Data science and algorithm-based information technology have now become impacting almost every aspect of our daily lives. But how do data science, AI, and machine learning contribute to one of the most urgent challenges facing society today: antibacterial resistance (AMR)? This collection explores data-driven approaches to AMR, with new approaches that are particularly interesting, with coordinated individual behavior, governmental changes in regulations, and new ways of working within the National Health Organization.
AI thrives when combined with cutting-edge technologies such as robotics, the Internet of Things, wearables, advanced communication systems, and ubiquitous, low-cost video imaging tools. These developments have sparked intense debate and legal scrutiny regarding the issue of copyright infringement, particularly from AI models trained in works of art.
So, what are the similarities in the context of antibiotics? Are existing datasets rich enough, standardized and openly available to support effective applications of data science? Can clinical microbiologists trust algorithm predictions given current medical and legal frameworks? And will the field of microbiology offer enough attractive challenges to attract quantitative researchers, especially when highly advantageous opportunities exist elsewhere?
Looking forward to optimism, you can draw inspiration from Alphafold, the most important biological breakthrough of AI to date. This powerful tool fused physical modeling with AI and AI to accurately predict protein structures from gene sequences. Will similar techniques help us discover new antibiotic molecules? In oncology, AI-assisted imaging rapidly transformed cancer diagnosis. What are similar tools to support decision-making in infection control? Will the algorithm help to optimize treatment for stubborn infections in intensive care, or can it guide the selection or engineering of bacteria targeting resistant pathogens?
The articles in this collection aim to provide an accessible introduction to these ideas before delving into quantitative methods that can be applied to antibiotic challenges. Data science and AI rely heavily on mathematical and statistical languages developed over the centuries, which can appear opaque or inaccessible to people outside the field. Conversely, infection experts operate within complex clinical and regulatory constraints that are unfamiliar with data scientists. Filling such disciplinary gaps is essential for meaningful progress to occur.
Antibiotics are uniquely multi-scale. These small molecules act on targets measured by their presence, but the evolutionary mechanisms governing resistance have unfolded for decades and spanned the globe. Microbial communities share genetic elements such as plasmids across continents, forming almost nonhuman collective intelligence. It seems appropriate that we should look to artificial intelligence in an effort to outperform microbial adaptation.
The collection hopes to inspire new interdisciplinary collaborations and shed light on how the rapid advances seen in the field of AI will address the critical threat posed by antibacterial resistance resistance.
