When someone dials an emergency number, you hear the emotions before the words are fully formed. A pause. A gasp. A scream. A strained sentence delivered too slowly. In those few milliseconds of silence, lives hang in the balance.
Corti is gaining attention for designing its AI for emergency calls, which entails listening in on ground conversations and detecting medical risks before or as soon as they occur. Corti collaborates with human dispatchers, informing them of any early warning signs to encourage faster emergency responses.
Corti, founded in 2016 by Andreas Cleve and Lars Maaloe, is a Danish health-tech company specializing in AI-powered, real-time patient consultation support.
Simply, Corti is an AI for emergency calls. It employs machine learning models trained on millions of recorded emergency calls throughout history to detect even the smallest hint of distress. It listens for irregular breathing, tone changes, word choices, and difficulty speaking.
Emergency calls are usually synonymous with chaos. Some callers may freeze up, unable to describe the situation or its gravity, and this hesitation can be fatal. Corti is specially trained to detect medical signals in noise or silence and interpret them by running them through emergency call software until a report is ready.
Corti is not an independent platform that decides and implements its own recommendations. It simply provides dispatchers with support, suggestions, and alerts to help them assess the risks and handle the situation quickly.
Corti is widely used in emergency medical services, public safety hotlines, and healthcare call centers. These organizations receive the majority of health-related emergency calls on a daily basis and may struggle to respond to all of them due to a lack of resources. Many emergency dispatchers and call handlers are not medically trained, but Corti helps them diagnose health conditions by listening to callers.
With Corti, emergency services can review call logs to ensure that problems are addressed and response protocols are improved.
Corti is also used in healthcare systems by nurses and medical centers. The AI aids in distinguishing callers who require immediate assistance from those who can be coached over the phone. Corti is now a key component of some cities’ national emergency response plans like Copenhagen, Seattle, Singapore amongst others.
As callers speak, the AI automatically begins to record the voice patterns, background sounds, and linguistic cues that can provide clues. The signals received are then run against hints that have been associated with specific medical emergencies.
When the AI figures out the risk, it instantly sends an on-screen prompt to the handler. These prompts may come with specific questions based on the analysis it has received. The call handler takes it up from there, better equipped to handle the situation at hand.
One of the most documented cases where Corti has been effective is cardiac arrest detection. Studies have shown that Corti can detect signs of cardiac arrest earlier than human operators alone, allowing dispatchers to provide life-saving instructions to the person in need before it is too late.
Even the slightest bit of delay can lead to fatal results or worse, death. Corti offers to be a “second ear,” listening attentively to save more lives. In the future of medicine, this may turn out to be one of the most powerful medical interventions ever.
Emergency dispatch is one of the most demanding jobs in healthcare. Call centers and their staff are frequently under pressure to gather information, assess medical risk, calm panicked callers, follow protocols, and make dispatch decisions in minutes. When incoming calls become overwhelming, they may miss out on important information and make rushed decisions.
Corti has been trained with datasets including both the spoken words and the acoustic properties of speech.
The AI attentively listens for specific indicators like:
Importantly, Corti does not rely on a single signal. It collects and interprets multiple inputs concurrently to gain a better understanding of the situation.
In cases like cardiac arrest, early detection is critical, and seconds count more than you might think. Studies show that cardiac arrest can be missed during calls, particularly when symptoms are minor or the caller does not realize how critical the situation is at the time. Corti continues to update its analysis as the call handler speaks with the caller. If the likelihood of a serious condition exceeds a predetermined threshold, the warning signal will activate within seconds.
Emergency services also carry out reviews on the call logs to ensure that the problems are addressed and that response protocols can be worked on, too.
In an emergency, voice alone can provide valuable information that standard questioning cannot.
Corti can use breathing sounds to determine whether the problem is difficulty breathing or a possible cardiac arrest. Speech patterns can also be used to diagnose a stroke. Vocal strain can mean respiratory failure.
Corti adds an extra layer of observation by analyzing both language and sound to detect signals that call dispatchers may unintentionally overlook due to the realities of multitasking in high-stakes environments.
AI systems in healthcare are expected to achieve high standards of accuracy and reliability. Corti is trained to assess real emergency calls faster and better than the human mind.
Corti’s alerts are based on probability; it offers likelihoods and suggestions rather than definitive diagnoses. The more helpful its suggestions are, the better it learns to perform over time.
Listening to emergency calls raises important ethical concerns about privacy, consent, and how the data collected from the calls is used and stored. Corti follows strict rules and controls regarding data privacy. It operates in regulated healthcare environments, and the calls received are only used to determine a caller’s emergency and nothing else.
In conclusion, Corti exists because medical emergencies can quickly become fatal. Leaving dispatchers to detect health warning signs is not always effective, as they may miss important signals. Corti provides constant monitoring, speed, and accuracy when it matters most. As voice-based health AI expands, particularly in clinical support, Corti AI is there to show how real-time analysis can improve emergency officers’ response time and quality.