FEATURE
AI could also help solve puzzles , such as : Why does my bacteria look like this ? Why did my sequencing not work ? The computer would immediately provide answers , saving hours of analysis — but also provide important answers to questions experts might not be able to easily figure out alone .
“ One of my biggest roles in the laboratory is acting as that interpreter between what the genomic data is showing us and how do we interpret it and make it actionable ,” Florek said . “ I ’ m always looking for new ways to interrogate and make that data more useful . If generative AI is a potential avenue for us to use to make that data more accessible … those are areas we want to explore more of .”
However , we ’ re not quite there yet . AI needs to continue to advance , and there are plenty of concerns that must be worked out first — around accuracy , privacy , ethics , data protection and more .
The Potential of AI
AI and “ machine learning ” are often used interchangeably , but they are not synonymous . AI is actually an umbrella term with many different areas and types . However , most AI in society today is based on machine learning , which allows a computer to analyze data to do a task without being explicitly programmed , according to the Office of Public Health Data , Surveillance and Technology ( OPHDST ) at the US Centers for Disease Control and Prevention ( CDC ). Machine learning can find patterns in data and / or predict an output based on its training .
Tools such as ChatGPT are a specific type of AI called “ generative AI ” ( GenAI ), which uses a type of machine learning called a large language model ( LLM ). These LLMs can recognize and generate text , images , audio and video . LLMs are trained on large data sets . Right now , this means that ChatGPT and similar tools are not answering a question correctly as much as they are predicting what the response to a prompt ( or question ) should be . GenAI imitates human language , but its answers are not always accurate .
Today ’ s GenAI has often been described as an intern who really wants to please you and give you answers — but their work products must be supervised and fact-checked .
“ We ’ re seeing a lot of the early adopters now use generative AI , but you have to realize it ’ s not intelligence — we may call it artificial intelligence , but it ’ s just math and code ,” said Jorge Calzada , MBA , head of machine learning and artificial intelligence and director of OPHDST ’ s Platforms Division .
While Florek isn ’ t in conversation with her data quite yet , her laboratory is using free open source machine learning models — often developed by academics — to help interpret data results . Florek works primarily in the infectious disease genomics arena . Her team has used machine learning in conjunction with their data analysis . “ We ’ re often using different models to help interpret and understand the genomic data to help explain or understand why we ’ re seeing something — whether it ’ s a new resistance mechanism or a new virus variant ,” she said .
Florek and her co-workers used machine learning during the COVID-19 pandemic to identify and classify emerging variants . “ That was incredibly powerful and a really excellent use of how we can apply some of these machine learning models to help classify this evolution pattern as it ’ s happening in real time ,” she said .
As the models become more sophisticated , Florek expects to move from using the software to support her analysis to being in conversation with the models , which would allow for more in-depth investigation and faster answers . Currently , working with large data sets can be difficult and require computer programming skills .
Kelly Oakeson , PhD , a bioinformatician at the Utah Public Health Laboratory , is excited about the prospect of using AI to determine things like how resistant an organism is to a specific antibiotic , which could lead to better and faster disease treatment . In that example , a laboratory would build up a training set of bacteria that are resistant to certain antibiotics . The team would conduct whole genome sequencing of those organisms to determine which genes are likely responsible for resistance to a specific antibiotic . Do that for 1,000 or so samples and you have a data set to use to train your internal machine learning algorithm , Oakeson explained .
Once you have the data set , “ you could start predicting the level of what might be resistant to what antibiotic , just based on
18 LAB MATTERS Fall 2024
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