Introduction
The Clinical AI interest group seeks to bring together health professionals from diverse healthcare fields and data scientists who have a shared interest in clinical AI and to foster this network through interactive events on up-to-date developments in the field and cultivate innovative research projects.
Explaining the science
The clinical application of medical artificial intelligence is an area of mass interest to researchers, governments and industry and has the potential to improve clinical care. However, if this potential is to be realised, a substantial effort is required to up-skill the clinical workforce and to educate future generations of healthcare professionals in the development, application and pitfalls of clinical AI.
Aims
The Clinical AI interest group will address the following aims:
- Sharing – To provide an incubator for clinicians and data scientists to share expertise in the design and methodology of clinical AI, share experiences of applying clinical AI and share in the collaborative, innovative application of AI in clinical practice.
- Pooling – To draw together a community of AI-interested clinicians and health-interested data scientists. All interested parties are invited to apply with an aim to communicate and collaborate across disciplines. This pool of individuals will be encouraged to feed into the cross-sector activities of the health and medical sciences programme, offering unique insights from the clinical front line in terms of the opportunities and potential for AI to solve clinically important problems’.
- Educating – To build training materials and facilitate educational events in order to educate AI-naïve clinicians and student healthcare professionals in the application of clinical AI. It is hoped that this double-impact approach will bring in those on the fringes and arm the next generation.
Talking points
What are the current examples of AI being used in clinical practice and is it supported by robust evidence?
Challenge: In recent times there has been an explosion in the development of AI algorithms for use in clinical practice. These may be undoubtably useful, but still require robust assessment and evaluation.
How could AI be used in the future to benefit clinical practice?
Challenge: With rapid developments in AI and the growth of their adoption in the clinical realm it is crucial to identify the future direction of travel and new areas of usage.
What are the ethical pitfalls in deploying AI in clinical practice and how can they be corrected?
Challenge: AI systems can learn and perpetuate existing biases within the health system and widen health inequalities. As more AI systems are developed and adopted, strategies need to be in place to ensure it benefits all patients and public, including those in minoritised and marginalised groups.
Supra-interest groups
We have formed supra-interest groups in different clinical specialties to bring together clinicians and AI experts in a more focused manner.
The current supra-interest groups are:
Anaesthetics and Intensive Care - led by Sandy Jackson, Lei Lu and Allan Pang.
The anaesthetics and intensive care supra-interest group focuses on all applications of AI around the time of surgery or other critical illness. These are data rich environments with both cost and complexity built in. These make them a prime target for clinical AI in numerous potential roles from closed loop systems to risk evaluation and the integration of novel sensors and monitoring. Get in touch if you are interested in hearing more or getting involved.
Medical Imaging and Computer Vision - led by Alexander Deng
The Medical Imaging and Computer Vision supra-interest group has a mission to feed its members’ curiosity.
This will take the form of live interactive debates on the most thought-provoking topics in this space, as well as case presentations from the front line of AI deployments in clinical workflows.
AI for Women's Health - led by Bianca Schor, Kristin Collett Caolo and Annalisa Occhipinti
The AI for women's health supra-interest group is an interdisciplinary community (e.g. clinicians, academics, data scientists, designers, health care professionals, students etc) interested in advancing women's health and how to leverage AI to do so.
The groups goals include:
- Building an interdisciplinary and diverse community of individuals working or interested to work in women's health with (or without) AI
- Bringing visibility to research on women's health and how AI can help to do so
- Facilitating collaborations and bringing more people to the field
- Offering relevant events & trainings, and sharing resources with the community
Pathology - led by Chris Banerji
Pathology is an extremely data rich clinical discipline spanning diverse modalities at multiple scales, from genomics and gene expression to cytology, tissue morphology and anatomy. This complex information is increasingly digitised (e.g., by whole slide imaging) and stored in large, often public repositories, paving the way for AI approaches to improve diagnosis, prognostication, treatment stratification and the health of our patients. This supra-interest group welcomes anyone interested in applying AI technologies to problems in pathology and overcoming the barriers to their clinical translation.
