Accelerating Artificial Intelligence in health and care: results from a state of the nation survey - AUTUMN 2018 - KSS AHSN
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Accelerating Artificial Intelligence in health and care: results from a state of the nation survey AUTUMN 2018
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 3
CONTENTS
4: Foreword
6: Introduction
8: Executive summary
10: What do we mean by AI in health and care?
14: Results of the national survey about AI
technologies in health and care
22: Real world analysis: feasibility and
implementation
38: Summary and next steps
44: Appendix 1: Case studies
48: Appendix 2: Further reading
52: Appendix 3: Glossary
59: Acknowledgements
62: About The AHSN Network AI Initiative4 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 5
FOREWORD
New technologies that harness envisions what can be achieved As it stands, the NHS is primed to We have already made some industry, academia, innovators By working together we will
the power of data, like artificial when the vast potential of AI is use AI to improve its efficiency, important steps forward in and commissioners we will be able to explore all potential
intelligence (AI), present huge unlocked. The report is based deliver better outcomes and this area. These include the continuously iterate the principles avenues of opportunity, and risk,
opportunities to transform on a survey conducted by NHS prevent ill health. However, introduction of a new national and guidance contained in the and to make sure that none are
healthcare, improve the quality England and the AHSN Network we must be realistic about the data opt-out and by the Bill, Code. Together, we will work to missed. We hope that, based on
of people’s lives, and to make the AI Initiative, and it underlines the challenges. First and foremost, currently before Parliament, to ensure that the NHS gets the this reassurance and reflecting on
job of working within the health potential for AI to contribute to the public must have confidence put the National Data Guardian maximum possible benefits from the information presented in this
and care system more rewarding. improved care: 94% of the UK’s AI that AI (and the health data which on a statutory footing. At the end these partnerships, both for enlightening report, you are left
We are determined to harness thought-leaders cite AI as being fuels the development of new of this report we point to, for the existing use cases of AI and those feeling as optimistic as we are
this potential. extremely important or very algorithms) is being used safely, first time, a Code of Conduct for that appear in the future. And, of about the ability of technology,
important for diagnostics; 89% legally and ethically, and that Digital Health Innovations and course, these developments all and AI in particular, to transform
While these opportunities are
support this view for operational the benefits of the partnerships Intelligence Algorithms, which is take place in the context of our health and care.
available to every country, the
and administrative goals; and, between AI companies and the designed to provide a national review of the current regulatory
UK is well-placed to take a global
79% have this opinion in regard to NHS are being shared fairly. As set of ‘rules of engagement’ for framework and analysis of the
advantage in this field. By virtue
the benefits for health promotion a consequence, realising the any NHS organisation entering future needs of the health and
of our universal single-payer
and preventative health. The potential of AI in health and into a partnership with an AI care workforce.
system, the complete longitudinal
report cites many exciting care requires changes to data developer.
datasets the NHS holds on every
examples of pilot schemes and infrastructure, organisational A collaborative approach is
citizen’s health and care, and
more developed programmes structures, commercial Inevitably, there is still more to do important; no single partner in
our world-leading AI and tech
that are already delivering better arrangements, and models to seize the opportunities ahead. this endeavour has a monopoly
industries, our goal should be to
healthcare for British patients. of consent. By working collaboratively with on wisdom about what will work.
bring the transformative power of
AI to every corner of the NHS.
For that reason, we are delighted
to introduce this ‘state of the
nation’ report, which looks to the
future of health and care and
Matt Hancock Lord O’Shaughnessy
Secretary of State Parliamentary Under
Secretary of State
Department of Health
and Social Care Department of Health
and Social Care
Photo attribution: Chris McAndrew [CC BY 3.0 (https://
creativecommons.org/licenses/by/3.0)], via Wikimedia Commons6 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 7
INTRODUCTION
The report is split into
In recent years there have been a
number of policy reports published
intelligent technology is being
realised. This survey went live to
four main sections:
on the potential for artificial the nation at the start of 2018 and
intelligence in healthcare. In this we have captured throughout this What do we mean by AI in health
report we are not attempting to report the initial findings from the 1 and care? This section describes
recreate that content but rather 131 responses. how AI is broadly defined and shows
to address some of the concerns how, as AI evolves, it is becoming an
In order to present a rich picture
raised and outline some of the increasingly complex landscape.
of the nation’s ecosystem and
emerging policy in this arena within
bring to life the complex and
the UK.
multifaceted aspects of the
Results of the 2018 national survey
We developed a survey in industry, the report also highlights
collaboration with industry, a number of case studies that set
2 of AI technology in health and care,
and the defining characteristics of
academia and policy makers in an the scene for the work needed to
the first 131 solutions that were
attempt to capture the reality of scale up evidence-based solutions
submitted.
what technology is actually being that are safe, effective and offer
developed within the UK health value going forward.
and care sector, and to understand Real world analysis of feasibility
what complexity of artificial 3 and implementation based on
evidence from over 100 leaders
and pioneers working in the field.
This highlights the top barriers and
enablers for catalysing an ethical,
evidence-based market for AI-
enabled solutions in health and care,
and defines the issues that will set
the agenda for the sector over the
coming months and years.
A summary of proposed next steps.
