To enable exponential health improvements we need to understand what artificial intelligence is, what we can do with it and how to do that. Artificial Intelligence (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. This the force multiplier that sits on top of all the low cost sensors and patient data inputs. For the A.I possibilities we can then think of D.A.S.H when looking at applications for health transformation. Neural Networks – e.g systems modelled on the brain & nervous system these are often used in deep learning systems and are used in character recognition, time series prediction, expert systems and classification Evolutionary computing – e.g systems modelled on evolutionary programming, evolution strategies and genetic algorithms used to solve complex real world problems e.g in populations or swarms Computer Vision – e.g systems enabling object recognition, image understanding and augmented reality used to automate vision based problem solving.
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.
The idea of Artificial Intelligence (AI) has a long history. It turned out, however, that reaching intelligence at human levels is more complicated than originally anticipated. Currently we are experiencing a renewed interest in AI, fueled by an enormous increase in computing power and an even larger increase in data, in combination with improved AI technologies like deep learning. Healthcare is considered the next domain to be revolutionized by Artificial Intelligence. While AI approaches are excellently suited to develop certain algorithms, for biomedical applications there are specific challenges. We propose recommendations to improve AI projects in the biomedical space and especially clinical healthcare.
Artificial intelligence is slowly, but surely, showing potential in improving modern healthcare. In the UK, researchers recently used four AI algorithms that beat doctors in predicting heart attacks. Moreover, Google's DeepMind is fighting blindness with machine learning. Lately, medical science is seeing potential in the ability of AI systems to find meaning in datasets that are too complicated for us to process. This potential is perfectly applicable in modern healthcare practices.
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 (!).