State-of-the-art review of neural network applications in pharmaceutical manufacturing: current state and future directions - Gholipour, Elnaz; Bastas, Ali (Springer, 2024)
Neural network applications, as an emerging machine learning technology, are increasingly being integrated into pharmaceutical manufacturing technologies, offering significant improvement opportunities for performance, efficiency and sustainability. This review paper utilizes a systematic methodology to establish key literature trends and themes. The state-of-the-art body of knowledge in this hot research area is analyzed in descriptive (e.g. neural network technologies studied, sustainability indicators considered, manufacturing process addressed) and thematic synthesis components. Process analysis and improvement, quality control and additive manufacturing were identified as the three focal research themes, and research lines within these themes were further studied and discussed. To guide future research, potential paths and research questions are proposed against the gaps identified. The originality of this work lies in its methodology (adoption of a systematic review approach, highly limited in the current literature), its inclusion of sustainability (as an imperative concept for manufacturing technology research) and its specific focus on neural network applications in the context of pharmaceutical manufacturing technologies (a perspective, either has been missing or addressed too widely by extant contributions).
Keywords: Machine learning; Neural networks; Pharmaceutical manufacturing; Sustainable manufacturing; Deep learning; Optimization