Artificial intelligence (AI), Machine learning, NLP, Robotics, and Automation are increasingly prevalent in all aspects and are being applied to healthcare as well. These technologies have the potential to transform all aspects of health care from patient care to the development and production of new experimental drugs that can have a faster roll-out date than traditional methods. There are numerous research studies suggesting that AI can outperform humans at key healthcare tasks, such as diagnosing ailments. Here is a great example, AI'outperforms' doctors diagnosing breast cancer¹. Artificial intelligence is a collection of technologies that come together form artificial intelligence. Tech firms and startups are also working assiduously on the same issues.
Wikipedia defines artificial intelligence in healthcare as the use of complex algorithms and software to emulate human cognition in the analysis, interpretation and comprehension of complicated medical and healthcare data. This "emulation" is done in less time and at a fraction of the cost. Artificial intelligence in healthcare was valued at about $600 million in 2014 and is projected to reach $150 billion by 2026. Reinventing and reinvigorating healthcare through the use of artificial intelligence is happening predominantly through assisting in better diagnosis, better processes, drug development and robot-assisted surgery. In 2015 misdiagnosing illness and medical error accounted for 10% of all U.S. deaths.
Artificial intelligence and machine learning are quickly becoming an integral part of healthcare delivery. Both on the clinical care and operational side of healthcare organizations, AI has is powering technology that keeps patients safe and improves efficiency for the revenue cycle, supply chain and more. Here are 100-plus companies in the healthcare space using artificial intelligence. To add a company to this list, contact Laura Dyrda at email@example.com. AiCure is an AI and advanced data analytics company that uses video, audio and behavioral data to better understand the connection between patients, disease and treatment. It allows physicians to have access to clinical and patient insights.
Machine learning (ML) is an application of artificial intelligence (AI) wherein the system looks at observations or data, such as examples, direct experience, or instruction, figures out patterns in data and predicts events in the future based on the examples that we provide. Machine learning is seeing more and more use across industries for various reasons: vast amounts of data are being captured and made available digitally; processing of large amounts of data has become cost-effective due to the increased computing power now available at affordable prices; and various open source frameworks, toolkits and libraries are available that can be used to build and execute ML applications. Specifically in healthcare, ML has led to exciting new developments that could redefine cancer diagnosis and treatment in the years to come. ML can increase access to treatment in developing countries which don't have enough specialist doctors that can treat certain diseases, it can improve the sensitivity of detection, add more value in treatment decisions, and it can help personalize treatment so that each patient gets the treatment that's best for them. In many cases they can even add to workflow efficiency in hospitals.