert
Paul Bentley
Amr Nimer, MD
Project:
Behaviour Analytics to quantify Neurosurgical Skill to Enhance Training and Economy of Movements
Supervisors:
Aldo Faisal
Dipankar Nandi
Edoardo Occhipinti
Project:
Hearables: AI for in-ear doctorless cardiovascular care in the community
Supervisors:
Danilo Mandic
Nicholas Peters
William Plumb
Project:
Understanding and optimising patient journeys within an integrated care system
Supervisors:
Giuliano Casale
Alex Bottle
Neophytos Polydorou
Project:
A Virtual Reality environment with emotion recognition and coaching capability for psychotherapy of depression and anxiety
Supervisors:
Abbas Edalat
Dasha Nicholls
Hadrien Reynaud
Project:
Medical image acquisition guidance with applications in free-hand ultrasound
Supervisors:
Bernhard Kainz
Paul Leeson
Joshua Southern
Project:
Computational personalisation and generation of health-promoting hyper foods using graph deep learning
Supervisors:
Michael Bronstein
Kirill Veselkov
Michael Tanzer
Project:
Artificial Intelligence enabled highly efficient Diffusion Tensor Cardiac Magnetic Resonance
Supervisors:
Daniel Rueckert
Sonia Nielles-Vallespin
Guang Yang
Michael Thornton
Project:
Decoding selective attention to speech for a mind-controlled hearing aid
Supervisors:
Danilo Mandic
Tobias Reichenbach
Alistair Weld
Project:
Confidence-driven Robotic Ultrasound Tissue Scanning for Surgical Resection Guidance
Supervisors:
Stamatia Giannarou
Alin Albu-Schaeffer
Kai Zhang
Project:
Developing interactive explanatory models for cancer prognosis
Supervisors:
Francesca Toni
Matthew Williams
Blanka Zicher
Project:
Learning to control individual motoneurons with AI-based decoded spinal cord output
Supervisors:
Dario Farina
Dan Wetmore
2021 Cohort
João Binenbojm De Sa Pereira
Project:
Closed-loop brain stimulation for induction of targeted functional brain plasticity in neurological patients
Supervisors:
Dario Farina
Paul Bentley
Lucille Cazenave
Project:
AI Therapist: Personalised Data Driven Robot-Assisted Neurorehabilitation
Supervisors:
Etienne Burdet
Paul Bentley
Dimitar Georgiev
Project:
Deep learning for cerebral organoid phenotyping and drug discovery
Supervisors:
Mauricio Barahona
Molly Stevens
Simon Hanassab
Project:
Using AI to improve decision making during in vitro fertilisation (IVF) treatment
Supervisors:
Thomas Heinis
Waljit Dhillo
Alexander Jenkins
Project:
Individualising patient treatment for atrial fibrillation based on the fibrillation mechanism and electrophenotype
Supervisors:
Danilo Mandic
Fu Siong Ng
Federico Nardi
Project:
Data-Driven Neuroscience for Diagnostics & Neurorehabilitation of Parkinson’s with Virtual Reality
Supervisors:
Aldo Faisal
Shlomi Haar
Alexander Ranne
Project:
AI-Driven Robotic Catheter System for Ultrasound-Guided Endovascular Surgery
Supervisor:
Ferdinando Rodriguez y Baena
Nassir Navab
Avish Vijayaraghavan
Project:
Multi-modal deep learning and domain knowledge integration to aid multidisciplinary teams in diagnosing in idiopathic pulmonary fibrosis
Supervisors:
Joram M. Posma
Philip Molyneaux
Weitong Zhang
Project:
Motion-Corrected Quantitative Body MRI (MoCoQ)
Supervisors:
Bernhard Kainz
Dimitrios Karampinos
2022 Cohort
Pauline Bourigault
Project:
Pose Estimation of body motion for brain disease Diagnosis (DPosED)
Supervisors:
Danilo Mandic
Barry Seemungal
Samuel Channon-Wells
Project:
Development of a new data driven molecular taxonomy of infectious and inflammatory disease
Supervisors:
Mauricio Barahona
Jethro Herberg
Sarah Cechnicka
Project:
Deep Learning–Based Segmentation and Classification for Outcome prediction in Kidney Transplant
Supervisors:
Bernhard Kainz
Candice Roufosse
Cosima Graef
Project:
AI System for Optimal Selection of Deep Brain Stimulation (DBS) Parameters
Supervisors:
Ravi Vaidyanathan
Yen F. Tai
Shlomi Haar
Kevin Horeau
Project:
Data-driven Digital Twins for the discovery of the link between shopping behaviour and onset of cancer
Supervisors:
Aldo Faisal
James Flanagan
Ruoyu Hu
Project:
An AI platform for learning to laugh: New approach to develop a sense of humour
Supervisors:
Abbas Edalat
Dasha Nicholls
