artificial intelligence in medicine

Artificial Intelligence In Healthcare – How AI Helps Save (& Facilitate) Lives

ByWITS ZEN Editor's Desk

Artificial intelligence technology has come a long way to give us the capabilities we have today. From simple AI tactics generation for chess to complex natural language and image processing abilities and more.

AI now helps empower and improve workflows, services, and operational benefits across industries. But as much as it is still incapable of fully replacing a human specialist, the technology can surely make the lives of such specialists easier and their work more productive.

Among other things, AI has certainly proven to be a highly efficient tool in healthcare. For one thing, it tends to provide more precise results at breast cancer diagnostics than the best live doctors in the world.

But the scope of AI in healthcare is quite wide and expanding. There are various AI-powered methods and tools that medical providers can use right now to offer better, more in-depth, and convenient healthcare services and initiatives.

Their Timely Employment Allows To:

  • Minimize or even completely eliminate human factor errors to boost the accuracy of diagnosis;
  • Reduce risks of surgery infections and reach impossible places with the help of robot-assisted surgery;
  • Thoroughly record and monitor a patient’s state and treatment progress in real time to promptly tackle emergencies and treat patients more reliably overall;
  • Automate diagnosis report generation to take up tons of unnecessary manual work, and more.

Major AI Capabilities To Employ Right Now

It goes without saying that the above-mentioned benefits result in more global benefits for a healthcare provider, helping optimize expenses, eliminate many risks, and contribute to the global development of medicine in the world. In particular, this can be done via a number of AI-driven applications, including the following.

Machine learning

ML-based algorithms enable healthcare systems to learn from their past experiences and generate long-term efficient conclusions based on various inputs.

There are many approaches to handling machine learning in healthcare, spanning different purposes (such as data analytics, forecasting, automated smart diagnostics, system performance optimization, etc.).

Precision medicine is the most common ML-powered field where AI is used to predict the most efficient treatment procedures for individual patients based on their physical attributes. The supervised learning ML principle is employed here as you need input data to enable the system to “learn” and build precise predictions.

Natural Language Processing

Abbreviated as NLP, natural language processing is the underlying AI-powered technology of human speech recognition that has remained among the top priorities of artificial technology for about 50 years of its existence.

This resulted in advanced text analysis and translation capabilities along with huge progress in human speech recognition. In healthcare, NLP can help medical systems properly comprehend and classify documentation. Which can be a yet another human error-optimizing integration.

Computer Vision

Along with NLP, comes CV or computer vision – an ability of the AI-based software to make out, comprehend, and classify objects from real-world images.

This ability can help a lot with the automation of image-based diagnostics. Doctors can be assisted in their efforts to make out issues on x-ray or CT scans with the naked eye. This takes your usual diagnostics a long way, as indicated by the breast cancer detection preciseness example.

Diagnosis & Treatment Solutions

Specialized EHR systems provide diagnosis and treatment optimization capacities that may assist live doctors greatly. The only difficult moment to handle here is the integration with the existing healthcare facility or system.

Most EHR solutions are standalone so you need to find a way to smoothly introduce them to the existing workflow. This requires either customizing software or hiring third-party specialists to help handle integration.

Medical Robotics

Doctors nowadays most beneficially use various robotized systems to get to impossible-to-reach parts of the human body, conduct cleaner surgeries, and handle certain surgical procedures on microscopic levels. CT and x-ray machines can be made smart with the integration of AI.

Today’s AI certainly helps develop medicine and healthcare further on the most unattainable levels. Stay in tune with insights by NIX United to learn about more prospects of relevant technologies in the existing market.

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