The next major shift in mastitis management will not come from faster treatment or better cure rates but from detecting mastitis risk earlier — before symptoms become clinically apparent, before somatic cell counts rise and before irreversible damage to the mammary tissue occurs. This is encompassed by a shift from confirmation to prediction and from reaction to prevention.
Mastitis Detection is Shifting from Reactive to Predictive
When asked what his ideal mastitis detection situation would be, Dr. Justin Hess of Clinton Veterinary Services was quick to bring up prevention first. While it would be nice to have an automated system flagging mastitic cows at infection onset, he believes the real future is stopping it before it begins.
“Cure is a lot harder than prevention. It’s always easier to keep a cow from getting mastitis than to fix it later,” Hess says. “[Even if] a system is better at detecting mastitis, you’re always going to be behind the 8-ball in the first place, where the goal is to prevent it altogether.”
Traditional mastitis detection has been largely retrospective. Abnormal milk, elevated conductivity, increased somatic cell count or visible inflammation signal disease is already established. The emerging goal of mastitis detection systems is to identify subtle deviations — such as minute changes in rest time, rumination or quarter-level yields — early enough to intervene before disease fully develops.
Can Technology Identify the Bacteria Causing Mastitis?
Alongside earlier detection, there is growing interest in providing more information about what type of mastitis may be developing. Current animal health detection systems are strong at identifying abnormality, but weak at characterization. Looking at the future of automatic health monitoring systems, Dr. Alon Arazi, chief veterinarian at Afimilk, sees two areas of opportunity.
“I think the two main things that will probably change in the future are, one, we are lagging in the ability to detect subclinical mastitis. The other thing is to not just diagnose mastitis, but also to give some information about the cause,” Arazi says.
One of the things he is hoping they will soon be able to do is determine the type of bacteria causing mastitis.
“I believe that soon we will be able to give some information on if it’s Gram-positive or Gram-negative bacteria and then help the farmer make a decision about treatment,” Arazi says.
With this information, more targeted antibiotics could be chosen without having to wait for milk culture results. This supports antimicrobial stewardship, allowing farmers to potentially avoid unnecessary antibiotic use in cases that may self-resolve or require a different course of action.
The Power of the Data-Driven Team
The preventive impact of these systems extends beyond individual cows; it changes how the farm staff and consultants interact.
“The system is working on the cow level as well as the group level and the herd level. We try to look at all aspects of the farm,” Arazi says.
When looking through a broader scope, early deviations may indicate upstream management issues — such as bedding consistency or parlor hygiene — that elevate mastitis risk across multiple animals simultaneously. In that sense, future detection systems are as much about identifying system-level vulnerability as they are about flagging individual cows.
This is where the team-based aspect becomes critical. Once an operation has established the characteristic norms of their herd, the data becomes a shared language between the producer, the parlor staff and the veterinarian.
“Once you have the data, you can analyze it looking backward. Not just what is happening now, but what was the situation in the past? How did things progress? It can help you understand where you should put your money,” Arazi says.
As mastitis detection becomes more predictive and informative, the role of veterinarians evolves rather than diminishes. Interpretation, prioritization and integration remain essential. Technology may identify risk, but the team — the farmer and the vet — determines the response by balancing biology, economics, welfare and practicality. The future of mastitis detection is not automation replacing expertise, but better information supporting a unified team in earlier, smarter intervention.
Ultimately, success will be measured not by how quickly mastitis is treated, but by how often it is prevented from occurring at all.
Having spent their careers at the intersection of veterinary medicine and dairy technology, Hess and Arazi share a common passion for evolving how we look at herd health. On the first episode of “The Bovine Vet Podcast”, they join host Andrea Bedford to discuss why mastitis is much more than a simple infection.
