AI techniques in Health
The most common AI systems that are currently emerging are machine learning, natural language processing, expert systems, computer vision and robots. Machine Learning is when computer programs are designed to learn from data and improve with experience. Unlike conventional programming, machine learning algorithms are not explicitly coded and can interpret situations, answer questions and predict outcomes of actions based on previous cases. These processes typically run independently in the background and incorporate existing data, but also “learn” from the processing that they are doing. There are numerous machine learning algorithms, but they can be divided broadly into two categories: supervised and unsupervised. In supervised learning, algorithms are trained with labelled data, i.e. for every example in the training data there is an input object and output object and the learning algorithm discovers the predictive rule. In unsupervised learning, the algorithm is required to find patterns in the training data without necessarily being provided with such labels. A leading form of machine learning termed Deep Learning is considered particularly promising.
Natural Language Processing (NLP) is a set of technologies for human-like-processing of any natural language, oral or written, and includes both the interpretation and production of text, speech, dialogue etc. NLP techniques include symbolic, statistical and connectionist approaches and have been applied to machine translation, speech recognition, cross-language information retrieval, human-computer interactions and so forth. Some of these technologies, and certainly the effects, we see in normal societal IT products such as through email scanning for advertisements or auto-entry text for searches.
”Expert systems” is another established field of AI, in which the aim is to design systems that carry out significant tasks at the level of a human expert. Expert systems do not yet demonstrate general-purpose intelligence, but they have demonstrated equal and sometimes better reasoning and decision making in narrow domains compared to humans, while conducting these tasks similar to how a human would do so. To achieve this function, an expert system can be provided with a computer representation of knowledge about a particular topic and apply this to give advice to human users. This concept was pioneered in medicine the 1980s by MYCIN, a system used to diagnose infections and INTERNIST an early diagnosis package. Recent knowledge based expert systems combine more versatile and more rigorous engineering methods. These applications typically take a long time to develop and tend to have a narrow domain of expertise, although they are rapidly expanding. Outside of healthcare, these types of systems are often used for many other functions, such as trading stocks.
Another area is Computer Vision systems, which capture images (still or moving) from a camera and transforms or extract meanings from them to support understanding and interpretation. . Replicating the power of human vision in a computer program is no easy task, but it attempts to do so by relying on a combination of mathematical methods, massive computing power to process real-world images and physical sensors. While great advances have been made with Computer Vision in such applications as face recognition, scene analysis, medical imaging and industrial inspection the ability to replicate the versatility of human visual processing remains elusive.
The final technique we cover here are Robots, which have been defined as “physical agents that perform tasks by manipulating the physical world” for which they need a combination of sensors (to perceive the environment) and effectors (to achieve physical effects in the environment). Many organisations have had increasing success in limited Robots which can be fixed or mobile. Mobile “autonomous” robots that use machine learning to extract sensor and motion models from data and which can make decisions on their own without relying on an operator are most relevant to this commentary, of which “self-driving” cars are well known examples.
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