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The MLP network has applications including pattern recognition, optimization aids, process identification, and controls, are usually trained by supervised training procedures operating in a single direction only, and can be used as universal pattern classifiers. ANNs involve various types, including multilayer perceptron (MLP) networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), which utilize either supervised or unsupervised training procedures 9, 10. ANNs constitute a set of nodes, each receiving a separate input, ultimately converting them to output, either singly or multi-linked using algorithms to solve problems. These comprise a set of interconnected sophisticated computing elements involving ‘perceptons’ analogous to human biological neurons, mimicking the transmission of electrical impulses in the human brain. A subfield of the ML is deep learning (DL), which engages artificial neural networks (ANNs). ML uses algorithms that can recognize patterns within a set of data that has been further classified. According to the McKinsey Global Institute, the rapid advances in AI-guided automation will be likely to completely change the work culture of society 5, 6.ĪI involves several method domains, such as reasoning, knowledge representation, solution search, and, among them, a fundamental paradigm of machine learning (ML). Its applications are continuously being extended in the pharmaceutical field, as described in this review.
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AI utilizes systems and software that can interpret and learn from the input data to make independent decisions for accomplishing specific objectives. At the same time, it does not threaten to replace human physical presence 3, 4 completely. AI is a technology-based system involving various advanced tools and networks that can mimic human intelligence. This motivates the use of AI, because it can handle large volumes of data with enhanced automation. However, this digitalization comes with the challenge of acquiring, scrutinizing, and applying that knowledge to solve complex clinical problems. Over the past few years, there has been a drastic increase in data digitalization in the pharmaceutical sector.