During this time, there was recognition by researchers and developers that AI systems in healthcare must be designed to accommodate the absence of perfect data. The 1980s and 1990s brought the microcomputer and new levels of network connectivity. However, MYCIN and other systems did not achieve routine use by practitioners. It provided the basis for a subsequent system, MYCIN, a knowledge-based consultation programme for infectious disease diagnosis, considered one of the most significant early uses of AI in medicine. Research in the 1960s and 1970s produced the first problem-solving program, or expert system, known as Dendral, designed for applications in organic chemistry. It is not surprising that, besides medical institutions, large IT companies such as IBM and Google have developed AI algorithms for healthcare. AI does this through machine-learning algorithms, which can recognise patterns in behaviour and create their own logic. What distinguishes AI technology from traditional technologies in healthcare is the ability to gain information, process it and give a well-defined output to the end user. A more elaborate definition characterises AI as a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation. Define AIĪI is a machine’s ability to make decisions and perform tasks that simulate human intelligence and behaviour, such as visual perception, speech recognition, decision-making and translation between languages. Self-learning algorithms record and analyse data from the worldwide web in real time, draw conclusions and make decisions. All of these IoT components are networked together, and can communicate and interact with each other. This has made the internet of things (IoT) possible – every device, room and wearable can now generate data, and upload it to the cloud. In addition to today’s advanced computer technology, there are new possibilities of producing micro-components, such as sensors or cameras, cheaply and in large quantities. The general practitioner, who used to be able to do almost everything, can only pass on to specialists. This was partly compensated by the specialisation of doctors. In addition, the new methods have created a flood of information that one person alone can no longer handle. This naturally limits the speed of the evaluation processes. However, all these new computeraided possibilities still require the processing, control and evaluation by doctors, pharmacists or other scientists. Thomas Dietrich, CEO of IVAM, discusses the role of AI in driving progress in the industry.īy collecting and analysing data, treatments and technologies can be developed much faster, knowledge about rare diseases can be more easily spread, and epidemics can be detected and fought faster. With the development of computer technology in the past century, treatment of diseases and development of technological solutions for diagnosis and therapy made a big step forward.
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