Technological Advancements in AI
Research in AI for various applications has been ongoing for a number of decades. As a result, there have been rapid technological advances within the field of AI and data science. This is predicted to make a paradigm shift in the integration of AI technology and its adoption and impact on society. The combination of increased computer processing speed, big data libraries and an increasing population of AI talent has enabled the rapid development of AI technology, specifically within healthcare.
Much of the recent hype surrounding AI applications is due to the development of deep learning (DL), which has an unprecedented impact on the way we benefit from AI algorithms today. DL allows finding correlations that were too complex to render using previous machine learning algorithms. Currently, tech giants are developing projects such as IBM Watson and Google’s Deep Mind, that are frontrunners in this area. These companies have demonstrated that AI can outperform the skills of humans in various thinking-related activities such as chess, Go, and other games.
Both IBM Watson and Google’s Deep Mind are currently being used for a number of healthcare-related applications. IBM Watson is employed to investigate the management of diabetes, advanced cancer care and modelling, and drug discovery, but has yet to show clinical value in patient care. Deep Mind is also being looked at for applications including mobile medical assistant, diagnostics based on medical imaging, and prediction of a patient’s clinical progress.
Meanwhile, many complex data and computation-based technologies have experienced exponential growth trajectories. The most well known example is that of Moore’s law, which is behind the explosion in the performance of computer chips. On the back of this, many consumer-oriented apps have followed similar growth patterns by offering cost-effective services in a rapidly growing market. When that is translated into healthcare and life science, the mapping of the human genome and the digitisation of medical data could result in similar growth patterns.
Hence, as genetic sequencing and profiling becomes cheaper, and electronic health records (EHRs) and other applications serve as a platform for data collection, we will start to see a paradigm shift in the digital transformation of the healthcare system. Although these areas may seem small-scale at first, the speed of transformation will expand the growth opportunities in other areas too. We are thus at the cusp of an exciting new era in the way medical services are delivered to patients across the world.
