Artificial intelligence, the ability of systems to replicate human behaviour in an
intelligent manner, shows promise in the United Kingdom of Great Britain and Northern
Ireland’s National Health Service (NHS), which provides free-at-the-point-of-service
health care via a national insurance scheme (Fig. 1). Recent advancements in artificial
intelligence have created sophisticated software programmes that could revolutionize
the NHS. Breakthroughs in machine learning, more notably deep learning (Box 1), have
led to algorithms capable of performing diagnostic skills equivalent to those of doctors,
automating administrative tasks and assisting in complex treatment management.
Fig. 1
The structure of the National Health Service in the United Kingdom of Great Britain
and Northern Ireland
NHS: National Health Service; UK: United Kingdom
Sources: National Health Service of England, Scotland, Wales and Northern Ireland
websites: https://www.england.nhs.uk/; https://www.scot.nhs.uk/; https://www.wales.nhs.uk/;
http://online.hscni.net/. Most recent national census data (2011) for each of the
constituent of the United Kingdom: https://www.ons.gov.uk/.
Box 1
Definition of machine learning and deep learning
Machine learning
A subset of artificial intelligence that allows programmes to learn without being
explicitly programmed. These programmes learn from data sets, identify patterns within
them and use the information to make predictions about data they have not been previously
exposed to.
Deep learning
A relatively new subset of machine learning, which uses algorithms that replicate
the human brain (neural networks) in structure and function. In machine learning,
the more data the algorithm can use, the greater its performance. In older styles
of machine learning, algorithms eventually reach a plateau in performance, regardless
of how much additional data is used to train the algorithm. Deep learning, however,
can use greater quantities of data leading to higher levels of accuracy in algorithms
that were not previously achievable, this is one of the main reasons deep learning
has revolutionized the artificial intelligence field today. Despite the advantages
of increased performance, one of the major drawbacks of deep learning is the lack
of transparency in the algorithm’s operation (known as a ‘black box’ system). In deep
learning, viewing the input and output of algorithms is possible; however, it is difficult,
even for developers, to determine how the algorithm produced its output. This difficulty
proves significantly problematic in disciplines such as health care, where justification
for decisions reached is crucial to ensure maximum safety for patients.
As a result of the vast linkable data that the NHS holds on all citizens throughout
their lives, the service could have a leading role in taking forward artificial intelligence
development for health care;
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however, its use remains limited, with little overarching policy guiding its development
and application. In 2018, the government of the United Kingdom published a code of
conduct outlining expectations for artificial intelligence development in the NHS,
covering aspects such as the appropriate handling of data, the need for algorithmic
transparency and accountability.
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The code states that, in combination with the conformité européenne (CE) mark certification,
health research and relevant regulatory approvals, it should provide an overall policy
and structure for the creation of safe and effective artificial intelligence. The
code, however, is only in its initial consultation stage. This paper discusses the
issues highlighted within the code of conduct and the ethical challenges associated
with addressing them to successfully integrate artificial intelligence within the
NHS.
Patient data in training algorithms
Daily, the NHS collects and records vast quantities of data, providing a valuable
opportunity for methods requiring training through large data sets, such as machine
learning.
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Patient data, however, could be potentially misused, and must be subject to relevant
ethical and legal considerations by both developers and within the NHS.
A recent study found that 51% (1020/2000) of people surveyed in the United Kingdom
were concerned about their data privacy as the use of artificial intelligence increases;
this finding was particularly relevant for those with less knowledge about artificial
intelligence capabilities.
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As part of the government’s code of conduct, complying with appropriate legal and
ethical frameworks is required to safeguard patients. However, the growth of machine
learning and its need for real-life data could present an ethical dilemma where patient
data are being used as an exploitable resource for purposes other than those for which
the data were originally collected. This situation has already occurred. The Royal
Free NHS Trust failed to obtain appropriate consent for the use of data from 1.6 million
patients in the development of one of its artificial intelligence applications.
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Repeated episodes such as this one could decrease public trust in the NHS handling
of patient data and might make patients refuse to share their information. Ensuring
that patients have explicitly consented to the use of their personal data in artificial
intelligence development is therefore crucial. The introduction of a national data
opt-out programme for patient information in 2018
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has made it easier for patients to control the use of their data. Additionally, guaranteeing
that patient information continues to be viewed as a valued asset subject to appropriate
ethical and legal considerations, as opposed to a free-for-all for use in training
algorithms, is necessary.
Transparency and accountability
Nearly all artificial intelligence software will fail to perform its intended purpose
at some point during its lifetime.
