THANK YOU FOR SUBSCRIBING
Pharma Tech Outlook | Monday, December 16, 2024
Some of the most recent applications of AI and machine learning in pharmaceutical manufacturing include diagnosis and disease identification, clinical trials, and predictive forecasting.
FREMONT, CA: Machine Learning (ML), a form of Artificial Intelligence (AI), has propelled breakthroughs in the pharmaceutical industry over the previous decade, making for interesting science fiction. The advantages of using AI in the pharmaceutical industry were highlighted following the pandemic, which accelerated the discovery of effective medications and vaccines. AI assisted researchers with disease identification and diagnosis, drug manufacture, selecting groups for clinical trials, and predictive forecasting, among other things, to ensure a seamless rollout of vaccines.
Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.
Recent applications of AI and machine learning in pharmaceuticals are listed below:
Predictive forecasting using AI and ML: Predictive technologies can help forecast epidemic outbreaks and seasonal ailments around the world. This could assist manufacturers and healthcare providers in improving their logistics and supply chains. It will also arrange inventory at the appropriate times and quantities to address severe shortages.
Identification of diseases and their diagnosis: The current AI developments assist researchers in identifying small and large concerns ranging from cancer, eye degeneration and long-term COVID effects. AI has enabled researchers and healthcare professionals to make significant advancements in sectors such as behavioral modification, personalized medicine, and digital therapeutics. Furthermore, the important input provided by the AI and ML combination can lead to an improved understanding of medical ailments ranging from skin illnesses, gum difficulties, and inherited serious illnesses. A novel advancement in precision medicine uses AI to perform extensive computations for treating patients who have rare, uncommon responses to therapies.
AI in particle size analysis for drug manufacturing: AI and machine learning have proven valuable tools for early adopters, such as those employing them for image analysis in the pharmaceutical manufacturing industry. Critical processes in medication development have been further streamlined with the addition of software upgrades that use AI and ML as their foundation.
For example, EyePASS, the image analysis software for the Eyecon2 particle size analyzer, now uses machine learning, a type of AI, to gather data in real time on the size and shape of powders and bulk solids. These applications have aided in troubleshooting and improving final goods. Including automation in a human-machine collaboration approach has enabled pharmaceutical companies to reduce manufacturing costs and risk.
This machine learning methodology employs convolutional neural networks (CNN), a supervised machine learning method in which learned characteristics from input data are manually labeled for particle identification. To break it down further, EyePASS images of materials are first manually labeled, after which CNN iterates and improves its feature extraction. The network learns the features of interest. Increased sets of images are being prepared to broaden its capabilities beyond pharmaceuticals.
More in News