AI for Healthcare

There are real opportunities for AI for healthcare, not only to automate some of the problem-solving carried out by doctors and other medical professionals, but also to make quicker and better decisions and apply problem-solving techniques that humans alone could not.

This will improve the cost of care, and improve outcomes, simply because things will happen earlier, faster, and better. Rather than replacing people with machines, creating unemployment, we foresee this as a way of dealing with the growing unmet need for clinical care.


What is AI for Healthcare

Artificial intelligence (AI) and machine learning solutions are transforming the way healthcare is being delivered. Health organisations have accumulated vast data sets in the form of health records and images, population data, claims data and clinical trial data. AI technologies are well suited to analyse this data and uncover patterns and insights that humans could not find on their own. With deep learning from AI, healthcare organisations can use algorithms to help them make better business and clinical decisions and improve the quality of the experiences they provide.

AI for Healthcare

The challenges involved touch on some of the hottest topics in AI research. For example, when you are dealing with people’s health, the explainability of AI, our ability to understand why systems have made certain decisions, becomes much more important. This means dealing with human–machine interaction, trust and the security of AI systems, and questions about autonomy. It also means operating in settings where there is tight regulation.

AI for Healthcare will make things will happen earlier, faster, and better.

AI for healthcare embodies everything that makes AI in general interesting. AI for healthcare can be divided into two broad categories. Perceptual AI replicates the ability of healthcare professionals to perceive disease, a skill that goes to the heart of diagnosis and monitoring. And intervention AI addresses decisions about how patients should be treated.

AI may be used for medical imaging, beginning with a patient being scanned and ending with clinically useful information.  AI may acquire images faster and with better quality, to extract information from the images, and to take this information and turn it into a diagnosis or a prediction about the patient.  Even an experienced doctor may not have seen all types of cancer. Algorithms can pool the data from hundreds of thousands of rare cases.

For purely visual tasks, the AI is learning to emulate what human experts such as radiologists do when looking for signs of a disease such as cancer. Humans have a very good perceptual system, and radiologists are trained to spot many different types of diseases. But when it comes to making predictions about the patient, even an experienced doctor may not have seen all types of cancer, or only a few cases of the rarest cancers.

This is where AI for Healthcare can make a difference. The learning algorithms can pool the data from hundreds of hospitals, with hundreds of thousands of these rare cases, and support the diagnosis of a clinician who will not have had this experience.


Benefits of AI in healthcare

Providing user-centric experiences

Using large datasets and machine learning, healthcare organisations can find insights faster and more accurately with AI, enabling improved satisfaction both internally and with those they serve.

Improving efficiency in operations

By examining data patterns, AI technologies can help healthcare organisations make the most of their data, assets and resources, increasing efficiency and improving performance of clinical and operational workflows, processes, and financial operations.

Connecting disparate healthcare data

Healthcare data is often fragmented and in various formats. By using AI and machine learning technologies, organisations can connect disparate data to get a more unified picture of the individuals behind the data.

This website uses cookies. By continuing to use this site, you accept our use of cookies.