4 This includes the key themes for
policy makers to develop a ‘Code
of Conduct’ for an AI-enabled
digital health and care market
going forward, and the regulatory
challenges that need to be
addressed.8 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 9
EXECUTIVE
SUMMARY
Over the last few years numerous The survey The results shows that AI has • ground AI solutions in real Showcase Code of conduct
reports have been written about huge potential to transform ‘problems’ as expressed
The survey was developed with whole the health and care by the users of the health To show what can be achieved as To address these challenges, a
the opportunities and benefits
the input of the AHSN Network system. Unlocking value in data/ system; AI is embraced across health and number of workstreams have
artificial intelligence (AI) can
AI Initiative Core Advisory Group analytics was the top category care, this report showcases some already been initiated across
offer for healthcare. These have (individual members are listed • engage healthcare
(75%) addressed by solutions of the emerging examples of the UK health and care sector.
ranged from the Reform report1 in Acknowledgements) and sent professionals and create an
submitted for the survey, more complex AI methodologies These include the Topol review
illustrating the areas where AI out nationally via the AHSN ethical framework to enhance
followed by condition recognition currently being used and which on workforce; a set of principles
could help the NHS become Network and a number of AI and and preserve trust and
(60%) and organisational hold significant potential to and guidelines summarised in
more efficient to the report by innovation networks including transparency;
processes (50%). deliver impact at scale in NHS, a Code of Conduct for digital
Future Advocacy2 which reviews the AI community run by NHS • build capacity and capability; social care and, importantly, health innovations incorporating
the ethical, social and political Horizons. The survey results are Whilst impressive, the survey • ensure the regulatory in preventative health. As the intelligent algorithms; and
implications of AI in health and self-reported and were compiled shows that many solutions are framework is fit for purpose; complexity and capabilities of a number of initiatives to
and analysed by a team at Kent primarily in their infancy and have these projects increase, it is vital understand and unlock the value
medical research. • explore innovative new
Surrey Sussex Academic Health a long way to go before the true that the policy and organisational of data to provide maximum
funding and commercial
While acknowledging these Science Network, supported by potential of AI for health and care contexts, processes and benefit to citizens and UK plc.
models; and
reports exist, we felt there was Health Education England Kent can be realised. As one survey regulation evolve to keep pace.
a need to understand what is Surrey Sussex. This report is a respondent commented ‘AI is • focus on building a
collaboration between the AHSN still evolving... it won’t solve all sound data infrastructure
actually happening on the ground
Network, NHS England, NHS the problems healthcare faces at and high quality data
and what is being developed. We
Digital and the Department for the moment’ and we must avoid sets, underpinned by
also wanted to ask people within interoperability and sharing
Health and Social Care. the trap of ‘overhyping potential,
the health and care system who standards.
Survey respondents included unrealistic claims and poorly
use artificial intelligence (which
CEOs, senior managers and others thought out products.’
is summarily defined as a series Furthermore, momentum is
working across the AI ecosystem The survey revealed that realising
of advanced technologies that starting to build for unlocking
in England. They represented both the truly huge potential of AI open innovation through
‘Focus on building
enable machines to effectively large organisations with 250 staff
carry out complex tasks that to transform health and care establishing open data
or more (32%) as well as micro services will require overcoming
would require intelligence if ecosystems across health and
organisations with less than 10
completed by a human) what
stage of deployment their work
staff (28%) across private, public
and charitable sectors as well as
several key barriers, and
working together across the
care.
a sound data
AI ecosystem to:
has reached. academia.
infrastructure and
high quality data
sets, underpinned by
interoperability and
Harwich, S. and Laycock, K. (2018). Thinking on its own: AI in the NHS. Reform. Available at: http://www.reform.uk/wp-content/
sharing standards’
1
uploads/2018/01/AI-in-Healthcare-report_.pdf.
2
Fenech, Matthew, Strukelj, Nika and Olly Buston (2018). Future Advocacy and Wellcome Trust. ‘Ethical, social and political challenges of
artificial intelligence in health’. Available at: https://wellcome.ac.uk/sites/default/files/ai-in-health-ethical-social-political-challenges.pdf.10 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 11
WHAT DO
There is no single, universally better understand clients’ Thanks to advances in AI and
agreed definition of AI, nor indeed current and potential future Big Data research, narrow AI
of ‘intelligence’. Broadly speaking, financial needs. technologies have the potential
intelligence can be defined for wide application in health and
WE MEAN BY
as ‘problem-solving’, and ‘an • Ambient (Intelligence) - social care, bringing benefits to
intelligent system’ as one which the application of several individuals, families, communities,
takes the best possible action in technologies (including and society as a whole. While
a given situation. Artificial or Augmented early examples from our survey
Intelligence, but also sensor
AI IN HEALTH
illustrate that much of this work
The ‘A’ of AI generally refers to networks, user interfaces, is at an early stage, current
one of the following: home automation systems, technologies support a more
• Artificial (Intelligence) – makes etc) to create proactive ‘smart’ general shift away from reactive
environments.
AND CARE?
it possible for ‘machines’ to care models to models that are
learn from new experiences, more personalised and proactive.
AI is generally classified into the
adjust outputs and perform
following types: But this is not without its
human-like tasks. It can be
• Narrow AI typically focuses on challenges in health and social
thought of as the simulation
a narrow task, or works within a care and more widely – ensuring
of human intelligence and
narrow set of parameters such these technologies are fit for
could include voice and visual
as reading radiology scans, or purpose, ensuring outputs are
recognition systems.
transparent and explainable, and
AI describes a set of advanced technologies that enable machines to • Augmented (Intelligence)
optimising hospital workflows;
ensuring people are trained in the
carry out highly complex tasks effectively – tasks that would require - outputs that complement • Strong or general AI is a use of these new technologies.
human intelligence, hypothetical concept which
intelligence if a person were to perform them. emphasising AI’s can refer to an AI that can learn
supplementary role. Examples to perform several different
include tools that support types of task, or to a sentient
radiologists in reviewing large machine with consciousness
numbers of scans, or that and mind.
support financial advisors to12 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 13
Example of how simple components form modules
which then form a complex AI application
Complexity
There is a significant amount of effort being
devoted in the research space to map machine
learning and AI, but it has been challenging to
Clinical
scale in AI
categorise them according to their ‘intelligence’. decision
Thus far, attempts at categorisation have been support Complex
limited to looking at their generic ability to solve system application
new problems, and at the speed with which they
adapt to these problems.
