ABSTRACT
The pharmaceutical industry is poised for significant transformation through Artificial Intelligence (AI). Using automated roadmaps and catalogues for a swift investigation of huge data and artificial neural networks (ANNs) for developing new avenues for treatment, disease progression forecasts, and evaluating pharmacological profiles of drug candidates can significantly improve treatment outcomes. Artificial intelligence has demonstrated significant potential in electrospraying and electrospinning applications. AI optimizes electrospinning and electrospraying parameters, and analyzing extensive experimental data, AI identifies intricate patterns and relationships, enhancing efficiency, quality, and control in nanofiber and nanoparticle production. AI aids in material design by predicting characteristics based on specific needs. It also contributes to closed-loop systems, ensuring real-time monitoring and adjustment for precise fabrication. To predict the diameter and morphology of electrospun fiber, an artificial neural network can be used. An AI-powered tool is utilized to predict and optimize input factors by employing ANNs and surface response methodology (RSM) for polymeric fibers. By learning from interactions and adapting to the environment, AI transforms nanofiber and nanoparticle production, benefiting industries such as healthcare, electronics, and energy. Additionally, AI algorithms analyze images to detect defects in produced materials.
