Drug–drug interactions (DDIs) can cause adverse reactions or reduce the efficiency of a drug. Using computers to predict DDIs is now critical in pharmacology, as this reduces risks, improves drug outcomes and lowers healthcare costs. Clinical trials are slow, expensive, and require a lot of effort. The use of artificial intelligence (AI), primarily in the form of machine learning (ML) and its subfield deep learning (DL), has made DDI prediction more accurate and efficient when handling large datasets from biological, chemical, and clinical domains. Many ML and DL approaches are bio-inspired, taking inspiration from natural systems, and are considered part of the broader class of biomimetic methods. This review provides a comprehensive overview of AI-based methods currently used for DDI prediction. These include classical ML algorithms, such as logistic regression (LR) and support vector machines (SVMs); advanced DL models, such as deep neural networks (DNNs) and long short-term memory networks (LSTMs); graph-based models, such as graph convolutional networks (GCNs) and graph attention networks (GATs); and ensemble techniques. The use of knowledge graphs and transformers to capture relations and meaningful data about drugs is also investigated. Additionally, emerging biomimetic approaches offer promising directions for the future in designing AI models that can emulate the complexity of pharmacological interactions. These upgrades include using genetic algorithms with LR and SVM, neuroevaluation (brain-inspired model optimization) to improve DNN and LSTM architectures, ant-colony-inspired path exploration with GCN and GAT, and immune-inspired attention mechanisms in transformer models. This manuscript reviews the typical types of data employed in DDI (pDDI) prediction studies and the evaluation methods employed, discussing the pros and cons of each. There are useful approaches outlined that reveal important points that require further research and suggest ways to improve the accuracy, usability, and understanding of DDI prediction models.

Recent studies have shown that voice analysis provides a feasible, non‐invasive diagnostic alternative for the early detection of neurodegenerative diseases (NDs), such as Parkinson's disease (PD) and Amyotrophic Lateral Sclerosis (ALS), using Artificial Intelligence (AI), primarily machine learning models (MLs) that can be integrated into complex clinical decision support systems (CDSSs). Although significant progress has been made, several computational challenges remain, including dataset variability, noisy and incomplete input data, and the need for robust generalization across linguistic and demographic differences. To overcome these issues, system and model reliability must be improved through extensive testing under realistic conditions and integration with clinical workflows. This comprehensive study reviews the potential of AI to analyze speech biomarkers for accurate early detection and diagnosis. Improvements over traditional methods have been achieved through the use of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Ensemble Methods (EMs), and diverse hybrid models. However, adoption of these models in real clinical practice is limited by challenges in standardization and effective real‐world applicability. Effective adoption of highly efficient AI‐based voice analysis in clinical practice requires standardized methods, collaboration among specialists from different disciplines, and evidence from real clinical trials. Physicians require comprehensive explanations of AI models to develop confidence in AI‐powered diagnostic tools. Explainable Artificial Intelligence (XAI) applications for neurodegenerative disease diagnosis remain underexplored in research, despite the need for healthcare practitioners to understand diagnostic predictions. Our study provides insights for researchers, software developers, and clinical practitioners, highlighting the need for future research on the development of robust, adaptive models that can accommodate data errors and remain fair and private across AI‐based diagnostics. Future research must build transparent AI models able to achieve high accuracy alongside interpretability standards for standard medical practice deployment. The review also systematically evaluates the quality of the data underlying the models that are reported, including the adequacy of the sample size, class balance, linguistic diversity, and recording conditions. This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Health Care Technologies > Computational Intelligence