Edison Liu
Project:
Reinforcement learning based management and control of paediatric intensive care ventilation
Supervisors:
Aldo Faisal
Padmanabhan Ramnarayan
Nina Merino Miralles
Project:
Inferring states of the central nervous system from muscle signals to study neural degeneration
Supervisors:
Dario Farina
Paul Strutton
Nina Moutonnet
Project:
Automatic seizure detection and monitoring in hospitalised patients using artificial intelligence
Supervisors:
Danilo Mandic
Gregory Scott
Georgios Papadopoulos
Project:
Deep learning for hyperplexed biosensor diagnostics
Supervisors:
Mauricio Barahona
Molly Stevens
Giulia Sanguedolce
Project:
Automated community assessment of aphasic stroke (The ACOUSTICS study)
Supervisors:
Patrick Naylor
Fatemeh Geranmayeh
Niro Yogendran
Project:
Radar passive monitoring of movement symptoms in Deep Brain Stimulation patients
Supervisors:
Tim Constandinou
Shlomi Haar
Yen F. Tai
2023 Cohort
Barbora Barancikova
Project:
Machine learning algorithms to model brain tumour patients’ dynamics using rough analysis
Supervisors:
Matthew Williams
Christopher Salvi
Seema Dadhania
Nicolas Calvo Peiro
Project:
Using AI for finding digital biomarkers for closed-loop deep brain stimulation
Supervisors:
Shlomi Haar
Anastasia Borovykh
Yen Tai
Daolong Chen
Project:
BehaviourGPT: Large Behaviour Models to measure health state in neurological diseases
Supervisors:
Aldo Faisal
Richard Festenstein
Connor Daly
Project:
Intra-Operative Sensing for Human-Robot Interaction in Neurosurgery
Supervisors:
Ferdinando Rodriguez Y Baena
Daniel Elson
Jinendra Ekanayake
Oskar Fraser-Krauss
Project:
Dynamic graph machine learning for early detection and characterisation of antimicrobial resistant outbreaks from acute care data
Supervisors:
Mauricio Barahona
Alison Holmes
Adam Gould
Project:
Arguing with physical activity data from a real-world wearable clinical trial with patients with a primary brain tumour
Supervisors:
Francesca Toni
Matthew Williams
Seema Dadhania
Isaac Hayden
Project:
Artificial neural networks for carboplatin dose optimisation for children with cancer
Supervisors:
Anil Bharath
Leo Huang
Project:
Automated evaluation of eczema severity scores for any skin colour in clinical and research practice in children and adolescents
Supervisors:
Reiko Tanaka
Claudia Gore
Adnan Custovic
Oliver Pitts
Project:
Physics informed machine learning to improve imaging-based quantification of airway structural and functional heterogeneities in lung disease
Supervisors:
Rossella Arcucci
Anand Shah
Salman Siddiquie
Jonathan Rubin
Project:
Lineage Inference and lineage-regularized embedding for studying metastatic cancer evolution during therapy
Supervisors:
Nick Jones
Trevor Graham
Marco Visentin
Project:
Trustworthy Machine Learning for Thyroid Image Analysis and Diagnosis
Supervisors:
Wenjia Bai
Aimee Di Marco
Neill Tolley
Simon Williamson, MD
Project:
Developing neuromorphic AI-based closed-loop control brain stimulation to treat Alzheimer’s disease
Supervisors:
Pedro Mediano
Nir Grossman
Christopher Butler
Fiona Kekwick
Project:
Robust, Trustworthy and Efficient AI for Clinical Neuroimaging Data Analysis
Supervisors:
Wenjia Bai
Paul Matthews
Gaya Mathusuthan
Project:
Deep Phenotyping of Clinical Deterioration
Supervisors:
Aldo Faisal
Paul Bentley
Our Affiliates
Patrik Bachtiger, MD
Project:
Point-of-care (digital stethoscope) diagnosis of heart disease using artificial intelligence applied to single-lead ECG and heart sounds
Supervisor:
Nicholas Peters
Finneas Catling, MD
Project:
Early diagnosis and improved treatment of critical illness, using methods from Bayesian statistics and machine learning
Supervisor:
Stephen Brett
Manish George, MD
Project:
Artificial Intelligence Stratification of Thyroid
Ultrasound
Supervisors:
Lord Ara Darzi
Neil Tolley
Anenta Ramakrishnan, MD
Project:
Cardiovascular trials
and epidemiology
Supervisors:
Anil Bharah
Jamil Mayet
Peter Weinberg
Arunashis Sau, MD
Project:
Artificial intelligence to
identify mechanisms an
electrophenotypes of atrial
arrhytmias
Supervisors:
Danilo Mandic
Fu Siong Ng
Nicholas Peters