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.
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.
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.