This event is part of the Demystifying regulation and commercialisation mini-series, hosted through the Christabel Pankhurst Institute for Health Technology Research and Innovation. These events, taking place in June and July 2021, are organised by Advanced Materials in Medicine (AMM), Translation Manchester, Digital Futures, the University of Manchester Innovation Factory, and the Institute of Data Science and AI. Significant AI health technology activity is taking place in the Greater Manchester (GM) region across academic institutions, regional government, SMEs and larger organisations. Examples include the development of the Greater Manchester Care Records (GMCR), which integrates primary, secondary and social care data from 2.8M individuals and the recent establishment of the Christabel Pankhurst Institute for Health Technology Research and Innovation (Pankhurst), a cross-sector multi-partner translational enabler; and the European Regional Development Funded Greater Manchester Research and Innovation Health Accelerator to support regional SMEs. Due to these developments, there is increasing awareness of the emerging evidence requirements to demonstrate the utility of AI-based health technology but less detailed understanding of the specifics of this complex and evolving environment.

A very clear explanation of why AI is now delivering on its decade-old promise, how every area of life and technology will be affected, a very useful explanation of some of the underlying technology, a precise assessment of where the hype is real and where we can....

Artificial intelligence has become a frequent topic in the news cycle, with reports of breakthroughs in speech recognition, computer vision, and textual understanding that have made their way into a bevy of products and services that are used every day. In contrast, clinical care has yet to reach the much lower bar of automating health care information transactions in the form of electronic health records. Medical leaders in the 1960s and 1970s were already speculating about the opportunities to bring automated inference methods to patient care,1 but the methods and data had not yet reached the critical mass needed to achieve those goals. The intellectual roots of "deep learning," which power the commodity and consumer implementations of present-day artificial intelligence, were planted even earlier in the 1940s and 1950s with the development of "artificial neural network" algorithms.2,3 These algorithms, as their name suggests, are very loosely based on the way in which the brain's web of neurons adaptively becomes rewired in response to external stimuli to perform learning and pattern recognition.