How AI is transforming the NHS
A ccording to the ONS,
approximately 22% of all
UK deaths in 2018 were
considered avoidable. Of these,
64% could be attributed to causes
considered preventable and 36% to
treatable conditions. Improving the
diagnostic process is therefore one of
the most important areas where AI is
being implemented.
For example, incomplete medical
histories and large caseloads can
lead to serious human error. Immune
to human error, AI can predict and
diagnose disease faster than most
medical professionals. In one study,
an AI model using algorithms and
deep learning diagnosed breast
cancer at a higher rate than 11
pathologists.
“Healthcare at every level is
dependent on algorithms of one
kind or another,” said Parashkev
Nachev, Professor of Neurology,
Brain Repair and Rehabilitation
at UCL Queen Square Institute of
Neurology. “They are needed to
ensure care is reproducible, objective
and scalable. What I’m trying to do
is maintain these characteristics
while introducing greater complexity,
so care can be better tailored to
individual patients.”
This means that when you present
to your doctor, your treatment is
informed not by the average of
the population – from which you
may be very distant – but by the
“neighbourhood” of people like you,
those whose characteristics are most
similar to yours.
He adds that these ideas can also
be applied to how hospitals are run.
For example, the UCLH’s work on nonattendance,
which drew intelligence
from complex algorithms in order to
work out which patients were more
likely not to attend their appointment
and remind those who need to be
reminded.
Whether being used to discover links between genetic codes, power
surgical robots or maximise hospital efficiency, artificial intelligence
(AI) is transforming the healthcare industry. By Tom Austin-Morgan
Among them were Oxford
University spin out, Brainomix’s
‘e-Stroke suite’ - a collection of tools
that use state-of-the-art AI algorithms
that provide real-time interpretation of
brain scans to help guide treatment
and transfer decisions for stroke
patients.
Another was RITA (Referral
Intelligence and Triage Automation),
Deloitte’s solution that automates the
triage of GP referrals by reducing the
administrative burden on healthcare
professionals by assessing the
urgency of referrals.
Dutch company, Aidence, has
developed Veye Chest, an AI platform
to optimise oncology pathways, to
automate early lung cancer detection.
Healthy.io’s home test kit and
mobile app allows patients to self-test
at home with clinical grade results.
Integrated into the Electronic Medical
Record (EMR), real-time results are
available for clinicians to review and
follow-up. Shifting to testing at home
is said to increase uptake, improve
quality, reduce workload in primary
care, and create savings.
Deep learning software has
been developed by Kheiron Medical
Technologies to solve critical
challenges in the NHS Breast
Screening Programme, including
reducing missed cancers, tackling the
rising shortage of radiologists and
improving delays that put women’s
lives at risk.
DLCExpert by Mirada Medical
uses AI software to automate the
time-consuming and skill-intensive
task of outlining (or ‘contouring’)
healthy organs on medical images for
radiotherapy planning so that they are
not irradiated during treatment.
Prof Nachev said, “The objective
was to increase the yield of reminding
interventions so that the fewest
number of patients missed their
appointments, ensuring consequent
delays to care are minimised.
Already established at UCLH, it is
an approach that others, such as
DrDoctor, are helping disseminate
across the NHS.”
Diagnosing and reducing error
The NHS Accelerated Access
Collaborative (AAC), chaired by Lord
Ara Darzi, aims to make the UK one
of the most pro-innovation health
systems in the world by pooling
the knowledge and expertise of
its board members who represent
various departments across the NHS,
government, and industry.
The AAC runs the Artificial
Intelligence in Health and Care Award in
partnership with NHSX and the National
Institute for Health Research. The
award will make £140 million available
over three years to accelerate the
testing and evaluation of promising AI
technologies – from initial feasibility to
evaluation within the NHS.
The winners of the first round of
the competition were announced on
the 8th September 2020.
“Without
supercomputers
we’ll be confined
to basic models,
and there will be
a hard ceiling
on what we can
achieve.”
Prof. Parashkev
Nachev
18 23 February 2021 www.newelectronics.co.uk
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