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BIOTECHNOLOGY


FASTER THAN HUMANLY POSSIBLE


Youngsahng Suh explains how AI is advancing the field of clinical diagnostics


T


he Covid-19 pandemic has underscored the need for speed in the development of tests, vaccines and therapeutics like never before.


Behind the scenes, many companies have achieved this with a sophisticated secret weapon: artificial intelligence (AI). Proprietary algorithms have driven everything from faster discovery, optimisation and validation through to more efficient manufacturing, quality control and supply chain management. Tis is true of vaccines and of clinical


AI tools are integral to disease diagnostics


diagnostics, which have played a critical role from the earliest days of the pandemic. How do AI and machine learning (ML) tools help us develop diagnostics quicker? Here, we explore AI’s pivotal role in designing complex assays and advancing them through the validation process. To do this, the platforms maximise today’s rich healthcare datasets and cloud-based computing platforms, reimaging how, what and when diseases can be detected and how individual patients will fare.


RAPID DEVELOPMENT AND OPTIMISATION


AI and machine learning systems are integral to the development of many modern diagnostics. Although SARS- CoV-2 testing has been front-and-centre during the pandemic, there are many valuable applications in other infectious diseases, cancers and chronic illnesses. Beyond speed, such applications can


drive greater sensitivity and specificity with sophisticated feedback loops. Tey also help companies adapt as testing needs change – for example, through the emergence of new variants. For example, using automated AI systems, the team at Seegene is able to develop a sensitive and specific molecular diagnostic test in a matter of weeks, instead of years. Tis is not the result of advances in a single step, but rather the introduction of specialised AI/ML at many points in the development journey. Seegene often begins with automated


referencing software that conducts data mining and pulls references relevant to a new test from all corners of the world. Natural language processing (NLP) technology is used to gather preliminary


34 www.scientistlive.com


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