Introduction
The study by Shin et al. (2023) provides valuable insights into lipid membrane remodeling by the micellar aggregation of long-chain unsaturated fatty acids, offering sustainable antimicrobial strategies. However, challenges remain in automating data collection, optimizing experimental tracking, and enhancing the predictive power of these studies. By integrating artificial intelligence (AI) and machine learning (ML), the research process can be significantly improved in terms of efficacy, efficiency, and productivity. Institutionalizing AI/ML into research workflows can enable real-time insights, adaptive experimentation, and data-driven decision-making.