Artificial Intelligence in Healthcare – Reimagining its Reality
With the ever-expanding nature of artificial intelligence, one feels certain its adoption in most industries isn't too far from being reality.
But when it comes to the healthcare industry, most still have their reservations despite it already being tested and utilised.
Read on to find out how AI is used in healthcare today.
Artificial Intelligence has been revolutionizing industries left, right and centre and the excitement around it is legitimate. The potential of what AI can do for the healthcare industry is still to be fully realised – but it’s off to a good start. We’ve discussed how artificial intelligence in healthcare can exist in the future in a previous post so today, we’ll be covering how its implementation is in place at present.
What is Artificial Intelligence?
Popular media remains the go-to source for the misconceptions surrounding AI today. And this makes its implementation in healthcare all the more difficult. With an industry posited for human wellbeing, letting it run (completely or partially) by software or machines makes the entire endeavour sound like a colossal oxymoron.
Understanding AI is important and today, it is the shorthand for any task a computer can execute just as well as humans, if not better. But there isn’t just the one form of computer intelligence available when its role in medicine is discussed. Most AI-based healthcare solutions do not rely on complete automation but on algorithms created by humans to analyse data and then suggest relevant treatments.
There’s machine learning, for instance. This form of computer learning is based on neural networks (similar to how biological brains function) and can involve multilevel probabilistic analysis. This allows computers to simulate and extrapolate solutions as a human mind would. But its obvious caveat today is the fact that because of its complicated nature, not even its programmers can predict how the algorithm will derive solutions.
Lastly, and perhaps most important to the healthcare industry, is deep learning. This involves the software to learn patterns in layers. Deep learning can work independently in layers of information and altogether too and this allows it to analyse multiple aspects of medical data before forming an educated conclusion. It possesses the power to transform a diagnostic medium in healthcare and can search for cancerous tissues at a cellular level.
Common AI in Healthcare Misconceptions
As mentioned previously, artificial intelligence in healthcare isn’t an idea without its critics. While the best is still to come for the industry, how quickly and efficiently we allow its integration is what matters. The effect of technology has the ability to be overestimated in the short term and underestimated in the long run – and AI isn’t immune to this phenomenon. Here are a few misconceptions plaguing the issue today:
Artificial Intelligence in Healthcare will replace Clinicians
It is optimistic at best to think of AI as a complete human replacement and in the healthcare industry, it’s still a worry that’ll take years to be grounded in reality. At the moment, specialised diagnostic roles in medicine like cardiologists and radiologists are the ones concerned. This is due to the misconception that AI will take over the diagnosis spectrum completely. The truth is somewhere in the middle. While automating the process has numerous benefits, clinicians will be required to teach the machines in the many years to come. AI in healthcare is like a GPS device. Human engagement and oversight might always be required as the final verdict.
Deep Learning is All There is And Will Be
Deep learning is powerful, but it is only the beginning of the true potential of AI. Cognitive technologies that truly rival human intelligence and thinking processes are still decades away. Simply put, machines are faster than humans but less intelligent at present. There’s still a way to go before this changes. And while deep learning may be incredibly data-hungry, an evolution in this process is occurring today. Data scientists are moving towards “small data” or “little data” already. This is sparked due to the fact that for obscure and rare medical conditions, deep learning falls flat. With such pitfalls, deep learning is bound to change and perhaps, be replaced.
You Need To Be a Programmer Too
When the beneficial evolution of artificial intelligence in healthcare is in question, it isn’t necessarily important to be a programmer or developer to make a viable contribution. This is especially true for clinicians and other professionals in the medical industry who can help by analysing the foundational layer of AI. This would help kickstart the progression from data to information to knowledge and then eventually intelligence.
Artificial intelligence in Healthcare Today
Today, the biggest problem in healthcare isn’t the difference between or their levels of intelligence, but rather how they approach patient problems. This combined with the differences in health systems supporting them, cause a wide variety of clinical outcomes globally. This is exactly the area where machine learning and AI can be brought in to help streamline the process.
Two AI approaches exist today: natural-language processing and computer shadow learning – and both have the potential to improve physician performance. Natural-language processing is an offset of AI which can help computers to understand and interpret human speech and handwriting. Not only can it review a multitude of detailed and comprehensive medical records (electronic and paper), but evaluate the best steps to help manage patients suffering from multiple illnesses. Computer shadow learning is simpler in operation. This involves using computers to watch and learn from the doctors at work.
Forward is a San Francisco based primary care startup with this methodology of utilising AI in practice. The company uses its expertise in artificial intelligence to find unique ways to help machines learn from skilled doctors in real-time. This involves having the AI follow and learn from the doctors every step of the way, rather than have doctors input data prior and following procedures and assessments.
The advantages of AI in healthcare are clear, and the biggest barrier to its future success is the misconceptions surrounding it. Medical culture is another culprit where doctors and medical professionals cling to their independence and dislike the idea of a machine analysing their processes. What needs to change is the general attitude surrounding AI as bringing the performance of all physicians to the top worldwide 20%, could help decrease diseases in the hundreds of thousands every year. AI may just be the way to get there.