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Corti

By Corti

IntermediatePlatform1.7K learners

Corti is an AI assistant for clinical decision support that analyzes conversations during medical calls and consultations in real time, flagging potential symptoms or risks and helping clinicians and emergency dispatchers follow…

#Corti#AITools#Platform#Intermediate#Abridge#Nabla#SukiAI#AmbienceHealthcare#ArtificialIntelligence#Glossary#SkillVeris

Definition

Corti is an AI assistant for clinical decision support that analyzes conversations during medical calls and consultations in real time, flagging potential symptoms or risks and helping clinicians and emergency dispatchers follow appropriate protocols. It originated with a focus on emergency medical dispatch before expanding into broader clinical settings and everyday patient consultations across many outpatient primary care practices worldwide.

Overview

Corti's origins lie in supporting emergency medical dispatch, where a dispatcher must quickly gather information from a caller and decide what guidance and resources to send, often under significant time pressure and with information that can be incomplete or emotionally charged. Corti's AI listens to these calls in real time and can flag indicators of specific medical emergencies, such as signs consistent with cardiac arrest, helping dispatchers follow established protocols more consistently than they might under stress alone. From that emergency-dispatch foundation, Corti expanded into broader clinical decision support, applying similar real-time conversation analysis to consultations and other clinical interactions, surfacing relevant information or protocol reminders to a clinician during the encounter itself rather than only after the fact through a note written once the visit has ended. A defining characteristic of Corti's use case is the real-time nature of its analysis: unlike documentation tools that generate a note after a visit concludes, Corti is designed to provide input while the conversation is still happening, which raises a higher bar for the speed and reliability of its underlying models compared to post-visit summarization, where there is more time to double-check output before it matters. Because the tool operates in emergency and clinical contexts where a missed or delayed flag can have serious consequences, Corti's design emphasizes supporting, not replacing, the judgment of trained dispatchers and clinicians, presenting suggestions and protocol reminders rather than making autonomous medical decisions that bypass a trained human's own assessment. In practice, it is used to support emergency dispatchers during live calls, flag potential cardiac or other emergency indicators in real time, assist clinicians with protocol reminders during a consultation, and improve consistency of emergency response decision-making across different dispatchers and shifts. Corti competes with a mix of specialized emergency-dispatch technology vendors and the broader category of AI clinical decision-support and documentation tools, with its real-time focus and dispatch-origin protocols setting it apart from ambient documentation products built primarily for note generation. Organizations evaluating Corti against a documentation-focused ambient AI vendor are generally solving a different problem: Corti's value is realized during the call itself, in the decisions a dispatcher or clinician makes in the moment, rather than in the quality of a record produced afterward for later reference. It is also worth noting that because Corti's suggestions arrive during a live, often high-stress interaction, interface design, how a flag is presented without distracting from the conversation itself, matters as much to its practical usefulness as the underlying detection accuracy.

Key Features

  • Analyzes clinical and emergency call conversations in real time
  • Flags potential symptoms or risk indicators as a conversation happens
  • Originated in supporting emergency medical dispatch protocols
  • Helps clinicians and dispatchers follow established response protocols
  • Provides input during the encounter rather than only after it concludes
  • Designed to support, not replace, trained clinical and dispatch judgment

Use Cases

Supporting emergency medical dispatchers during live emergency calls
Flagging potential cardiac or other emergency indicators in real time
Assisting clinicians with protocol reminders during a consultation
Improving consistency of emergency response decision-making
Surfacing a protocol reminder to a clinician during a live consultation
Reducing variance in how different dispatchers handle similar emergency calls

Alternatives

Abridge · AbridgeNabla · NablaSuki AI · Suki AIAmbience Healthcare · Ambience Healthcare

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