Public Health - led by Alisha Davies, Elliott Roy-Highley and Samantha Field
The Public Health supra-interest group focuses on the development and application of AI to prevent disease, prolong life, and promote, protect and improve health. The supra group enables sharing knowledge and ideas, pooling skills and learning across domain experts, data science and AI researchers, to advance the development of safe, ethical, responsible, and equitable AI across the three domains of public health.
NeuroAI - led by Jordan Tsigarides, Beatrice Panico, Danyal Khan
The NeuroAI supra-interest group seeks to celebrate, connect and catalyse innovations in artificial intelligence applied to neuroscience. It is an interdisciplinary collaboration spanning data science, computer science, the clinical neurosciences (including neurology and neurosurgery), neurotechnology, regulation and industry. Join us during our regular webinars, network events and collaborative database.
MSK AI - led by Nick Fuggle, Jordan Tsigarides, Meghna Jani, Salah Hammouche, Justin Green, Abhinav Singh, Luke Farrow and Samantha Cooray.
The MSK AI Supra-Interest Group is a vibrant community dedicated to advancing artificial intelligence in the field of musculoskeletal diseases. Our group fosters interdisciplinary collaboration, bringing together a diverse background of individuals including data science, computer science, rheumatology, orthopaedics, regulation, and industry. We aim to celebrate, connect, and catalyse innovative AI solutions for musculoskeletal health. Participate in our regular webinars and networking events (upcoming) to stay at the forefront of AI-driven advancements in this vital area.
How to get involved
Please complete the form below to join the Clinical AI Interest Group.
You can indicate your interest in one of the supra-interest groups on our membership form.
Once you have joined, you will be sent a monthly newsletter in which you can find out more about our activities and events, and you will be invited to join our slack workspace.
Click here to join us and request sign-up
YouTube playlist
Follow the link to access past seminars on the group's playlist.
The group also organised a summer school in July 2023. You can watch the presentations here.
Recent updates
We have started a Clinical AI Skills project - details can be found here: https://github.com/alan-turing-institute/Clinical-AI-skills
Organisers
Dr Alisha Davies
Head of Research and Evaluation, Public Health Wales and Co-chair of Clinical AI Interest GroupDr Joseph Alderman
Anaesthesia & intensive care registrar at University Hospitals Birmingham and Researcher in AI and digital health at University of BirminghamDr Emma Karoune
Senior Researcher - Research Community Building | Tools, Practices and SystemsCollaborators
Shakir Laher
Research Application ManagerBianca Schor
PhD student and organiser of the AI for women's health supra-interest groupDr Alexander Deng
Programme Lead for Fellowships in Clinical Artificial Intelligence, Clinical Scientific Computing, Guy’s and St Thomas’s NHS Foundation TrustDr Sandy (Alexander) Jackson
NIHR Doctoral Fellow, Specialist Registrar in Anaesthetics and Intensive Care Medicine, Perioperative and Critical Care Theme, NIHR Southampton BRC, University of Southampton and University Hospital SouthamptonKristin Collett Caolo
PhD student and organiser of the AI for Women's Health supra-interest GroupDr Annalisa Occhipinti
Associate Professor at Teesside University, Co-organiser of the AI for Women's Health Supra-interest groupDr Samantha Field
National Medical Director's Clinical Fellow and co-chair of the Faculty of Public Health’s AI and Digital Health Special Interest GroupDr Elliott Roy-Highley
Specialist registrar in public health medicine, co-chair of the AI & Digital Public Health Special Interest Group of the Faculty of Public Health.Dr Maria Beatrice Panico
Head of the clinical team at Scendea and co-organiser of the NeuroAI Supra-interest groupDr Jordan Tsigarides
Senior Clinical Fellow (Rheumatology) and co-organiser of the NeuroAI Supra-interest groupDr Danyal Khan
Neurosurgery trainee (London Deanery), NIHR Academic Clinical Fellow (UCL) and co-organiser of the NeuroAI Supra-interest groupDr Lei Lu
Lecturer in Health Data Science and AI at King’s College LondonDr Allan Pang
Military anaesthesia speciality trainee and a PhD student at the UKRI Centre for Doctoral Training in Artificial Intelligence for Medical Diagnosis and Care at the University of LeedsContact info
Please use the link above to join our group mailing list, but for other enquiries please contact the group organiser - [email protected]