A more straightforward way of understanding AI
is to classify AI systems by their complexity. A
‘Complexity Scale for AI’ can be seen in the boxed Inference User
engine interface Modules
section and compared with methods and case
studies revealed in our survey.
A glossary of AI-related terms used within the
Complexity Scale for AI can be found at Appendix 3
on page 50.
Components
Algorithm derived of modules
Link to EPR for Interface widgets Context-sensitive
by ML from
current case & resource files help
historical dataset
High complexity Middle complexity Low complexity
AI applications AI modules or AI reasoning methods
Devised by Jeremy Wyatt, Director and Professor of Digital Healthcare, Wessex Institute of Health Research,
components Clinical Advisor on New Technologies, Royal College of Physicians, Fellow of American College of Medical
Informatics & UK Faculty of Clinical Informatics)
• Autonomous vehicle • Natural language • Deep learning module
• Machine translation tool to SNOMED code • Ensemble methods (e.g. Random Forest Models)
processing module
• Care companion robot • Neural networks
• Image processing
• Chat bot • Object segmentation algorithm
module
• Surgical or pharmacy • Signal processing algorithm / filter The lowest level of the complexity The above provides an example state that they use ‘AI’. We would
• Text to speech module
robot • Generative adversarial networks scale comprises single specific of a complex AI application. also like to encourage those
• Knowledge based or
• Mammogram
expert system module • Time series analysis reasoning methods (e.g. neural investing in these technologies
interpretation system Algorithms in healthcare are not
• Signal processing & • Graphical models networks, pattern recognition to understand what type of AI is
• ECG interpreter a new phenomenon and have
classification module • Decision trees, rule induction e.g. CART algorithms). When these being developed, how complex it
• Diagnostic decision been deployed for decades.
• Recommender module • Clustering algorithm reasoning methods are combined is, and indeed question what the
support system What we have attempted to
• Classification algorithm with other functions (e.g. a ‘A’ in AI truly represents.
• Speech driven radiology show here is how technology
report tool with SNOMED • Regression – linear, multiple, logistic database or user interface), we
utilising intelligence within its
coded output • Inference engine for rules or frames get ‘modules’, which sit at the
algorithms can fall under many
• Argumentation, temporal or spatial reasoner e.g. QSIM next level of complexity and are
different subsections and with
• Text generator using DCGs the problem-solving components
varying degrees of complexity.
• Case-based reasoning algorithm of a system. At the top level of
We encourage developers and
complexity, we have applications
industry to be transparent as to
Devised by Jeremy Wyatt, Director and Professor of Digital Healthcare, Wessex Institute of Health Research, Clinical Advisor on New or packaged systems comprising
what complexity or methodology
Technologies, Royal College of Physicians, Fellow of American College of Medical Informatics & UK Faculty of Clinical Informatics) two or more of these modules
they are utilising when they
(e.g. an autonomous robot).14 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 15
Complexity of
current projects
As part of the survey we asked complexity scale (see previous At the highest end of the
respondents to list some of the section), it can be seen that many complexity scale, 8% of solutions
AI methods employed in their of the current solutions are using employed machine translation
solutions. This enabled them ‘lowest complexity’ advanced methods. 20% of solutions
to be categorised in a way that statistical techniques rather than indicated they used ‘other’ AI
shows how solutions in the AI more complex AI applications. methods, including a range of
RESULTS OF
space vary greatly in terms of Classification and neural network chat bot solutions (considered
complexity. machine learning methods ‘highest complexity’ solutions).
were by far the most popular
By mapping some of the methods
techniques, used by 60% and 51%
THE NATIONAL
employed by survey respondents
of solutions, respectively.
against Professor Jeremy Wyatt’s
SURVEY ABOUT AI
The percentage of solutions
Case studies reporting using a method of AI
delivering
TECHNOLOGIES IN
Lowest complexity
value now Classification 60%
A range of case studies Neural networks 51%
HEALTH AND CARE
identified through the Decision trees 39%
survey at various stages
Clustering 36%
of maturity (from those at
Time series analysis 33%
research stage through to
examples with regulatory Ensemble methods 25%
approval and/or publicly Regression 25%
available) are listed in Graphical models 16%
Appendix 1. Generative adversarial networks 11%
This section presents key findings from 131 self-reported entries in These solutions are Knowledge based/expert systems 37%
delivering value to the Image processing 33%
response to our survey that began in Spring 2018. The information health and care sector in
has been used to create an online map that illustrates what sort of the following areas: Middle complexity
• Unlocking value in data/ Text to speech 9%
problems are being solved currently, who some of the key players analytics Natural language processing 38%
• Leveraging skills and
are, and how we can group or categorise current projects to help our capacity Highest complexity
• Organisational
understanding of the current reality of AI in health and care. Machine translation 8%
processes Other (please specify) 20%
• Condition recognition.
Unsure/Not applicable 9%16 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 17
Diagnostics
Unlocking Condition With wide consensus that diagnostics presents
value in data/ recognition some of the strongest early AI use cases, we chose
to make it a special focus for this first AI map and
analytics 75% 60%
in focus
survey.
Across the range of diagnostics categories, AI is
already offering opportunities to free up workforce
capacity and to dramatically increase diagnostic
The percentage of accuracy. Taking advantage of the convergence
solutions aiming across diagnostics, personalised medicine and
data science, some organisations on the map are
to address each already seeking to mine big data sets to enable
specified category identification of individuals at the earliest stage of
disease, when interventions have a higher likelihood
of success.
