Leveraging Artificial Intelligence for the Prevention and Control of Infectious Diseases - Sawhney, Shagun; Adhikary, Arindam; Alam, Inkashaf; Sharma, Kanisheka (Journal of Neonatal Surgery, 2025)
Infection diseases caused by pathogenic microorganisms, such as bacteria, viruses, fungi, and parasite, continue to be a significant global health concern. This problem is made worse by the lack of quick diagnostics and the rapid increase in antibiotic resistance. Through quantitative modelling, sequencing and the programmability of biology molecules like proteins, peptides and nucleic acids, advances in systems and synthetic biology have made it possible to develop new anti-infective treatments, vaccines, and diagnostic platforms. By enabling data-driven prediction, optimization, and analysis across various biology domains, these developments are being accelerated by powerful tool like machine learning and AI. Machine learning models have contributed to the discovery of new antimicrobial compounds, including graph-based predications, aptamers, and other alternative modalities. Additionally, machine learning techniques have guided the development of vaccines, predicated virulence and immunogenicity, enhanced our understanding of host-pathogen interactions, and enabled the automatic interpretation of data from mass spectrometry sequencing and microscopy. CRISPR- based sensors, toehold switches, and rapid susceptibility testing is example of AI-integrated diagnostics that have the potential to detect emerging infections more quickly, portably, and easily. Notwithstanding advancements, problems with data quality, model generalizability, bias reduction and clinical validation still exist. It is anticipated that as synthetic biology and next generation AI continue to be integrated, therapeutic discovery will progress, diagnostic precision will increase, and global capabilities for fighting infectious diseases will be strengthened