AI is one thing above all: fast. Large amounts of data can be processed quickly and precisely, which significantly shortens diagnosis times and standardizes results, improving the reliability of diagnostics. For example, algorithms based on machine learning analyze high-resolution images of blood samples to detect differences between leukocyte types. These subtle differences are often crucial for the early detection of diseases. (Read more here)
At the same time, AI is also revolutionizing the prediction of laboratory results in clinical chemistry. By analyzing historical data, AI systems identify patterns that make it possible to predict future test results. This capability not only improves diagnostic accuracy, but also helps to avoid unnecessary tests and increases the efficiency of the entire diagnostic process. Especially in emergency situations, the fast and precise analysis capability of AI-based systems can be lifesaving. (Read more here)
In the pharmaceutical industry, the use of AI speeds up the process of screening and optimizing chemical compounds. Machine learning enables researchers to efficiently identify and optimize potentially effective substances from extensive chemical databases. This significantly shortens development times for new drugs and increases the likelihood that these compounds will be successful. The more accurate prediction of their efficacy and safety through AI thus has a profound impact on drug development. (Read more here)