Antibiotic resistance is a fast-growing public health crisis, causing more than a million global deaths annually and contributing to nearly 5 million more. These infections are more difficult and more expensive to treat than typical infections and are responsible for longer hospital stays, driving up costs for hospitals and patients alike.
Treatment mostly comes down to guesswork on the part of physicians. Ara Darzi, a surgeon and director of the Institute of Global Health Innovation at Imperial College London, says AI-powered diagnostics offer a better way.
“We’re standing, right now, in 2026, at the first genuine inflection point in this crisis,” Darzi said on April 16 at WIRED Health in London.
The overuse and misuse of antibiotics and a lack of new drug development have been fueling the rise of resistant microbes. When bacteria are exposed to levels of antibiotics that don’t immediately kill them, they develop defense mechanisms to survive. Unnecessary prescriptions allow bacteria to develop immunity, rendering life-saving medications ineffective. It means a dwindling list of treatment options for patients with serious infections.
The problem is set to get worse. A 2024 report in The Lancet predicted that drug-resistant infections could cause 40 million deaths by 2050.
Traditional diagnostics to determine an antibiotic-resistant infection usually take two to three days, as they require culturing bacteria from a sample. But for some infections, such as sepsis, that is time patients don’t have. For every hour of delayed treatment, the risk of death increases by between 4 to 9 percent. While waiting for test results, doctors must use their best judgment in choosing which antibiotics to use.
AI-based diagnostics could help inform those decisions. “AI-powered diagnostics are achieving accuracy above 99 percent without additional laboratory infrastructure,” Darzi said.
These types of rapid diagnostics are especially needed in rural and remote areas of the world, he added. The World Health Organization estimates that antibiotic resistance is highest in southeast Asia and the eastern Mediterranean, where one in three reported infections were resistant in 2023. In Africa, one in five infections was resistant.
AI could also help discover new drugs for resistant infections and predict the spread of resistant bacteria. The UK’s National Health Service is working with Google DeepMind to develop an AI system to combat antibiotic resistance. In one demonstration, the system identified previously unknown mechanisms of resistance in just 48 hours, cracking a mystery that had taken researchers at Imperial College London a decade to understand.
Paired with an automated laboratory, Darzi said it’s now possible to run hundreds of parallel experiments around the clock. Deep learning models can now screen billions of molecular structures in days, while generative AI is being used to design compounds that do not exist in nature.
Yet major pharmaceutical companies have dropped antibiotic development because of a broken economic model. New antibiotics would need to be reserved to prevent resistance, but pharma companies profit based on high-volume sales. There’s little incentive for companies to stay in the game.
Darzi argued that new payment models are needed in order to encourage the development of new antibiotics. In 2024, the UK began a pilot program for a Netflix-style payment model in which the government pays a fixed annual subscription fee to a pharmaceutical company for access to new antibiotics, not for the volume prescribed. Sweden is also experimenting with a partially delinked model.
“The question that will determine the shape of medicine for the next 100 years is not whether we have the tools to respond. We have the tools,” he said. “The question is whether we have the character to take seriously what we are seeing.”