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Pharmacist uses barcode reader to identify and sell a drug | Image Credit: ©alphaspirit -stock.adobe.com

Life sciences is a decade behind other industries in its optimization and strategic exploitation of data. This is perplexing, given how much companies profess their ambitions to exploit AI. An industry podcast brought together life sciences thought leaders to debate the subject. The panelists noted that really, by now, standardized data should be yielding greater intelligence, and powering pharma’s future, accelerated by AI. If only companies could find new momentum to finally sort out their underlying data. This article sets out some of the key points that arose from the panel.

Raman spectroscopy: Utilized in Raman experiments to analyze molecular  | Image Credit: ©luchschenF -stock.adobe.com

Counterfeit pharmaceuticals jeopardize public health worldwide, often leading to ineffective or dangerous treatments. Enter Raman spectroscopy: a rapid, non-destructive analytical technique that unveils the molecular secrets of medications. With its ability to identify counterfeit drugs through unique molecular fingerprints—even without unsealing their packaging—this technology is transforming pharmaceutical forensics and safeguarding global health.

Large-language models are excellent for general-use AI systems, but they don’t understand pharmaceutical companies’ proprietary documentation—the validated procedures and quality protocols that ensure drug safety. Smaller, domain-specific language models give companies more control and efficiency in their AI use.