Overall, 66% of the initial solutions featured on
our map indicated they contained one or more
categories of diagnostics. As can be seen below,
many early solutions are in diagnostic imaging/
radiology (25%), where digital imaging has been
Leveraging
in widespread use for a number of years. This
skills and compares to far fewer solutions listed in pathology
capacity (9%) and endoscopy (3%), where the digital and AI
43% solutions are only recently starting to emerge.
Organisational
processes Other
50% 24%
Key areas
We wanted greater insight into what types of
problems are being addressed across the range of
solutions. Survey respondents were able to select
44%
Not applicable
where AI 25% 21%
multiple entries from a list of four categories.
Results can be seen below.
The percentage
Unlocking value in data/analytics was the top 11% Other
of companies
can deliver
category (75%) addressed by solutions submitted Imaging/Radiology Genetics
for the survey, followed by condition recognition
(60%). Organisational processes were addressed
& Genomics by category of
by half of the solutions, reflecting the increasing 9% diagnostics
impact
3%
Pathology
20%
use of AI to automate routine clinical, managerial Endoscopy
and back office tasks (e.g. document management,
paperwork and scheduling).
Physiological
measurement18 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 19
The percentage of solutions, which
indicated a point of care, delivering
in each point of care site
The percentage Yes 57%
of projects
linked to smart
Patient’s own connected
home 49%
devices
Other 13%
No/Unsure
Hospital 68% Residential care
43%
or nursing home,
assisted living
38%
Mobile, semi-mobile
units and other and Personal/
Point of
Another way that AI is enabling (8%), vendor/supplier managed
‘pop up’ style settings wearable
new models of care is by settings (19%) and mobile,
of no fixed abode 19% technology using remote diagnostic and semi-mobile units (19%), were
35%
care
monitoring capabilities to selected by the least number of
change where and how care is respondents.
delivered. We asked solutions to
Already, 57% of solutions within
indicate the points of care where
the survey say they able to link
they deliver services (multiple
Vendor/supplier to smart connected devices (e.g.
selections were possible).
Internet of Things). With super-
managed clinic Excluding those entries that did fast 5G broadband networks
facilities 19% not indicate a point of care, the being tested this year, it is likely
Medical majority of solutions reported that the number of IoT-enabled
transport delivering services in hospitals solutions offered in non-acute
vehicle 8% (68%), followed by a patient’s points of care will increase over
Community or own home. Care settings such the coming years as 5G networks
primary care as medical transport vehicles are rolled out more widely.
clinic 39%20 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 21
Regulation Proprietary (closed source) 35%
Our survey aimed to capture a device. Devices meeting the
Unsure 19%
The percentage of
full range of AI activity across requirements can place a CE Prefer not to say 16%
solutions according
the health and care ecosystem, mark (or logo) on their product to Not applicable 14%
ranging from ongoing research show that the medical device has Other (Please specify) 8%
to type of licence
projects to fully scaled met the requirements as set out
GNU GLPv3 (open source) 4% the computational
commercial products and in the conformity assessment.
services. With new solutions The CE marking also means Apache Licence 2.0 (open source) 3% product has
coming to market regularly, it is that the product can be freely MIT Licence (open source) 2%
important for buyers (including marketed anywhere in the EU.
commissioners and consumers) In the United States, the Food
and users of AI to have and Drug Administration (FDA)
mechanisms for distinguishing provides medical device approval.
which solutions have the
When we asked our respondents
appropriate evidence base and
about regulatory status, only 18%
Licensing
are ready for ‘at scale’ adoption. Currently, 35% of solutions in Proponents of open standards,
of solutions indicated they had
In the UK, medical devices secured approval in the UK/EU or the survey have been developed such as the Apperta Foundation,
must demonstrate that they abroad. A further 23% indicated using proprietary (closed a not-for-profit community
meet the requirements set they were in the process of source) software, distributed interest company supported by
out in the Medical Devices securing approval. under licensing agreement to NHS England and NHS Digital,
Directive by carrying out a named users who are given maintain that liberating both data
conformity assessment. The authorisation to modify, copy and applications and making
assessment route depends and republish applications. The them portable and interoperable
on the classification of the source code for this software is eliminates lock-in, facilitates
not shared publicly for anyone innovation and competition,
to look at or modify. Proprietary and forces vendors to compete
software developers often on quality, value and service.
pride themselves on product A downside can include the
‘usability’ and providing a high significant capacity and capability
level of ongoing support for required to run open platform
maintenance, security, content ecosystems.
updates and training.
The percentage of projects with regulatory approval A further 19% are unsure what
In contrast, only 9% of licence their computational
respondents report using one of product uses altogether, and this
the following three open source needs to be explored further to
licences – GNU GLPv3, Apache understand the reasons for this.
Licence 2.0 and MIT Licence.
Open platforms are vendor
41% 23% 18% 18% and technology neutral and
are based on open standards,
meaning that any application
built on an open platform will
operate on an open platform.
Not applicable In process of No Yes
securing approval22 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 23
Who responded?
Survey respondents included Respondents cite a broad range
CEOs (42%), senior managers of experience with AI, with many
(15%) and others working across indicating that they wear multiple
the AI ecosystem in England. hats when dealing with AI.
They represented both large
organisations with 250 staff or
more (32%), as well as micro
organisations with less than 10
staff (38%) across private, public
and charitable sectors, as well as
academia.
