From documenting patient encounters to powering intake tools and digital assistants, conversational artificial intelligence (AI) promises to reduce physician burden and improve access. And major moves in the industry show just how quickly it’s being embraced.
The UK’s National Health Service (NHS) recently partnered with OpenAI to pilot tools like ChatGPT as a first line of engagement for patient symptom reporting. In parallel, the American Medical Association (AMA) is tracking a wave of state-level AI regulations in the U.S., focused on ensuring transparency, safety, and physician oversight as these tools become more deeply embedded in care delivery.
But growing adoption brings new pressure. Ambient AI tools must not only understand what’s being said by both patients and healthcare professionals at the point of care but also interpret it accurately in the clinical context and avoid generating the wrong output.
A recent Fortune article highlights what can happen when that context is missing. An AI-driven screening tool deployed in the UK falsely diagnosed patients due to hallucinated outputs — raising concerns not just about the model itself, but about the systems and safeguards (or lack thereof) surrounding its use.
Framing the challenge: A promising start
Ambient solutions are already delivering real benefits. A recent JAMA Network Open study found that ambient AI tools reduced physician documentation time by nearly 60%, with improved clinician satisfaction and less burnout.
But many of these tools are built on general-purpose large language models (LLMs) that are not designed to parse the nuances of medical terminology. Without clinical grounding, they may mishear, misinterpret, or miscode — not because they’re broken, but because they do not have the ability to reference the clinical knowledge required to get it right.
Recent research, such as the real-world evidence (RWE)-LLM framework, supports this reality: Generalist models can struggle in high-stakes, domain-specific settings without healthcare-specific data or validation.
Why clinical grounding strengthens ambient tools
Ambient vendors have much to gain by embedding real-world clinical semantics into their workflows to build smarter, safer tools that providers and patients can trust.
Grounding ambient AI in clinical context ensures that:
- Terminology is interpreted correctly across specialties, settings, and provider types
- Coding decisions reflect intent, not just surface-level language
- Outputs are explainable and traceable, supporting regulatory and clinical oversight
What grounding looks like in practice
True clinical grounding goes beyond fine-tuning an LLM. It means layering in structured clinical knowledge from the beginning, using a foundation built on:
- Mapped and normalized terminology across specialties and code sets
- Awareness of synonym variation and documentation preferences by provider type
- Editorial policies derived from real clinical guidelines and real-world usage
This helps ambient tools do more than “sound right.” It ensures they are right — delivering reliable, accurate, and explainable outputs that support both clinician and patient trust.
The path forward: Building on a strong foundation
The potential of Ambient AI for both patients and providers is invaluable. But as regulations evolve and expectations rise, vendors and implementers must ground these tools in clinical truth, not just linguistic probability.
That means pairing generative power with domain-specific intelligence. It means prioritizing interoperability, accuracy, and transparency. And it means embracing clinical grounding as not just an enhancement, but a core requirement for responsible AI in healthcare.
Supporting safer ambient AI with IMO Health
At IMO Health, we help AI vendors and health tech organizations build smarter, safer, and more clinically aligned tools. Our knowledge layer integrates decades of clinical terminology expertise with real-world usage patterns — helping ambient systems reduce hallucinations, improve coding accuracy, and deliver explainable results from day one.
