The future of work: AI & automation

Lord Ara Darzi, co-director of the Institute of Global Health Innovation at Imperial College London, co-authors an essay with Hutan Ashrafian for The New Statesman, titled, 'The AI will see you now: The success of automation in healthcare depends on greater patient and public engagement'. The authors discuss the ways in which AI has already advanced healthcare, in areas such as diagnostics and prediction, which will enable a shift towards preventative individual care and public health measures. They argue that in order for AI to be used to its full potential in healthcare, greater government collaboration, regulation and patient education is needed to bridge the technology divide and encourage usage of available technologies.


Artificial intelligence technologies have permeated many aspects of the medical world, enhancing the speed of disease diagnoses, while also introducing chatbots that interact with patients. Since 2017, the scale of AI technologies has grown exponentially, and they have been incorporated to modernise all of the 20th century’s top five disruptive innovations, including telemedicine and robotics, mobile health, basic laboratory biology, improved clinical environments, and health informatics, such as electronic patient records.

The potential of an AI algorithm to offer pertinent advice in human decision-making is exciting. But we must be mindful not to oversimplify AI or to characterise it as it has been in much of science fiction – as confusingly futuristic or even dystopian. There is a vast and growing ecology of AI algorithms that have distinct capabilities, including classical supervised learning, unsupervised learning, machine learning and deep learning.

Each so-called species of algorithm has its own place in the digital ecosystem. For example, 90 per cent ofUS Food and Drug Administration approved AI innovations are currently in the field of diagnostics, including the use of mammograms to screen for breast cancer. Beyond screening, the role of point-of-care diagnostics is also particularly amenable to these algorithms, either in the diagnosis of urinary tract infections from an automated read of urine dipsticks or even algorithms to predict heart arrhythmia through smart watches.

The New Statesman