REAL WORLD
ANALYSIS ON Q: What describes your current
experience with AI? I evaluate
FEASIBILITY AND
AI 43%
I use AI
I procure AI
IMPLEMENTATION
53%
17%
In order to inform government policy and the AHSN Network AI
Initiative offer, we conducted a survey of 106 thought leaders and AI
pioneers during May and June 2018. In this section we outline survey
results, highlighting top barriers and enablers for catalysing an ethical, I regulate
AI 5%
evidence-based market for AI solutions in health and care.
I develop AI
Other 51%
25%24 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 25
Game- The best AI-enabled solutions
always solve a valuable problem
or ‘use case’, as expressed by
Development in drug discovery
and medical research will also be
hugely aided by AI.
‘80% of all
changing
users - citizens, carers and
Respondent views ranged from
professionals. Working with
AI being ‘ubiquitous’, ‘pervasive’
users to understand their needs
and ‘high impact’ that will ‘replace dermatology
use cases
and then working with them to
front line tasks’ to rather less
prototype and test solutions
iteratively is key to refining the
optimistic predictions. Many see
AI as a tool to help doctors and all
diagnoses will be done
using AI within 3 years
product’s value proposition and
healthcare professionals become
ensuring successful uptake and
more efficient and deliver a
adoption at scale.
We asked respondents to identify
higher standard of care at less
cost to benefit patients. Most see - it will be better than
the areas where the strongest AI having a key role in helping to
early use cases are. make decisions across the board
and in better planning for scarce
dermatologists at
Overall, the views were clear that
the main game-changing use
resources. diagnosing’.
cases for AI will be in three key
areas in the immediate period: Respondent prediction
• Diagnostics
• Non-clinical (operational and
administrative efficiency)
• Health promotion and
preventative health.
The top three use cases are • ‘Translation into routine processes (e.g. document
explored in more depth below. practice, widespread use management, paperwork and
Treatments of clinical decision support scheduling). Machine learning will
Health and Diagnostics Non-clinical Keeping Diagnostics (accurate and
tools for complex diagnostics, increasingly be used to process
promotion and interventions (accurate (e.g. save up to date early detection) was cited
genomics and lifestyle advice’ images and texts.
preventative (including and early time with with medical overwhelmingly as a strong
health surgery) detection) administration) research early AI use case, with 94% of A reduction in administrative
• ‘80% of all dermatology
respondents citing it as either diagnoses will be done using staff overheads is expected, and
extremely important or very a positive view on how AI will
Extremely important 47% 40% 80% 66% 35% important. Some predictions from
AI within 3 years - it will be
better than dermatologists at impact clinicians also emerged.
survey respondents include: diagnosing’. • ‘AI and clinicians will work more
Very important 31% 28% 14% 23% 30% • ‘Huge impact in radiology closely as one team’
Use cases for non-clinical
for assisted reporting and
applications, for instance saving
Quite important 16% 26% 5% 9% 21% screening’
time with administration, were
• ‘Supervised machine learning -
Clinicians remain in control’.
• ‘Increased use in radiology and seen as extremely important
Somewhat important 5% 5% 1% 2% 10% other imaging applications, or very important by 89% of
particularly in prioritisation/ respondents. AI will increasingly
Not at all important 1% 2% 0% 0% 4% triage of scans to ensure these be used in the automation of
routine clinical and managerial
are brought to human attention
first’ tasks and for back office26 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 27
Some predictions include:
• ‘Significant improvements in
workflow management and
Comments from respondents
include:
• ‘Move towards using AI as
• ‘AI will be instrumental in
detecting minuscule changes
in individual’s records (data),
Overall AI To gauge what factors might support the
development of AI in health and care, we asked
respondents to consider the extent to which the
enablers
making it possible to detect following actions or policies were important in
data analysis coupled with the a tool for early prevention
and catch problems even realising the potential of AI in health and care.
emergence of intelligent clinical and diagnosis using large
before they actually form. It will
decision support systems’ population level datasets i.e. The numbers below highlight actions or policies that
enable prevention in the most
identifying individual risk. Use respondents viewed as very or extremely important:
• ‘AI will become a standard literate sense of the word’.
of AI in demand management
part of devices and image and predictive modelling’ For an excellent overview of
management systems’.
AI use cases, refer to Future
• ‘Better allocation of resources
Health promotion and Advocacy’s Ethical, Social and
by earlier detection of patterns
preventative health was cited
as extremely important or very
and thus disease, with
Political Challenges of Artificial
Intelligence in Health and Care
92% 88% 87% 82% 81%
better targeted preventative
important by 78% of respondents. (April 2018)3, a report produced
strategies as a result’
Overall, respondents expect AI to with the Wellcome Trust.
be used in a more predictive way, • ‘AI will take a large amount 3
Ibid.
facilitating the shift from reactive of the early identification of
care to a more preventative disease, allowing clinicians Engagement Ethical frame- Capacity and Clarity around Education of
health model in which people are to focus on the complicated of healthcare work to build/ capability to ownership of healthcare
more empowered to take care of cases’ professionals preserve trust and deliver scope data professionals
their own health. transparency
AI enablers
We then looked at the relative importance of factors
impacting diagnostics, building on potential actions
suggested by our AHSN Network AI Initiative core
specific to
advisory group members.
