At the crossroads between statistics and artificial intelligence: statistical learning in laboratory medicine

While conventional statistics is already a closed book for many medical professionals, they often convey a sense of awe or even anxiety when terms such as machine learning (ML) or artificial intelligence (AI) come into play. The idea that a machine can learn something on its own, or can even use mysterious computer structures such as “artificial neurons” to behave intelligently in some way often gives people who are not very familiar with statistics and computer science a feeling of powerlessness.

by Georg Hoffmann and Frank Klawonn

It is therefore the aim of this special issue of J Lab Med to dissolve the mysticism associated with these terms and provide fact-based knowledge about the fundamentals of data science at the crossroads between conventional statistics and machine learning (Figure 1). While there is considerable overlap between the three fields depicted in the diagram, each has its unique focus and methods. Statistical learning serves as a bridge between statistics as a centuries-old branch of mathematics and machine learning as an upcoming sub-discipline of artificial intelligence.[…]

In this special issue of J Lab Med, our focus is on practical applications of algorithms and tools in laboratory medicine, which should enable readers to analyze their own data and thus gain their own experience. Those who are already familiar with the programming language R (https://r-project.org) will benefit most from this special issue, as the worldwide R community provides countless ready-to-use and free of charge libraries for statistical data analysis and machine learning. But even readers without programming experience can benefit from the practical examples and illustrative figures. And some of them may hopefully be motivated to take an R course in order to use such programs themselves.

Some of the topics were already discussed in the first special issue of the “Applied Biostatistics in Laboratory Medicine” series, which appeared a year ago 1], [2], [3. Due to the current AI hype triggered by the widespread use of ChatGPT and other large language models (LLM), we as editors have decided to dedicate an entire issue now to this crossover area between statistics and artificial intelligence with a focus on methods that are based on statistical models for laboratory results.

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Original Paper:

At the crossroads between statistics and artificial intelligence: statistical learning in laboratory medicine (degruyter.com)