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Introducing FactsR™ by Corti -- the First Clinical Reasoning Layer for Ambient AI in Healthcare

Jun 11, 2025 · Source: cision
Introducing FactsR™ by Corti -- the First Clinical Reasoning Layer for Ambient AI in Healthcare

Real-time, recursive, fact-first architecture reduces AI-generated "note bloat" by 65 percent and minimizes post-visit edits - transforming passive documentation into active clinical intelligence.

COPENHAGEN, Denmark, June 11, 2025 -- Corti, the leading infrastructure layer for building clinical-grade AI in healthcare, today announced the launch of FactsR™, a breakthrough real-time agentic reasoning system for clinical consultations. Designed with ambient documentation in mind, FactsR™ reduces general purpose AI driven "note bloat" by 65 percent, keeping records precise, relevant, and tightly aligned with the actual clinical conversation. By minimizing post-visit edits and transforming passive transcripts into active clinical intelligence, FactsR™ sets a new benchmark for ambient AI in healthcare - while unlocking the path to real-time decision support at the point of care.

Corti_new

Unlike traditional LLM pipelines retrofitted for healthcare, FactsR is powered by Corti's recursive fact-first reasoning loop - a purpose-built engine designed to surface, validate, and structure clinical knowledge in real time as conversations unfold. Delivered as a modular API, it enables developers to embed clinical-grade intelligence directly into their healthcare applications - creating safer, leaner, and more trusted AI experiences at the point of care.

Why a recursive approach matters

Traditional ambient solutions pipe raw transcripts through generic models after the consultation has ended, producing verbose, error‑prone summaries that clinicians spend up to three hours a week correcting.

FactsR reflects a foundational shift in AI system design - from passive summarization to active reasoning. It allows developers to build AI that offers more concise, accurate results that clinicians can interact with live, in consultations and beyond. The process unfolds in four key stages:

  1. Listen and Extract in Real Time

    As the consultation unfolds, FactsR continuously identifies and surfaces structured clinical "facts" - such as symptoms, vitals, medications, and social history - while the conversation unfolds, live.
  2. Vet and Refine with Specialized AI

    Each fact is automatically reviewed and improved through an AI-driven feedback loop. If something is unclear, the system refines it until it is accurate, consistent, and ready to use - no guesswork, no clutter.
  3. Clinician-in-the-Loop

    Clinicians can quickly review, accept, or adjust facts as they go. Early adopters report far fewer post-visit edits and rarely need to add missing information after the consultation. This design keeps clinicians in control, ensuring that AI augments rather than replaces clinical judgment.
  4. Generate EHR-Ready Notes

    Once the facts are finalized, the system assembles a clean, concise summary - free from long, verbose summaries or irrelevant content.

This means:

  1. Less Screen Time

    Early trials show that users spend minutes, not hours, on corrections
  2. Better Patient Focus 

    Real‑time reasoning means decisions stay in the consultation, not in hindsight.
  3. Audit‑Ready Transparency 

    Every fact carries a timestamp, confidence score, and link back to the conversation.
  4. Healthcare AI that Delivers

    Reduces general purpose AI driven "note bloat" by 65 percent.

The innovation behind FactsR has been published together with evaluation results on the public benchmark Primock57 dataset. The evaluation shows that FactsR increases clinical completeness by 13 percent - capturing significantly more of the relevant medical information compared to traditional ambient scribes - while reducing note bloat by over 65 percent with a clinician-in-the-loop.

"Corti's system already does an excellent job capturing clinical details accurately, even in natural conversation," said a beta user from an IT team in a regional Danish hospital. "But when testing this new innovation, what really stood out was the shift toward structured, recursive fact extraction. It goes beyond basic transcription to surface the right clinical facts in real time - exactly what busy clinicians need to stay focused and cut down on documentation overload."

"FactsR exemplifies Corti's core philosophy: healthcare AI must be purpose-built, real-time, and accountable," said Lars Maaløe, CTO and co-founder of Corti.ai. "By breaking conversations into structured clinical facts and validating each one through recursive reasoning, FactsR elevates ambient documentation into a foundation for real-time clinical intelligence. When AI can listen, understand, and reason with medical context, it becomes more than a scribe - it becomes a trusted collaborator. With a developer-friendly API, we're enabling any healthtech company to embed this capability directly into their applications - safely, scalably, and in minutes."

Availability

FactsR is offered today through a consumption‑based API with enterprise‑grade HIPAA and GDPR compliance. Developers can start for free at docs.corti.ai and access SDKs for safe and effective app building in as little as 30 minutes. A self‑hosted option for on‑prem or sovereign‑cloud deployments enters limited preview this summer.

About Corti.ai

Corti.ai is a research and development company building state‑of‑the‑art AI foundation models for healthcare. The company's mission is to eliminate administrative hurdles and bring expert‑level reasoning to every corner of the globe, driving down costs and improving quality of care. Corti's models integrate seamlessly into any healthcare application through SDKs and APIs, enabling vendors, providers and payers to leverage safe, cutting‑edge AI across an extensive range of clinical use cases.

Contact: press@corti.ai 

Logo - https://mma.prnewswire.com/media/2502362/Corti_Logo.jpg

Cision View original content:https://www.prnewswire.co.uk/news-releases/introducing-factsr-by-corti--the-first-clinical-reasoning-layer-for-ambient-ai-in-healthcare-302478660.html

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