TECH OUTLOOK 8NOVEMBER 2023IN MY OPINIONIf you are a technology enthusiast like me, you have probably read and heard that AI (artificial intelligence) is going to change our lives, our way of working, and the whole world. But how exactly is AI going to change our discipline which works with chemical and/or biological substances? Let us indulge our imagination a little and entertain the possibilities of how AI can change the field of bioanalysis. This will in turn give us some food for thought on how AI could change related fields.AI for bioanalysis is narrow AI Many people only think of AI as AGI (Artificial General Intelligence), or human-level intelligence. But AGI is actually the long-term goal, although in recent years it is gaining steam. Current mainstream AI is narrow AI that focuses on specific sub-problems where AI can produce verifiable commercial applications, e.g. smart search engines, self-driving cars, etc. AI for bioanalysis obviously falls into the narrow AI category. AI sub-fields that could potentially revolutionize bioanalysis include but are not limited to reasoning, problem-solving, planning and decision-making, natural language processing, perception, and advanced robotics.Bioanalysis extracts quantitative information about the substances of our interests (drugs, targets, biomarkers, etc.) out of biological systems. In other words, it works at the boundaries between real world space and data space. Thus, when talking about AI for bioanalysis, it will have as much to do with the intelligence in manipulating real world substances, i.e., smart experiments, as with the intelligence in processing the data acquired. Typical bioanalytical work streams fall into three main categories:· Methodology (method development, method validation, cross-validation)· Sample testing production (sample extraction, instrumental analysis, data processing)· Peripheral support activities (plan writing, report writing, sample logistics, QC/QA, etc.)The above data processing and peripheral support activities take data (symbols and rules) as input and output data in the form of documents, knowledge, etc. and thus essentially operate in the digital world. Operating in the digital world is native to AI programs. Overall, scientific document writing is mostly factual, follows certain pre-defined formats and doesn't need generative AI such as ChatGPT. Already there are software companies working on automating document writing. AI could "touch up" the documents with natural language processing prowess and make the report language flow better, while leaving the scientific data intact, which scientists insist upon. Human is still the ultimate quality controller of AI-produced scientific writings and AI QC/QA'ed data.AI enhancement for bioanalytical sample extractionA decade ago, there was already extensive research to automate the majority of small molecule bioanalytical sample preparation on commercial general purpose liquid handling robots using a combination of in-house developed software and scripting on the robots. The idea was to take user input about a sample extraction experiment and compute a complete liquid handling scheme for the experiment, then carry out the experiment using the pre-computed scheme. With the current advancement of AI, the following areas of that system could use some enhancement:1. Merge of bioanalytical-specific user interface with robot internal scripting.2. Integration of peripheral lab instruments, such as capper and decapper, plate sealer, vacuum, and centrifuge, both hardware and By Ming Li, Director of Bioanalytical and Biomarker Outsourcing Operations, Alexion PharmaceuticalsA GLIMPSE OF HOW AI CAN HELP REVOLUTIONIZE BIOANALYSISMing Li
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