‘Better allocation The percentages below show the key factors that
pioneers believe are very or extremely important
of resources by
diagnostics
to address in order to realise the potential of AI in
health and care:
earlier detection of
patterns and thus
disease, with better 93% 93% 85% 82% 78%
targeted preventative
strategies as a result’
Respondent feedback Data sharing for Support the Consistent Reviewing Work with
medical imaging spread of proven labelling governance models commissioners
for AI training innovations methods for (eg ST 11-7) in light to help them
imaging data of machine learning understand how
algorithms to buy AI-enabled
products and
services28 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 29
Trust, privacy and ethics
Education of
healthcare Education
professionals of public
According to survey respondents,
the top two factors enabling the
• ‘The speed at which AI will
have an impact on healthcare
Educating healthcare
professionals and the public on
Extremely important 50% 37%
realisation of AI in health and care will depend very much on the the potential of AI in a balanced
are ‘engagement of healthcare public’s (and therefore the way was also raised as a key Very important 31% 28%
professionals’ and establishing government’s) trust in AI and issue by survey respondents.
an ‘ethical framework to build/ the company using patient This is central to achieving Quite important 17% 25%
preserve trust and transparency’. data to develop AI. This will not and maintaining trust in an
impact all AI products but a environment where there is much
Overall, respondents agreed with
significant proportion’ negative media coverage on
Somewhat important 2% 10%
the need for a clear governance
the risks of AI and its potential
structure to guide decisions
and build trust. This needs
• ‘There has to be first an
enabling framework within the
impact on workforce. A narrative Not at all important 0% 0%
around data sharing is needed.
to be underpinned by a clear NHS. This would include ethical There is also the need to engage
ethical framework to address considerations, the right for the public actively in order to help
such issues as transparency in human interpretation of the AI define the problems that need
algorithm development. algorithms’ Key comments include: Predictions for the future include:
solving and co-develop solutions
Comments from the respondents enabled by AI. • ‘We need a narrative around • ‘Greater public support for AI
• ‘We need transparency of
include: data sharing and trust …’ due to better understanding
algorithm development’
• ‘There is a need for widespread [of] how AI works’
• ‘[the potential of AI] is based • ‘We need public education
understanding of augmented and enabling regulatory • ‘There will be a new cohort
intelligence, predictive on the governance structure
developed and ability to forge frameworks’ of healthcare professionals
analytics, deep learning and that will be educated to think
machine learning’ trust’. • ‘It’s not enough to ‘educate’ how to empower their human
the public- we need active abilities with AI driven tools’.
participation of patients and
other interested parties at all
stages of the development
Engagement Ethical framework to process’
of healthcare build/preserve trust • ‘Much more needs to be
professionals and transparency done to educate healthcare
professionals, listen to/
Extremely important 58% 61% understand their concerns,
and get their buy in. At the
Very important 33% 26%
moment the conversation is too
polarised between naysayers
‘We need a
who say, “AI will never change
narrative around
Quite important 8% 9% healthcare significantly” and
techno-utopians who say, “AI
will replace all doctors and data sharing
Somewhat important 0% 3% nurses” - the reality is of course
much more nuanced than that.’ and trust …’
Not at all important 1% 0%
Respondent feedback30 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 31
Workforce Evidence of effectiveness
knowledge of AI and regulation
Workforce opportunities will be in the new era of AI will also be • ‘Ability for enough people to Linking strongly with the theme 78% of respondents felt that • ‘Regulation needs to be
addressed in detail in the Topol essential, along with training in understand the back end of of trust, the requirement for regulation was extremely light touch, to allow patient
Review (being led by Dr Eric technical and legal aspects of AI. AI, and even perhaps learn the evidence of effectiveness of important or very important confidentiality but at the same
Topol and facilitated by Health coding within hospitals to help the digital health innovations in realising the potential of time allowing the industry to
Above all, securing clinical
Education England), but it is understanding, engagement internal management’ and intelligent clinical decision AI in health and care. Gaps in flourish so we can achieve
important to note that at a high and buy-in to the co-design support (algorithms) was a topic regulating AI-enabled products efficiencies in the fast time
level, education has come out as • ‘We need to understand and
and use of AI will be important that ran throughout respondents’ and services, and uncertainty possible’
a clear enabler. design the human computer
to leverage the potential of the comments. about the roles of the various
interaction and how algorithms • ‘Regulation is important, but it
87% of respondents indicated technology. This will not only regulators and when a product,
are used in practice’ A number of respondents called would be better to find a global
that building capacity and assuage clinicians’ fears and service or algorithm should
for the ability to explain the solution rather than country by
capability is extremely important concerns, but will ensure that the • ‘Understanding financial and be subject to regulation were
algorithm and providing enough country. Particularly concerned
or very important to achieving AI algorithms developed augment clinical pathways in more detail also strong themes. There is a
information to allow regulators to if the UK decides to go its own
AI’s potential. This includes basic (rather than replace) and increase will be important…’ clear need for a new regulatory
independently replicate results way post-Brexit, as the NHS
education on AI and its potential the accuracy of human clinical framework to keep up with
• ‘Helping to build interdisciplinary on a similar set of data, ensuring market isn’t large enough to
applications for senior managers decision making. Helping senior advances in AI.
teams so that clinicians with algorithms are safe and unbiased. be worth separate certification
and directors in clinical, decision-makers to understand
good ideas can have these Representative comments Comments included: beyond FDA [United States] &
management, commercial and and have realistic expectations
of what AI has to offer will also be realised by people with include: CE [EU]’.
procurement roles. Training in • ‘Legacy regulations will
important. computer programming skills’ • ‘Many AI-based tools will limit widespread adoption
areas such as user-driven design,
change management, ethics and Key points to consider from our • ‘Having highly skilled data struggle to get used through of diagnostics and health
having difficult conversations survey participants include: scientists involved is crucial’. lack of evidence and/or clinical prevention applications’
conservative behaviour’
• ‘[Government should] address
• ‘Clinical support should be barrier of regulation and
gained by discussion of the the ability to rapidly iterate,
Somewhat Extremely scientific case and justifying prototype, and validate
important important the technology. Just offering prospectively’
46% an algorithm lacks scientific
0%
credibility’.
