scholarcy – AI tools in Research

Introduction

In the past 18 months AI technology has started to find its place within education and industry, but how have researchers assimilated some of the many tools out thereinto their workflow? In a recent interview with a senior scientist working in the field of Immunology. Their average day involves conducting lab work, performing analysis, and communicating the results to their team of researchers.

We asked for their perspective on the use of AI in a professional research environment, and here’s what they had to say:

For me personally, I do actually use machine learning technology, which is an application of AI. I use ML tools for image analysis after experiments have been performed. I’ll get slides of tissue sections and they’ll be stained with different markers to look at different immune cells. I then use a software that lets me click on a cell and say whether it’s positive or negative for a marker.The software uses my manual annotations to learn what I want to be called an immune cell, or what is background noise. I know some other research groups are using this tool called AlphaFold, which is used to predict the structure of proteins and that can be used to for drug discovery in future research.

AI tools for image analysis is an incredible application for machine learning. Semi-automating this aspect of the research journey not only saves time but allows academics and scientists to focus on the more important parts of the discovery process.In many ways, AI image analysis tools for scientists are like grammar checking tools for writers. Image analysis is still not a perfect science however -telling a computer what a cell is and isn’t, has proven to be a very difficult task, often requiring human input to check that the AI system has correctly classified the cells. Whilst there is room for improvement with these algorithms, they have certainly already changed the route academics take in the research environment.

So what I usually do is, I’d have around 30 images and you’d have maybe five images per experimental group. And I’ll get a representative image from each of those experimental groups and use that to train my machine learning tool. So there is some human interaction that you need to make sure it is accurate. You can’t just trust it blindly. I’d apply it to just a small region of interest and then go into my tissues again and apply it in a bigger region, and then actually check to see that it is correct, because sometimes it can make misclassifications of a cell, especially with the disease that I’m looking at. You sometimes have a whole cluster of cells composed of different markers which may result double positives that don’t exist in a biological system.

Definitely.

AI tools certainly seem to have streamlined research processes involving image analysis, but it still relies very heavily on subject matter expertise to ensure complete accuracy and trustworthiness.