The future of AI in healthcare

AI is revolutionising how the healthcare sector works to reduce spending and improve patient outcomes, with total savings set to hit $150 billion per annum by 2026.

Investment by the healthcare sector in Artificial Intelligence is growing at a prolific rate as governments and businesses continue to realise the benefits of implementing technology solutions in clinical research and practice, drug development, insurance and medical process.

As the healthcare sector continues to evolve and adopt new technologies, clear communication is required for both planned and unplanned changes and challenges that arise. So too, is a deep understanding of the innovations available to the healthcare sector and its impact.

Four ways AI is revolutionizing healthcare

1. Diagnosis and treatment planning

AI algorithms can help doctors analyse medical images and make diagnosis or recommend treatments. For example, the average radiologist currently reads and interprets an image every four seconds, for eight hours a day.

AI has the potential to support physicians by analysing and processing medical images more accurately and efficiently through pattern recognition in datasets, greatly reducing the margin of human error.

The use of AI in medical imaging enables computer systems to flag acute abnormalities, detect and classify diseases, predict stroke outcomes and assist in the management of chronic diseases. In turn, this allows physicians to provide an accurate and personalised diagnosis more efficiently.

2. Predictive modelling

AI can be used to analyse large amounts of patient data and make predictions about future health outcomes. This can help doctors identify at-risk patients and intervene early to prevent negative outcomes.

Understanding and articulating how this process works is important to acknowledge and share with patients and staff to instil confidence and flag potential risks.

3. Population health management

AI can be used to analyse population-level data and identify trends and patterns that can inform public health policies and interventions. This can be a powerful tool in decision making and communicating justification for such decisions.

4. Non-Clinical Workstreams

The benefits of AI to non-clinical workstreams are also significant, with improvements seen across human resources (HR), finance, and customer databases.

AI allows staff to streamline operational tasks throughout HR and finance by automating functions such as payroll, recruitment, and performance management to improve workflows.

The move towards AI and an online cloud model has also allowed clinics and hospitals to collect data in real time and automatically generate patient reports.

Ethical considerations

It is important to acknowledge the ethical implications that arise with the use of AI in the healthcare industry. Two key ethical issues that have raised concerns are the question of data privacy and the potential for AI computer bias.

Allowing machines access to customer data poses an unprecedented threat to patient privacy. As patients provide excess data and more patterns are identified across multiple vendors, the safety of patient data greatly diminishes. The use of multiple third-party vendors to analyse data means patients lose agency in terms of data usage, while regulatory bodies are unable to sustain safeguards and assurances at the rate at which the AI systems are expanding.

Another prevalent issue of AI is the inherent bias where the success of code used to write the systems is largely dependent on adequate population representation. This is exacerbated in the healthcare industry where an underrepresentation of minority groups can lead to incorrect diagnosis. It is important that AI systems have access to diverse, high-quality data mapped using a data-centric approach to minimise the likelihood of an incorrect diagnosis based on a lack of representative data.

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If you require expert support to transition your business or communicate confidence to customers, consumers and employees alike in relation to Artificial Intelligence, please contact Group Executive Director Health and Care, Rebecca Williams.


Artificial Intelligence in Medical Diagnostics Systems Report 2020 (

The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload – PubMed (