Healthcare with Artificial Intelligence and Machine Learning

Healthcare with Artificial Intelligence and Machine Learning

Advancements in computer sciences have led to some exciting discoveries in the past few years. We finally have the computing power that is required for advanced Neural Networks of Artificial Intelligence and Machine Learning systems to take advantage of massive pools of data, but how does this affect critical services like healthcare, and what are some of these potential impacts?

Improved Patient Diagnostics

The way that AI systems are able to leverage Machine Learning is said to be one of the driving forces behind the adoption of these technologies in healthcare in 2017. There is a vast array of practical applications that this can be applied to, such as diagnosing patients, tracking viral mutations and even predicting hereditary diseases based on genetic sequencing information. Once enough data has been collected and analysed, these healthcare systems could be able to predict patient outcomes based on their patient history files as well as real time information on a patients current status.

This would then form the basis of a powerful new toolkit that doctors and healthcare professionals could use to verify and form new treatment plans with. While this may all sound farfetched and almost science fiction, the groundwork is currently being laid for AI systems and healthcare providers to form meaningful partnerships that will help realise the lofty ambitions of these companies. Start-up businesses in the AI Healthcare field have started to multiply, with no signs of this growth slowing down anytime soon.

Creating Enhanced Treatment Plans

Another way that AI is going to be used is in the formulation of advanced treatment plans for superior patient outcomes, and not just for specific people. As the system learns how positive patient outcomes are achieved, it will then compile and correlate this data, making it available to other systems so that advanced treatments with higher probabilities of positive patient outcomes are far more likely. This will make the job of a doctor treating a patient as more of a supervisory role, there to double check any suggestions that come from the AI system, and to override anything that comes across as being risky or against hospital protocols.

The longer these systems are in place for, the more efficient and accurate they will get when developing and choosing treatment plans for patients. As has been evidenced in recent Machine Learning examples such as Deepmind’s AlphaGo and Open AI’s Dota2 bot. AlphaGo Zero is now capable of teaching itself how to play games, and once it has learned a game from deducing the rules and by figuring out what a positive outcome looks like, it is vastly superior to the human trained version of the same Machine Learning platform. This means that these new AI systems have the potential to identify health issues and select corrective actions for them in ways that we haven’t even yet imagined.