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AI and Machine Learning are at the heart of future healthcare.

We live in a world that is being run by data, an immense amount of data.

Today, we can collect genetic data, environmental data, and an array of biological data such as microbiome and immunome data. It is this abundance of data that can be collected to give insight into overall health which is central to achiveing more “personalised healthcare”.

Because of the influx of the different types of information we can hold for an individual, the future is relying on data management solutions to influence the way we store, use, and protect information. Moreover, we are in need of smarter ways to manipulate this data into meaningful, actionable insights and therefore, the future of healthcare heavily relies on artificial intelligence and machine learning.

When personalised medicine becomes routine, machines will be able to store the data on electronic health records, and use technology to help make personalised options that help clinicians to make more effective, efficient treatment decisions.

AI can transform the way in which clinicians work by giving them the option to handle the data more accurately, quickly and with an efficiency that would otherwise not be capable. However, there are some that distrust the applications of AI and fear that the future looks like it will remove the personal touch from healthcare. We are already seeing machines capable of more accurate iamge analysis that oncologists or radiologists.

According to this blog post, Estonia is leading the way in terms of digitization and data ownership, while China is implementing AI in their hospitals and the UK is using voice to text applications to save doctors time typing. In addition, AI/ML is helping find insights into rare diseases, where insights come so rarely, and enormous data sets are needed.

Whilst many are wary to AI infiltration, especially in healthcare, times are changing. Last year, the UK the government announced a boost for AI in healthcare by opening a national laboratory in England, where the lab will focus on technologies that are likely to benefit the health system and patients, including algorithms to predict demand for hospital beds and tools that can be used to aid in diagnostics. Likewise, Microsoft reported an “encouraging increase” in the use of AI in the UK health system.

As we move towards a more digitised health system, here are four examples of how the NHS is currently using AI:

  • Addenbrooke’s hospital in Cambridge uses Microsoft’s InnerEye system to scan patients with prostate cancer. The system scans the image, outlines the prostate, marks the tumours and sends a report to the clinician.
  • The NHS uses HeartFlow’s AI technology to analyse CT scans of patients with suspected coronary artery disease and creates personalised 3D models of their heat to show how the blood is flowing around it. This type of analysis is considerably cheaper and less invasive than angiograms and allows the clinicians to look for where there are there are blockages.
  • GPs and nurses in Merton and Wandsworth use the app “C the Signs” to help identify patients at risk of cancer earlier. This can help prioritise further investigations that the patient might need, and potentially prevent the cancer being diagnosed at a much later stage when the disease has significantly progressed.
  • Moorfields Eye Hospital has been using Google’s DeepMind to help clinicians improve the way eye conditions are diagnosed and treated. The technology can automatically identify sight-threatening conditions within seconds and rank the patients in order of urgency for treatment.

As we broaden the opportunities of AI and ML to create a faster, more cost-efficient health service, the role of the doctor is not to be replaced by AI, but to aid them. As we develop these technologies, educating clinicians on how the technology works, it’s accuracy, and what this means for their utility in the clinic will enable them to make the best decisions for their practice.