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Artificial Intelligence in Healthcare.

A key challenge for future governance of AI technologies will be ensuring that AI is developed and used in a way that is transparent and compatible with the public interest, whilst stimulating and driving innovation in the Healthcare sector.

Artificial Intelligence in Healthcare.

Wednesday March 27, 2019,

7 min Read

• AI is being used or trialed for a range of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery.

• AI has the potential to help address important health challenges, but might be limited by the quality of available health data, and by the inability of AI to display some human characteristics.

• The use of AI raises ethical issues, including: the potential for AI to make erroneous decisions; the question of who is responsible when AI is used to support decision-making; difficulties in validating the outputs of AI systems; inherent biases in the data used to train AI systems; ensuring the protection of potentially sensitive data; securing public trust in the development and use of AI technologies; effects on people’s sense of dignity and social isolation in care situations; effects on the roles and skill-requirements of healthcare professionals; and the potential for AI to be used for malicious purposes.

• A key challenge will be ensuring that AI is developed and used in a way that is transparent and compatible with the public interest, whilst stimulating and driving innovation in the sector.


There is no universally agreed definition of AI. The term broadly refers to computing technologies that resemble processes associated with human intelligence, such as reasoning, learning and adaptation, sensory understanding, and interaction. Currently, most applications of AI are narrow, in that they are only able to carry out specific tasks or solve pre-defined problems. AI works in a range of ways, drawing on principles and tools, including from math, logic, and biology. An important feature of contemporary AI technologies is that they are increasingly able to make sense of varied and unstructured kinds of data, such as natural language text and images. Machine-learning has been the most successful type of AI in recent years, and is the underlying approach of many of the applications currently in use. Rather than following pre-programmed instructions, machine learning allows systems to discover patterns and derive its own rules when it is presented with data and new experiences.



AI has the potential to be used in planning and resource allocation in health and social care services. For example, the IBM Watson Care Manager system is being piloted by Harrow Council with the aim of improving cost efficiency. It matches individuals with a care provider that meets their needs, within their allocated care budget. It also designs individual care plans, and claims to offer insights for more effective use of care management resources.

AI is also being used with the aim of improving patient experience. Alder Hey Children’s Hospital in Liverpool is working with IBM Watson to create a ‘cognitive hospital’, which will include an app to facilitate interactions with patients. The app aims to identify patient anxieties before a visit, provide information on demand, and equip clinicians with information to help them to deliver appropriate treatments.


AI can be used to analyse and identify patterns in large and complex data sets faster and more precisely than has previously been possible. It can also be used to search the scientific literature for relevant studies, and to combine different kinds of data; for example, to aid drug discovery.The Institute of Cancer Research’s canSAR database combines genetic and clinical data from patients with information from scientific research, and uses AI to make predictions about new targets for cancer drugs. Researchers have developed an AI ‘robot scientist’ called Eve which is designed to make the process of drug discovery faster and more economical. AI systems used in healthcare could also be valuable for medical research by helping to match suitable patients to clinical studies.


AI has the potential to aid the diagnosis of disease and is currently being trialed for this purpose in some UK hospitals. Using AI to analyse clinical data, research publications, and professional guidelines could also help to inform decisions about treatment. Possible uses of AI in clinical care include:

• Medical imaging – medical scans have been systematically collected and stored for some time and are readily available to train AI systems. AI could reduce the cost and time involved in analyzing scans, potentially allowing more scans to be taken to better target treatment. AI has shown promising results in detecting conditions such as pneumonia, breast and skin cancers, and eye diseases.

• Electrocardiography – the Ultrasonic system, trialed at John Radcliffe Hospital in Oxford, uses AI to analyse electrocardiography scans that detect patterns of heartbeats and diagnose coronary heart disease.

• Screening for neurological conditions – AI tools are being developed that analyse speech patterns to predict psychotic episodes and identify and monitor symptoms of neurological conditions such as Parkinson’s disease.

• Surgery – robotic tools controlled by AI have been used in research to carry out specific tasks in keyhole surgery, such as tying knots to close wounds.


Several apps that use AI to offer personalized health assessments and home care advice are currently on the market. The app Ada Health Companion uses AI to operate a chat-bot, which combines information about symptoms from the user with other information to offer possible diagnoses. GP at Hand, a similar app developed by Babylon Health, is currently being trialed by a group of NHS surgeries in London.

Information tools or chat-bots driven by AI are being used to help with the management of chronic medical conditions. For example, the Arthritis Virtual Assistant developed by IBM for Arthritis Research UK is learning through interactions with patients to provide personalized information and advice concerning medicines, diet, and exercise. Government-funded and commercial initiatives are exploring ways in which AI could be used to power robotic systems and apps to support people living at home with conditions such as early stage dementia, potentially reducing demands on human care workers and family carers.

AI apps that monitor and support patient adherence to prescribed medication and treatment have been trialed with promising results, for example, in patients with tuberculosis. Other tools, such as Santeria, use AI to analyse information collected by sensors worn by patients at home. The aim is to detect signs of deterioration to enable early intervention and prevent hospital admissions.


AI has the potential to be used to aid early detection of infectious disease outbreaks and sources of epidemics, such as water contamination. AI has also been used to predict adverse drug reactions, which are estimated to cause up to 6.5 per cent of hospital admissions in the UK.


In the future, it is likely that AI systems will become more advanced and attain the ability to carry out a wider range of tasks without human control or input. If this comes about, some have suggested that AI systems will need to learn to ‘be ethical’ and to make ethical decisions. This is the subject of much philosophical debate, raising questions about whether and how ethical values or principles can ever be coded or learnt by a machine; who, if anyone, should decide on these values; and whether duties that apply to humans can or should apply to machines, or whether new ethical principles might be needed.