Why AI Can Revolutionise Healthcare Possibilities

StrategyDriven Editorial Perspectives Article |AI in Healthcare|Why AI Can Revolutionise Healthcare PossibilitiesAI and machine learning are technologies which come with many promises. They are still advancing but, with the rate of development seen in recent years of numerous technologies in various industries, what it could look like in the very near future is potentially more impressive than we could imagine now. The promises are wide-reaching. They describe improved efficiency of information streams which will optimise, for example, urban planning and traffic management and financial services will benefit from improved data storage and modelling. Those are just two examples. The final promise is that AI will achieve a human level of intelligence. However, its more immediate impact is on human life.

For Healthcare

AI is making grounds in many industries. Recently, in the entertainment industry, YouTube used AI to organise an ‘infinite music video’ for Billie Eilish’s ‘Bad Guy’, in celebration of it hitting one billion views. One notable area for AI and machine learning’s application is in healthcare. The use of data in healthcare is widespread: its collection, entry, use, and modelling. AI and machine learning is and will continue to improve the process and product of this practice.
Data collection is constant. Patients are tested and retested regularly and their medical data is, therefore, updated. If – the more appropriate word is probably ‘when’ – instruments and machines are linked with each other and to databases – especially with the introduction of 5G wireless technology – data entry will be much easier, automated, and AI will have much more effective access to it.

This access will enable – with other data inputted from other areas, such as lifestyle choices and habits, to flesh out the profile (should that be permitted) – AI to make better and faster diagnoses and prognoses than the average doctor. This could help in two ways. One is that having better and faster diagnoses and prognoses is never bad. Two is that this could lighten the workload of human doctors and specialists, and mean that their presence can be prioritised elsewhere.

Patient Care

London tech entrepreneur Tej Kohli pours $100m into AI & machine learning ventures, which will help advance his humanitarian initiatives. His foundation is widely known for its commitment to curing corneal blindness, with other efforts including Open Bionics (a bionics and prosthetics company), Aromyx (who measure and digitises taste and scent), and Seldon (an open-source platform that allows developers and organisations to share data and train models and systems). This means AI and machine learning isn’t just about the data collection which aids diagnosis and prognosis. They will have a direct impact on patient care. Robotics and other advanced technology is making its presence felt already – for instance, in surgery (inspiring the ‘they did surgery on a grape’ meme).

The discovery and development of pharmaceuticals is an expensive and time-consuming process. The consequence of this falls to patients. AI will streamline this whole process: analysis of literature and molecules, simulations for theoretical human test subjects and real animal test subjects, and testing during the clinical trials. Moderna are another example of this, as they have used AI and cloud computing to develop personalised cancer treatments in short periods of time.

AI’s and machine learning’s steady implementation has begun to accelerate with recent developments and will only continue to find itself more often in the health sector now it’s being used more readily, as use breeds improvement.

How Impactful Is Machine Learning In Today’s Business World?

Since 2012, with the proliferation of Python in general software development, Machine Learning has become the biggest trend in the technology world. Because of the many applications that ML could have within every business, it’s quite easy to understand why the topic is so heavily looked after. With applications ranging from the mobile world to the automotive industry, let’s break down Machine Learning in its complexity.

Why Automation Is Important

Machine learning as a matter it’s appealing because it proposes as an automated form of management for both infrastructures and digital tools. The brightest example would surely be related to warehouse management and production, where robots are the majority of the entire chain. In this case, a Machine Learning coded program could definitely be implemented in terms of management: the tool, installed in the central brain, could heavily optimise the entire production chain.

On the other hand, automation is as fragile as it could be: a simple error in the mainframe could cause a series of cascade malfunctions, naturally leading the entire production process to the end.

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The Mobile Sector

Smartphones have been taking over the world since the launch of the first iPhone. Many mobile app development companies, in fact, are heavily working on different Machine Learning algorithms, in particular, the ones that are UX (User Experience) focused.

Applications like Alexa’s voice search, for example, have been incredibly popular from a development point of view, given the fact that Voice Search Optimisation will be potentially the biggest thing in the near future.

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Service Providers

When it comes to personal finance and mortgages, there is a variety of tools available online which are calculating requirements, status and projections. An interesting case study would be related to bridging loans, given the fact that fast-paced finance is heavily looked after by many clients. There are reasons to believe that in the near future most of the calculators, online tools and projections will automatically be generated by Python coded applications. This is incredibly important because it states the fact that the current Machine Learning development level is at a point in which we are able to create and monitor complex finance matters.

To Conclude

With dozens of applications available, machine learning is definitely going to be the biggest focus in the near future, given the fact that automating certain sides of a business can definitely be a very impactful thing.

About the Author

StrategyDriven ContributorPaul Matthews is a Manchester based business writer who writes in order to better inform business owners on how to run a successful business. You can usually find him at the local library or browsing Forbes’ latest pieces.