The Use of Artificial Intelligence (AI) in Biosciences

Artificial intelligence (AI) is the creation of intelligent systems that execute tasks requiring human intelligence to function. The application of AI in bioscience including clinical, agricultural, and pharmaceutical research has revolutionized over the years amplifying productivity and improving delivery time. This chapter explores AI-driven advancements in biological sciences, including genomics, machine learning-based antimicrobial resistance prediction, personalized medicine, AI-assisted drug discovery, precision agriculture, and plant disease detection, examining AI’s transformative potential, addressing challenges, and outlining further directions. By harnessing AI, researchers can accelerate discoveries, improve treatment outcomes, and enhance agricultural productivity, ultimately mitigating the impact of Pseudomonas-related diseases, while promoting healthier environments and advancing clinical, pharmaceutical, and agricultural research. Systems powered by AI-trained models using machine learning (ML) and deep learning (DL) such as deep colony are currently being used in clinics. A total of 5000 culture plates were processed by a large US laboratory involved in the diagnosis of urinary tract infections. Deep colony was able to match the experts’ evaluation and reports in 95.4% instances. Other AI tools, including PotentialNet used in aiding drug design, achieved a significantly high-performance for protein-ligand binding affinity. In the agricultural sector, several DL networks are used to process tabular, non-visual, and visual data such as convolutional neural networks (CNNs), autoencoders and long short-term memory (LSTMs), K-nearest neighbor (K-NN), support vector machine (SVM), K-means classifier, and artificial neural network (ANN) have been employed in identifying plant diseases.