The right artificial intelligence (AI)-enabled laboratory software can enhance productivity and efficiency but, with so many options, choosing the best solution isn’t necessarily straightforward. Keeping a few key points in mind, however, can detangle even complex and conflicting options.
AI is becoming a staple resource among scientists and offers a range of capabilities but with generative AI solutions that are in still in development (like Stable Diffusion and ChatGPT) making headlines, some caution is warranted. As a lab manager, it’s important for you to understand what AI can and cannot do well, along with what your lab needs today and in the next few years.
Here are three tips to help you select the best software for your intended uses:
1. Define your goals. Identify the goals of the software as well as the automation for your lab. “This includes considering the tasks that need to be automated, the workflow, and the expected outcomes,” says Neil Harper, founder of PDH-Pro, a continuing education provider. Speech recognition and conversational AI to streamline human-to-machine interactions, chatbots for data searches, image recognition for pattern matching, machine learning (which requires human training in your lab), and deep learning using neural networks (which access data from multiple sources and learn similarly to humans) are distinct options. Understanding your goals and the capabilities of various AI solutions will help you select the best applications for your uses.
2. Future-proof your investment. As your lab uses AI, its potential applications will likely increase. Therefore, choose an AI platform that can scale to handle additional AI-enabled data sources and workflows.
Generative AI is expanding into the life sciences with software that generates synthetic data in mere minutes, using only a few lines of code and models trained on genomics, medical, and other datasets.
Also ensure the AI software is compatible with your existing and planned computing infrastructure. Depending upon the organization, computer hardware refreshes occur approximately every three to five years, according to the Uptime Institute. In the past year or so, many universities and organizations have begun upgrading their computing infrastructure to support AI applications.1,2 Involve your IT department in decisions to ensure the AI software you are considering is compatible with the existing computing infrastructure in your facility and discuss any planned or likely IT upgrades to minimize the risk of future incompatibility.
3. Consider accuracy and ease of use. Generative AI is expanding into the life sciences with software that generates synthetic data in mere minutes, using only a few lines of code and models trained on genomics, medical, and other datasets. Generative AI is easy to use and can augment otherwise limited data sets—just be sure its results are accurate. When making your selection, weigh the ease of use and accuracy of output against the time-consuming historical process of training machine learning applications on your own data sets as the foundation for future analyses.
Selecting the right AI software is challenging, but it needn’t be daunting. Keeping these three tips in mind can help you identify the features you truly need today and help future-proof your software to achieve—and maintain—optimal performance.
1. Cortez, MG. “Universities Make Upgrades to Connectivity via Wave 2 Wifi,” EdTech. January 4, 2018. https://edtechmagazine.com/higher/article/2018/01/universities-make-upgrades-connectivity-wave-2-wi-fi.
2. Omaar, H. “Industry-University Partnerships to Create AI Universities.” Center for Data Innovation. July 19, 2022. https://www2.datainnovation.org/2022-ai-universities.pdf.