AIMI Journal Club: Alastair Denniston, PhD and Xiao Liu, MBChB
The live event is for the Stanford community. The recorded presentation is available here for everyone to view.
The following two papers will be shared during this meeting:
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Professor Alastair Denniston is a consultant ophthalmologist (eye specialist) at University Hospitals Birmingham NHS Foundation Trust, and the Centre for Regulatory Science and Innovation (Birmingham, UK), leading a programme of work in health data research and the application of digital healthcare (including artificial intelligence) to improve patient care in the ‘real world’. He is also the Director of INSIGHT, the UK’s Health Data Research Hub for Eye Health and a Member of the UK Government’s Regulatory Horizons Council.
Alastair leads a team committed to ensuring that the best innovation within the broad field of ‘artificial intelligence’ is translated safely, efficiently, equitably and inclusively to patients. This includes improving the design and reporting of clinical trials in this area (CONSORT-AI and SPIRIT-AI), highlighting issues of representativeness including the risk of health data poverty; developing tools to support safe deployment (including validation datasets); and working with regulators and other stake-holder groups to support the best of these innovations right through the implementation pathway to routine patient care and public health.
Dr. Xiao Liu is an ophthalmology resident and doctoral researcher at the University of Birmingham and University Hospitals Birmingham NHS Foundation Trust. She is interested in clinical evaluation and implementation of digital health technologies (particularly machine learning algorithms as diagnostic tests) to improve patient care in the real world. Her previous work has been around indentifying gaps in reporting and methodology in early clinical AI evaluations, and providing guidance for clinicians to critically appraise the evidence-base supporting AI health systems.