Regulation of AI
Not at all
important Capacity and Extremely important 44%
1% capability Very important 34%
to deliver
Quite scope Quite important 17%
important Very
12% important Somewhat important 5%
41%
Not at all important 0%32 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 33
Funding and
Given AI’s potential for system
wide impact, with funding
flows and incentives crossing
‘Understand
commercial models
organisational boundaries
and hierarchies, some
respondents also commented
on the opportunity to reimagine ‘value’ of public/
commercial models in the new
Despite the financial challenges
experienced in the NHS, ‘funding
models for AI development and
deployment’, which came in at
92% of respondents said
they believed ‘supporting the
era of AI: NHS data and how
and budget restraints’ featured
only tenth on the list of thirteen
12th and 13th (last) on the list
respectively.
spread of proven innovations’
is extremely important or very
• ‘Understand ‘value’ of public/
NHS data and how this can be this can be sold
factors affecting the potential of important to realising the sold to developers or used to
AI in health and care, with 68%
of respondents indicating that
This result could be reflective
of the early stage of the
potential of AI in diagnostics. generate additional income’ to developers or
funding was extremely important
or very important. Featuring even
development of the AI market
in health and care. In contrast,
• ‘Evaluate cross department
business models - who used to generate
in diagnostics, where the early
additional income.’
owns hospital-wide clinical
lower on the overall list of key
AI use cases are strongest and efficiency? For example,
factors were ‘NHS internal market
where we are already starting to will radiology purchase an
and procurement’ and ‘lack of
see products come to market,
clarity over appropriate business
AI product whose benefit is Respondent feedback
realised by reduced drug cost
in neurology? How do those
dots get joined up?’.
Lack of
clarity over
appropriate
business
models for AI NHS internal
Funding/budget development market and
Extremely
restraints and deployment procurement
important
Not at all
50%
important 1%
Extremely important 45% 34% 29%
Very important 23% 25% 34% Support
Quite important 27% 33% 21% the spread
Somewhat
important 2% of proven
Somewhat important 4% 8% 13% innovations in
Not at all important 1% 0% 3% Quite diagnostics Very
important important
5% 42%34 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 35
Data quality, sharing Consistent labelling Data sharing for
and interoperability methods for imaging
data
medical imaging for
AI training
Extremely important 54% 69%
The importance of a sound data Respondents’ comments include:
infrastructure with high quality
• ‘The current datasets in Very important 31% 24%
data and the relevant standards
healthcare are patchy, dirty
on sharing and interoperability
came through as key factors in
and often incorrect! Garbage Quite important 11% 6%
in garbage out. Often data are
realising AI’s potential.
not digitised. The first action
in machine learning or AI is to
Somewhat important 3% 1%
Data quality clean up dirty data’
A key concern affecting the ability • ‘We need clean labelled un-
Not at all important 1% 0%
of AI to deliver on its potential gamed datasets’
is that of the quality of the data
• ‘We need data compatibility
itself, much of which is not
through labelling and
digitised or in machine-readable
standardisation’.
format. ‘Data readiness’ (getting
data ready for AI) was a key Data sharing and interoperability
theme.
Overall, the view emerged that Key points voiced by respondents • ‘We need distribution across
the underlying data infrastructure were: public and private sectors,
is not fit for purpose for AI with patient access to any
Open standards and requires standards to
• ‘The role of private companies
information generated and
Clarity around to promote data and developers including
facilitate data sharing and the ease of sharing this’
Data sharing ownership of sharing and ownership of and access to
development of appropriate
public and patient data and • ‘Clarification of concepts
framework data interoperability commercial models to leverage
how data sharing agreements around patients being curators
the value of public/NHS data.
are negotiated’ not owners of the (“their”) data;
This is an especially pressing
Extremely important 56% 54% 47% concern where public sector • ‘The underlying IT
compliance with GDPR but still
entities have entered into allowing retention of images/
infrastructure in the NHS is
Very important 24% 28% 31% agreements with companies to poor and not AI ready. We need
blood results/other data to
feed Big Data’.
process data. These datasets a large push to standardise IT
Quite important 16% 10% 19% often end up in proprietary
format or in difficult to access
formats and data sharing’
repositories. Intellectual property • ‘Robust development,
Somewhat important 4% 7% 2% of algorithms developed using testing and validation of AI
these proprietary data sets often is key. Without appropriate
governance it will be a liability’
Not at all important 1% 0% 1% rests with the companies (outside
the public sector/NHS)4.
4
Naylor, A. and Jones, E. (2017). Unleashing the potential of health and care data. Future Care Capital. Available at:
https://futurecarecapital.org.uk/policy/healthcare-data/.36 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 37
Towards a
A number of respondents endorsed the NHS Digital and NHS England are also laying the
establishment of open data ecosystems in order groundwork for open innovation with a number of
to leverage insights from multiple datasets, initiatives including:
sustainable
enabling the real power of AI to come into its own. • Apperta Foundation, which recently published
For example, Transport for London provides a ‘Defining an Open Data Platform’
common API to access 80% of the UK’s transport
data. Thousands of developers (including the • Code4Health, which provides a home for the
ecosystem original Citymapper app) build on top of this open
API platform. Similarly, Open Banking, recently
introduced in the UK, will see the UK’s nine biggest
increasing number of open source projects
providing software suitable for use in health and
care
banks release data in a secure, standardised form,
so that it can be shared more easily between • International exemplars in the area of open
authorised organisations so they can then use it innovation platforms in the health and care space
to create more products and services to benefit include REshape Centre Radboud (Netherlands)
citizens. The intention is to put citizens in control and Boston Children’s Hospital (United States)
of their own banking data, providing an easier way • Closer to home, University Hospitals Plymouth
for them to move, manage and make more of their NHS Trust and Great Ormond Street Hospital are
money. exemplars in building open data ecosystems and
In order to unlock open innovation around data- fostering open innovation.
driven health and social care services, any open
data ecosystems must provide mechanisms for
data to flow safely and securely across disparate
Case Study
health and care organisations, whilst ensuring
informed consent and transparency. Enabling
citizens to ‘donate’ their consumer data (e.g. from
banking, retail, transport, telecommunications,
utilities, etc) and data from sensors and IoT-enabled Great Ormond Street Hospital DRiVE Unit (Digital
devices could also support citizens to stay healthy
and in their own homes for longer5.
