Singapore General Hospital (SGH) is developing an artificial intelligence solution called 'Augmented Intelligent Infectious Diseases' (AI2D), aimed at determining the necessity of prescribing antibiotics, reducing antibiotic usage, and identifying the most appropriate antibiotic for each patient. The project is in collaboration with DXC Technology and currently covers pneumonia cases. The AI2D model is built on de-identified clinical data of approximately 8,000 SGH patients from 2019 to 2020, including X-rays, clinical symptoms, vital signs, and infection responses.

Recently, there has been a new trend in the scientific community — AI scientists. Simply put, this type of artificial intelligence can not only propose hypotheses and conduct experiments but also independently write research papers. While I have always believed that the best role of AI is as a tool for humans, rather than a replacement, this AI scientist seems to show considerable potential. Image source note: The image is generated by AI and is licensed from service provider Midjourney. We often hear about cases of scientists utilizing AI, such as the research conducted by MIT in 2019.

The issue of antibiotic resistance has led to a significant number of deaths globally, posing a major public health risk. Recent research utilizing artificial intelligence technology has successfully designed a new antibiotic, Protegrin-1.2, by screening thousands of variants. It effectively kills bacteria while significantly reducing damage to human cells. The study was led by Dr. Bryan Davis from the University of Texas, who used a system called SLAY to screen a vast number of antibiotic peptides, ultimately discovering this new selective antibiotic. Experiments showed that Protegrin-1.

Through deep learning, researchers at MIT have successfully discovered a new type of antibiotic that effectively combats drug-resistant bacteria, which cause more than 10,000 deaths annually. The application of artificial intelligence in the medical field is gaining increasing attention, and MIT's research provides an efficient and resource-effective framework for drug development. Researchers have shared their findings with the startup Phare Bio, planning further data analysis to search for compounds that can kill other bacteria. This study offers mechanical insights into the application of deep learning in antibiotic development and provides a new approach to addressing the problem of antibiotic resistance.