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Although in some sectors, artificial intelligence failures can be trivial, failures
in the health-care sector can have catastrophic consequences. Therefore, the ability
to hold the responsible party accountable is vital. However, the assignment of accountability
in artificial intelligence, and specifically machine learning, can be challenging,
primarily due to the lack of transparency. Some studies suggest that humans may no
longer be in control of what decision is taken and may not even know or understand
why a wrong decision has been taken, because transparency is lost.
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Currently, artificial intelligence within the NHS is a tool to support staff rather
than a decision-maker, meaning medical professionals are held accountable for decisions
regardless of whether their decisions were influenced by artificial intelligence technology
or not. The fact that NHS professionals can be held accountable for decisions influenced
by potentially inaccurate artificial intelligence, which cannot be proven in some
situations due to lack of transparency, may deter them from embracing the technology.
Although the code of conduct highlights the importance of transparency, at present,
ensuring this transparency may not be entirely possible and, if artificial intelligence
is to take on a greater role in supporting clinicians in decision-making, the issue
must be addressed.
The development of transparent machine learning techniques would benefit the accountability
dilemma in artificial intelligence and address the previously mentioned regulatory
issues, and therefore is an important area for study. Tackling transparency in artificial
intelligence, specifically machine learning, can be challenging; however, research
suggests it could be possible. Scientists have recently identified methods for developing
transparent deep learning neural networks.
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Furthermore, developers tackled transparency issues, while improving artificial intelligence
software for analysing ophthalmologic images, by displaying selected information regarding
how the software arrived at its recommendation.
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Despite these promising examples, technological solutions to making machine learning
transparent are still in their infancy and will require developers with expertise
in this fast-moving field.
Public trust
One of the key messages highlighted throughout the code of conduct is that gaining
public trust is a high priority and crucial to artificial intelligence’s successful
deployment within the NHS. Current reports, however, suggest that the NHS is in a
less than desirable position, with a recent poll finding that only 20% (400/2000)
of respondents support the use of artificial intelligence in health care,
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demonstrating the scale of the problem. Although there are numerous artificial intelligence
success stories from recent years, these have arguably been outshined by catastrophic
failures, which have inevitably dented the public’s already limited trust in the use
of artificial intelligence. High-profile incidents, in combination with unfamiliarity
and a lack of understanding present a significant problem.
Public trust in the use of artificial intelligence is essential for its growth, and
relevant parties, including the government, must adopt measures to gain this trust.
Efforts need to be made not only to ensure that patients are sufficiently empowered
with education on current technology, but also reassured that the technology is safe
and developed according to relevant technical and ethical standards.
Outlook
Ultimately, the use of artificial intelligence, especially machine learning, within
the NHS has the potential to significantly improve patient care if measures are put
in place to address current barriers. Due to the significant volume of patient data
required in artificial intelligence development, developers and providers must adhere
to data regulation and control to avoid inappropriate data exploitation. Data use
must be subject to relevant ethical and legal considerations, which will require research
attention from social scientists, philosophers and bioethicists working in applied
health sciences and lawyers collaborating with health-care innovation teams, and providers
to secure appropriate permissions and safeguards. A more joined-up, cross-disciplinary
working model between academics and lawyers alongside NHS partners (technical and
clinical) is needed at the earliest stage of every project to inform implementation.
Addressing data use could diminish transparency and accountability concerns by ensuring
the logic behind algorithms is available and clear. Training and capacity building
in the technical aspects is needed to create expertise in how to investigate and make
sense of system failure. Such focus within the discipline is improving and those working
on this area of computing sciences will need to develop robust and transparent practices,
and identify means of explaining technical processes to non-experts. An updated code
of conduct is needed to guide practices and/or a benchmark gold standard codebreaker
process. An example of such work is the Realizing Accountable Intelligent Systems
project, an academic collaboration to develop auditing systems for artificial intelligence.
Finally, with low levels of public trust in artificial intelligence, educating the
public, ensuring that artificial intelligence policy is accessible and providing reassurance
that software has been developed according to regulatory and safety standards, are
needed. The growth of artificial intelligence within the NHS requires political attention
to address current policy limitations and gaps within NHS trusts, and may also benefit
from nation-wide unity on how to approach artificial intelligence use across the NHS.
Academic research, including embedded stakeholder engagement, will be required to
inform the development of such policies, and advocacy will be needed to promote these
policies, using a range of science communication approaches for multiple public audiences.
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Although it may be some years before the NHS is fully able to use the potential of
artificial intelligence, it is time to prepare for its growth.