Principles for Artificial Intelligence (AI)
and its application in healthcare
British Medical Association
bma.org.uk
,British Medical Association Principles for Artificial Intelligence (AI) and its application in healthcare 1
Contents
Executive summary......................................................................................................2
Introduction....................................................................................................................3
1 AI usage in UK health services.......................................................................4
1.1 Defining Artificial Intelligence........................................................................ 4
1.2 Examples of the application of Artificial Intelligence
to healthcare........................................................................................................ 4
2 Impact of AI on healthcare – benefits and risks.......................................7
2.1 How does AI have the potential to improve healthcare?........................ 7
2.2 AI brings both benefits and risks................................................................... 8
2.2.1 Health outcomes and patient experience................................................8
2.2.2 Clinicians’ experience of work and system efficiency........................11
2.2.3 The benefits and risks of AI are interrelated..........................................13
2.3 The key issue is how AI is implemented....................................................14
3 BMA principles for AI policy and implementation.................................19
, 2 British Medical Association Principles for Artificial Intelligence (AI) and its application in healthcare
Executive summary
AI in healthcare includes a range of applications aimed at enhancing efficiency,
diagnosis, and treatment. Though AI lacks a single definition, it broadly refers to
technologies simulating human intelligence to perform complex tasks, in the context
of healthcare this includes: healthcare administration, clinical decision-making,
improving diagnostics, personalised treatment, providing digital therapies, population
health data analysis, and biomedical research.
AI holds promise for transforming healthcare by improving precision, efficiency, and
preventive measures. It can enhance diagnostic accuracy, personalise treatments,
and streamline administrative tasks, potentially reducing healthcare demand and
improving outcomes. However, the success of AI depends on its implementation,
including proper testing, integration into workflows, and addressing issues of liability,
regulation, and data governance. Risks include potential harms to patient health,
exacerbation of health inequalities, and impacts on doctor-patient relationships and
productivity. Effective AI use requires careful management to maximise benefits and
mitigate risks, ensuring it complements and enhances existing healthcare systems.
AI should be expected to transform, rather than replace, healthcare jobs by
automating routine tasks and improving efficiency. This could enhance job quality
and reduce burnout by minimising administrative burdens and allowing staff to focus
on complex, value-added activities. However, the implementation of AI including
effective deployment and long-term evaluations, must be managed carefully to avoid
increasing workload and pressures.
Implementing AI in healthcare requires careful consideration beyond merely
introducing new technologies. Key issues include ensuring AI tools are rigorously
tested for safety and efficacy, avoiding reliance solely on lab-based evaluations.
Effective integration into clinical practice is crucial, as poor real-world performance
can arise from inadequate training or data quality. Governance and regulation must
adapt to manage AI’s evolving nature and protect patient safety. Involving staff and
patients in development and implementation, along with continuous training, is
essential. Moreover, robust IT infrastructure and clear legal liability frameworks are
needed to support successful AI adoption in healthcare.
The BMA advocates, as set out in the principles at the end of this paper, for AI in
healthcare to prioritise safety, efficacy, ethics, and equity. Each AI implementation
must be rigorously assessed in real-world settings and continuously monitored to
ensure it improves care quality and job satisfaction without exacerbating inequalities.
Strong governance and up-to-date regulation are essential to protect patient safety.
Involving staff and patients in AI development and providing them with the choice
to opt out or dispute AI decisions is crucial. Training for healthcare professionals and
robust IT infrastructure are necessary for effective AI integration. Legal liability must
be clear, ensuring developers are accountable and doctors can challenge AI decisions.
and its application in healthcare
British Medical Association
bma.org.uk
,British Medical Association Principles for Artificial Intelligence (AI) and its application in healthcare 1
Contents
Executive summary......................................................................................................2
Introduction....................................................................................................................3
1 AI usage in UK health services.......................................................................4
1.1 Defining Artificial Intelligence........................................................................ 4
1.2 Examples of the application of Artificial Intelligence
to healthcare........................................................................................................ 4
2 Impact of AI on healthcare – benefits and risks.......................................7
2.1 How does AI have the potential to improve healthcare?........................ 7
2.2 AI brings both benefits and risks................................................................... 8
2.2.1 Health outcomes and patient experience................................................8
2.2.2 Clinicians’ experience of work and system efficiency........................11
2.2.3 The benefits and risks of AI are interrelated..........................................13
2.3 The key issue is how AI is implemented....................................................14
3 BMA principles for AI policy and implementation.................................19
, 2 British Medical Association Principles for Artificial Intelligence (AI) and its application in healthcare
Executive summary
AI in healthcare includes a range of applications aimed at enhancing efficiency,
diagnosis, and treatment. Though AI lacks a single definition, it broadly refers to
technologies simulating human intelligence to perform complex tasks, in the context
of healthcare this includes: healthcare administration, clinical decision-making,
improving diagnostics, personalised treatment, providing digital therapies, population
health data analysis, and biomedical research.
AI holds promise for transforming healthcare by improving precision, efficiency, and
preventive measures. It can enhance diagnostic accuracy, personalise treatments,
and streamline administrative tasks, potentially reducing healthcare demand and
improving outcomes. However, the success of AI depends on its implementation,
including proper testing, integration into workflows, and addressing issues of liability,
regulation, and data governance. Risks include potential harms to patient health,
exacerbation of health inequalities, and impacts on doctor-patient relationships and
productivity. Effective AI use requires careful management to maximise benefits and
mitigate risks, ensuring it complements and enhances existing healthcare systems.
AI should be expected to transform, rather than replace, healthcare jobs by
automating routine tasks and improving efficiency. This could enhance job quality
and reduce burnout by minimising administrative burdens and allowing staff to focus
on complex, value-added activities. However, the implementation of AI including
effective deployment and long-term evaluations, must be managed carefully to avoid
increasing workload and pressures.
Implementing AI in healthcare requires careful consideration beyond merely
introducing new technologies. Key issues include ensuring AI tools are rigorously
tested for safety and efficacy, avoiding reliance solely on lab-based evaluations.
Effective integration into clinical practice is crucial, as poor real-world performance
can arise from inadequate training or data quality. Governance and regulation must
adapt to manage AI’s evolving nature and protect patient safety. Involving staff and
patients in development and implementation, along with continuous training, is
essential. Moreover, robust IT infrastructure and clear legal liability frameworks are
needed to support successful AI adoption in healthcare.
The BMA advocates, as set out in the principles at the end of this paper, for AI in
healthcare to prioritise safety, efficacy, ethics, and equity. Each AI implementation
must be rigorously assessed in real-world settings and continuously monitored to
ensure it improves care quality and job satisfaction without exacerbating inequalities.
Strong governance and up-to-date regulation are essential to protect patient safety.
Involving staff and patients in AI development and providing them with the choice
to opt out or dispute AI decisions is crucial. Training for healthcare professionals and
robust IT infrastructure are necessary for effective AI integration. Legal liability must
be clear, ensuring developers are accountable and doctors can challenge AI decisions.
