One of the hottest tech trends these days is artificial intelligence (AI), with researchers looking into the use of AI for applications ranging from autonomous vehicles to financial management, to healthcare. The healthcare industry is often at the forefront of innovation and technological advances due to the wealth of medical devices, equipment and processes that permeate the industry. But AI in particular seems poised to transform the way we collect, understand and use data on patient health, healthcare services and historical health data to revolutionize medical diagnostics, treatment and research. What makes AI so suitable for use in medical research and the healthcare industry? Largely, the appeal of AI is its ability to collect, analyze and make sense of vast amounts of unstructured and variable data--especially text, statistical numbers, and visual images--quickly and often more accurately than a human being.
Artificial intelligence simplifies the lives of patients, doctors and hospital administrators by performing tasks that are typically done by humans, but in less time and at a fraction of the cost. One of the world's highest-growth industries, the AI sector was valued at about $600 million in 2014 and is projected to reach a $150 billion by 2026. Whether it's used to find new links between genetic codes or to drive surgery-assisting robots, artificial intelligence is reinventing -- and reinvigorating -- modern healthcare through machines that can predict, comprehend, learn and act. Check out these 32 examples of AI in healthcare. In 2015, misdiagnosing illness and medical error accounted for 10% of all US deaths. In light of that, the promise of improving the diagnostic process is one of AI's most exciting healthcare applications.
After revolutionizing various industry sectors, the introduction of artificial intelligence in healthcare is transforming how we diagnose and treat critical disorders. A team of experts in the Laboratory for Respiratory Diseases at the Catholic University of Leuven, Belgium, trained an AI-based computer algorithm using good quality data. Dr. Marko Topalovic, a postdoctoral researcher in the team, announced that AI was found to be more consistent and accurate in interpreting respiratory test results and in suggesting diagnoses, as compared to lung specialists. Likewise, Artificial Intelligence Research Centre for Neurological Disorders at the Beijing Tiantan Hospital and a research team from the Capital Medical University developed the BioMind AI system, which correctly diagnosed brain tumor in 87% of 225 cases in about 15 minutes, whereas the results of a team of 15 senior doctors displayed only 66% accuracy. The introduction of technologies such as deep learning and artificial intelligence in healthcare can help achieve more efficiency and precision.
There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human. I am certain that healthcare will be the lead industrial area of such a revolution and one of the major catalysts for change is going to be artificial intelligence. With the evolution of digital capacity, more and more data is produced and stored in the digital space. The amount of available digital data is growing by a mind-blowing speed, doubling every two year. In 2013, it encompassed 4.4 zettabytes, however by 2020 the digital universe – the data we create and copy annually – will reach 44 zettabytes, or 44 trillion gigabytes (!).
Is Artificial Intelligence (AI) the silver bullet that will make doctors all over the world unemployed? Will AI be able to outperform oncologist in creating treatment plans for cancer patients? Keep reading and get new perspectives on healthcare AI as I untangle opportunities and grand challenges within the field. "Too much information, too little time" is one of the big challenges in healthcare today. Patients, healthcare professionals and medical devices generate huge amounts of data.