Can human-centric AI design enhance rather than diminish the humanity of patient and clinician?
While not without it’s challenges, the opportunity that AI presents to offer more individualised care is, in a slightly ironic fashion, working towards more human-centric care models.
The growing pressure on clinicians and health systems to provide timely, efficient and effective care is here to stay.
– Dr Melanie Flory, neuroscientist, psychologist and associate director of research at the Royal College of Art’s Helen Hamlyn Centre for Design.
Her research enquiry sits at the intersection of design, systems thinking, and cognitive neuroscience, seeking new insights into the interplay between emotion and cognition.
The theoretical conflux of AI and precision medicine promises insight generation, swift decision-making, personalised diagnosis and prognostication, and better survival rates, among a plethora of other advantages.
The opportunity that human-centric AI design and AI-assisted care affords is that of enhancing rather than diminishing the humanity of patient and clinician.
Smart hospital systems – with improved operational efficiencies, automated workflows and connectivity between systems, devices, assets, data, and people – could allow integration with speciality clinics, primary care providers, and other healthcare venues to ensure patients receive the right care at the right time and place, anywhere in the community. Instead of every care service being warehoused in a one-size-fits-all building, more individual specialist units could be established to suit specific patient populations – each then hooked up to one big digital infrastructure network that monitors supply and demand in these smaller hubs.
The ability for AI to efficiently and effectively manage the huge amounts of data in healthcare will not only help free up valuable time and resources but also directly improve health outcomes.
– Sam Shooter, Director.
He believes the social value and impact of AI, meaningfully implemented in healthcare design, could be vast. Of course, it must be responsibly executed, with control mechanisms in place to maintain ethical practice. While there are known biases in algorithm writing and AI design can be seen to pose the biggest risk to the creation of inclusive design, if we remain acutely aware of these, AI-powered tools also have the potential to provide the answers and design for the widest range of people.
Look out for part 3 coming soon, where we look at how we can eradicate bias AI designs.