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An increasing number of medical devices incorporate artificial intelligence (AI) capabilities to support therapeutic and diagnostic applications.


The term artificial intelligence (AI) describes the capability of algorithms to take over tasks and decisions by mimicking human intelligence. Many experts believe that machine learning, a subset of artificial intelligence, will play a significant role in the medtech sector. “Machine learning” is the term used to describe algorithms capable of learning directly from a large volume of “training data”. The algorithm builds a model based on training data and applies the experience, it has gained from the training to make predictions and decisions on new, unknown data.






El papel de la inteligencia artificial y el aprendizaje automático en el sector de la salud ya era tema de debate mucho antes de la pandemia de coronavirus.


Why is IoT important?


Artificial intelligence in the medtech sector is at the beginning of a growth phase. However, expectations for this technology are already high, and consequently prospects for the digital future of the medical sector are auspicious. In the future, artificial intelligence may be able to support health professionals in critical tasks, controlling and automating complex processes. This will enable diagnosis, therapy and care to be optimally aligned to patients’ individual needs, thereby increasing treatment efficiency, which in turn will ensure an effective and affordable healthcare sector in the future.


In clinical applications, artificial intelligence is predominantly used for diagnostic purposes. Analysis of medical images is the area where the development of AI models is most advanced. Artificial intelligence is successfully used in radiology, oncology, ophthalmology, dermatology and other medical disciplines. The advantages of using artificial intelligence in medical applications include the speed of data analysis and the capability of identifying patterns invisible to the human eye.






Data Quality


Many deep learning models rely on a supervised learning approach, in which AI models are trained using labelled data. In cases involving labelled data, the bottleneck is not the number of data, but the rate and accuracy at which data are labeled. This renders labeling a critical process in model development. At the same time, data labelling is error-prone and frequently subjective, as it is mostly done by humans. Humans also tend to make mistakes in repetitive tasks (such as labelling thousands of images).

Labeling of large data volumes and selection of suitable identifiers is a time- and cost-intensive process. In many cases, only a very minor amount of the data will be processed manually. These data are used to train an AI system. Subsequently the AI system is instructed to label the remaining data itself—a process that is not always error-free, which in turn means that errors will be reproduced. Nevertheless, the performance of artificial intelligence combined with machine learning very much depends on data quality. This is where the accepted principle of “garbage in, garbage out” becomes evident. If a model is trained using data of inferior quality, the developer will also obtain a model of the same quality.



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