It is no secret that the advancements in technology are constantly transforming the world. With artificial intelligence (AI) and machine learning (ML) now on a roll, these superior technologies are revolutionizing the realm of data computing. From diagnostic assays and data analytics to drug discovery and development, the application of AI and ML has brought forth an analogous transformation in the biotech arena. These diagnostic assays are developed and updated only during a significant paradigm shift, inducing a significant time gap and missing out on countless opportunities to refine the results of previous diagnoses. Machine learning coupled with AI bridges this time gap by leveraging advanced data computations to run accurate digitized diagnostic tests, allowing the algorithm to determine and embed the required features into the final result.
A lab assistant had to undergo a tedious process of collecting and analyzing the diagnostic test results, and those days are no more. With AI platforms now in the bag, repetitive tasks such as designing constructs for gene editing and analyzing test data are completely automated and made efficient. The AI platform proffers the ease of use and proves valuable in expediting the overall process, from planning and designing an experiment to conducting the experiment and analyzing the results, enhancing the efficacy of the biotech workflow. Scientists and researchers can utilize industry-agnostic open-source AI-powered analytics platforms to access a spectrum of statistical analysis models, and alleviate the load of diagnostics data analysis from the lab attendants. However, the massive amounts of data circulating within a biotech ecosystem often form bottlenecks as data is being generated faster than it can be used.
The combination of AI and ML is just what the healthcare industry needs in order to eradicate the data bottlenecks. Biotech organization can exploit the ability of artificial intelligence and machine learning to regulate the flow of data into the ecosystem, keeping the infrastructure organized and mitigating any bottlenecks previously created. Additionally, the capability of AI’s computer vision to analyze drug compounds and cell images with superfine quality, eliminating the need to painstakingly peer into the microscope and screen for areas of interest, might just replace microscopes in the near future. This computer vision can be further enhanced with the addition of machine learning to pinpoint small molecules that could be the key to discovering therapeutic benefits. To summarize it all, application of artificial intelligence and machine learning is spreading across the healthcare industry and are here to stay, writing the future of biotech as we know it.