The implementation of Artificial intelligence (AI) is reaching to cover more and more areas, transforming the way we look at logistics, manufacturing, education and others. Increasingly, it looks like healthcare is shaping up to be the next big beneficiary of this most cutting-edge technology.
As we head into 2021, all eyes are on one area where healthcare and AI are meeting in particular; medical diagnosis.
How medical diagnosis benefits from Artificial Intelligence
The application of AI to medical diagnosis is already pushing the boundaries of what we expect in terms of detection, and particularly early detection. In fact, it is already being proved that AI software can discern whether a patient has a specific disease before any of the evident symptoms, which would traditionally inform a diagnosis, can be seen.
These astonishing results are being achieved thanks to 'deep learning', a form of machine learning that sees the AI technology use multiple layers to extract progressively more detailed information when examining the raw inputted data.
It is a technique that is already yielding results. In a recent study, the US Journal of the National Cancer Institute showed an AI system detecting breast cancer with the same average level of accuracy as a trained radiographer.
Further to the NCI study, the latest research from Google has shown that a neural network can actually be trained to diagnose the tell-tale signs of lung cancer faster than a radiographer. The techniques needs to be validated with a larger audience and undergo further testing on a greater sample, but the results so far are more than encouraging.
Applications of AI in medical diagnosis
The diagnostic detection of diseases is at the forefront of AI's rollout into healthcare and biomedicine. We've already seen several breakthroughs of the type above, but how does it work? One of the most promising methods sees in vitro diagnostics using biochips and sensors. Machine learning is then used to analyse gene expression, a key way to diagnose cancers, with the AI interpreting the data to find and classify any abnormalities that it discovers.
Cancer detection is not the only area that is being improved by AI. Using point-of-care testing (POCT) alongside and biosensors, cardiovascular diseases are also being caught while still in the early stages.
Meanwhile, medical imaging is also benefiting from AI. One-dimensional signal processing is using AI to extract signal features in electroencephalography (EEG), which is commonly used to predict epileptic seizures, helping to minimise the negative impact on patients. The use of AI in multidimensional imaging, image segmentation and thermal imaging has also been shown to improve picture quality, improving the efficiency of analysis by some margin.
It's not just state of the art technology in the world's best hospitals and labs that will be able to reap the rewards that an increased use of AI will bring. Rolling AI out to portable devices, such as ultrasound scanners, will allow even untrained personal to use them to effectively diagnose a wide array of illnesses and improve quality of life in underdeveloped regions.
How big data informs the use of AI in medical diagnosis
As learning algorithms interact with greater amounts of training data, they become more precise and accurate. This, in turn, is allowing AI to offer newer insights when it comes to medical diagnostics, as well as providing better treatment options and, ultimately, improving patient outcomes.
This leap forward is being aided by a significant increase in health care data, the scope of which allows for great improvements in the efficiency and efficacy of patient care in a relatively short time frame.
This 'big data' typically comes from sources such as Electronic Medical Records (EMR), as well as wearable health trackers like FitBits, which AI allows us to analyse in new ways. AI is also inherently well-suited to carrying out repetitive tasks and working processes, allowing it to cope with large amounts of data with ease to provide another layer of decision support that can help to mitigate potential errors.
The future of AI in medical diagnosis
Clearly, AI is already transforming the medical diagnosis sphere, providing analysis on a vast amount of varied data within in moments.
Going forward, it is expected that AI will continue to take on a supporting role for physicians, allowing doctors the opportunity to spend more time tending to parents by removing the need for them to be involved in repetitive, time-consuming routines. This should also lessen the workload in what is can be an incredible stressful working environment.