Research, Informatics and Virtual Environments)
Momentum is growing to establish open data GOSH’s DRiVE unit provides a good example in a secure environment in the cloud that
ecosystems across health and care. This should of the type of open data ecosystem and is compliant with ICO and GDPR guidance
accelerate over time as forthcoming Industrial infrastructure required for exploitation of AI’s regarding the use of data for research.
Strategy Grand Challenge investments in initiatives potential within health and care. The DRiVE Clinicians, researchers and industry partners
such as Digital Innovation Hubs (connecting regional unit provides both a concept and a physical looking to address specific problems can come
health and care data with biomedical data in secure space dedicated to accelerating research together in secure virtual ‘workspaces’ to run
environments) and the Healthy Ageing Challenge and evaluation of new AI-enabled technology analyses and APIs. The data therefore does
start to bear fruit. and data analysis, with the aim of developing not leave GOSH’s control and governance,
scalable solutions for child health. Working providing full transparency. The GOSH team
with partners including University College and regulators have full data provenance,
London (UCL)/ Alan Turing, major industry including details of IP addresses accessing the
partners and NHS Digital, early areas of focus code and whether changes have been made.
will include machine learning, assisted decision During the development phase, the GOSH
making and the use of medical chatbots. team are also working to generate synthetic
datasets for innovators to test their early
GOSH’s open data ecosystem captures
algorithms on, prior to validation on real data.
and integrates data from multiple sources
5
Woods, T.M. and Kihlstrom, E. (2018). Data and the Future of Health and Social Care. Report, Proceedings and Key Recommendations.
Round Table, 17th November 2017. FutureHealth Collective. Available at: https://www.colliderhealth.com/future-health-collective.38 Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey
Accelerating Artificial Intelligence in health and care:
results from a state of the nation survey 39
Where are we now?
Whilst AI solutions are increasing is currently reviewing technical 1. Build fairness and transparency
in their complexity, most now and clinical safety requirement in digital health innovations,
delivering impact are on the low and design standards before algorithms and clinical decision
complexity end of the spectrum. publishing onto the library. support tools.
Understanding the vast potential Ultimately, the NHS must
of AI – as well as its limitations 2. Help identify the requirements
protect its reputation as an and standards that
- will be key moving forward. internationally trusted health and
As one survey respondent organisations and suppliers
care system, ensuring patient need to fulfil in order to show
commented, ‘AI is still evolving… safety and high quality care, and
it won’t solve all the problems that products are safe, secure
preserving the trust between and maintain public trust.
healthcare faces as the moment’ citizens, clinicians and the wider
and we must avoid the trap of health and care system. In order 3. Identify gaps within regulatory
SUMMARY
‘overhyping potential, unrealistic to do this we have collated a set and approval processes that
claims, and poorly thought out of principles outlined in a Code of need to be addressed to
products’. Conduct, which is in early stages accommodate developing
Top AI enablers include of development. technologies.
AND NEXT
engagement with health The purpose of the Code is By working collaboratively with
professionals, as well as to provide a source of clear academia, industry, innovators,
grounding the use of AI in principles and guidance for the commissioners and AHSNs to
real problems as expressed development of trusted digital iterate and continually update
STEPS
by citizens, carers and other health innovations and intelligent
these principles, we can go
health professionals. Providing algorithms within the UK NHS
some way to staying abreast of
mechanisms for improving data health and care sector. This
evolving technologies, helping to
quality and the underlying data code can be used by innovators,
catalyse the scale and adoption
infrastructure will also be key, industry, commissioners,
of intelligent technologies.
along with introducing a safe, academia and individuals,
Addressing these requirements
evidenced and transparent as a framework to support
with the right solutions will spur
approach to how algorithms and development and deployment of
collaboration across the NHS,
The analysis of survey responses, together with the constellation of innovations are developed. any DHI or intelligent algorithm
social care and other partners in
(IA). Whilst this code will ensure
Currently in the NHS, we have the ecosystem and build public
organisations in the AI map and illustrated by the case studies in this a number of programmes such
that the DHI/IA being developed
trust. Strong cross government
are in line with the principles and
report, reveals that AI in health and care is still at a relatively early as the Local Health and Care
values of the UK health and care
collaboration, including pooling
Record Exemplars, NHS Test resources and partnering on joint
system, it is still a requirement
stage. At the same time, there are many promising early use cases Beds and the forthcoming Digital
that the relevant regulatory
initiatives is also underway and
Innovation Hubs that give us the is the key objective of the AHSN
for AI in this space, especially in diagnostics. The health and care opportunity to test and refine
and/or approval processes are
Network AI initiative.
adhered to.
digital health innovations (DHIs)
AI ecosystem continues to grow at pace, with a range of promising and algorithms with our partners. This code, if followed, can ensure
that within the NHS and the wider
interventions in the pipeline, currently gathering evidence that they The NHS Apps Library and
UK health and care sector we
Digital Assessment Questions
are safe, effective and offer value prior to regulatory approval and are examples of how the NHS collectively:
widespread implementation.You can also read
