AI That Listens: Why Context Matters More Than Accuracy in Clinical AI

I still remember the morning vividly. I was a newly qualified doctor on a gastroenterology ward in the UK’s National Health Service, starting the day shift at around 7:30 a.m. The hospital didn’t yet have fully integrated electronic health records, but we used a digital system to record vital signs and calculate each patient’s Modified Early Warning Score (MEWS) – the colour-coded index that tells you, at a glance, who is sick and how urgently they need attention.

As I logged in, one new patient’s chart caught my eye: her score was flashing bright red. A systolic blood pressure under 75 mmHg. A heart rate in the 40s. In medicine, those numbers are a siren – the kind that makes your pulse quicken before you even think. I leapt from the desk and sprinted down the ward, already rehearsing resuscitation orders in my head.

When I reached the bed, I stopped short. Sitting upright in a chair beside it was a cheerful woman in her fifties, dressed, reading a book, sipping tea. She looked up, smiled, and said, “You saw my blood pressure, didn’t you?”

I stumbled through a quick assessment (airway, breathing, circulation), half-expecting the numbers to catch up with what I was seeing, or for the patient’s condition to catch up with the numbers. They didn’t. She was completely fine. Calmly, she explained that this was normal for her. In fact, she’d already spent a few unnecessary nights in intensive care because her readings kept alarming the system and the doctors who trusted it. “They all panic,” she said, “but I promise, this is just me.”

As I walked back to the desk, still a little breathless, it struck me how much variety we see in “normal”, and how data is not the full picture. The readings were perfectly accurate, yet entirely misleading. The numbers told a story, just not *the* story.

In 1931, the philosopher Alfred Korzybski coined the phrase “the map is not the territory.” It’s a simple reminder that a description, however precise, is not the thing itself. The chart in front of me that morning – the flashing score, the red digits – was the map. The woman at the bedside was the territory. Both mattered, but only together did they tell the truth.

This distinction lies at the heart of clinical practice. Much of medicine operates in the space between measurement and meaning. We rely on data, such as vital signs, laboratory values, imaging, risk scores, etc., but we interpret them through layers of context that rarely appear in a database. A high fever matters differently in a child who has just had vaccinations than in an elderly patient on chemotherapy. A creatinine of 250 is alarming for some, reassuring for others whose “normal” has long been higher. A patient’s tone when describing pain, the look of effort on their face, the quiet hesitation before answering, none of these are captured by a monitor or chart, yet they guide our judgment as surely as any lab result.

This is the daily balancing act of medicine: the discipline of precision, tempered by the art of context. The map gives us structure; the territory gives us truth.

The same lesson applies to artificial intelligence in healthcare. AI systems excel at mapping. They can transcribe a clinical conversation with extraordinary accuracy, flag subtle arrhythmias on an ECG, or detect tiny pulmonary nodules that human eyes might overlook. These capabilities are invaluable – accuracy underpins safety, trust, and reliability. But accuracy alone does not make a system clinically intelligent. Without a sense of why a signal matters for this patient, in this moment, within this web of circumstances, even the most precise model can fail to grasp the meaning of what it sees.

Many of the limits we observe in today’s AI systems (the plateauing of performance despite larger datasets and finer metrics) stem from this lack of contextual understanding. Models can mirror patterns, but not necessarily understand them. They know what the words are, but not what the words mean. They can describe the map with astonishing fidelity, yet still miss the landscape that gives those contours life.

In healthcare, that landscape is everything: physiology, history, habit, fear, intuition, and experience. To build AI that truly supports clinicians, we must find ways to encode not only the measurable but the meaningful; to design systems that learn the language of context as fluently as they learn the language of data. Because in the end, accuracy tells us where we are on the map; context reminds us where we stand in the world.

Accuracy: Essential, Measurable – Yet Not Enough

Accuracy lies at the heart of modern medicine. From the moment a nurse records a vital sign to when a radiologist interprets an image, precision matters: errors in data or interpretation can lead directly to patient harm. In clinical AI, accuracy remains the foundational requirement for trust and safety. If a model mis-transcribes a consultation, mis-classifies a lab result, or fails to detect an arrhythmia, it cannot credibly be used in practice.

In the context of healthcare AI, accuracy typically refers to how well a model’s output aligns with a defined “ground truth”, for example, the correct diagnosis, the correct transcription of speech, the correct detection of an image abnormality, or the correct prediction of an outcome. Standard metrics include sensitivity (true positive rate), specificity (true negative rate), positive predictive value (PPV), negative predictive value (NPV), and discrimination measures such as area under the receiver operating characteristic curve (AUROC)[1]. Benchmarking efforts have emerged to systematically capture these measures. For example, Stanford University’s MedAgentBench, which tests AI agents on simulated electronic health record tasks and found that even high-performing models achieved only 65-70% success in complex clinical scenarios [2].

Why accuracy matters for trust, safety & adoption

In medicine, a mis-transcribed note, a missed diagnosis, or an incorrect risk prediction can erode clinician trust, cause workflow disruption, and—at worst—lead to adverse outcomes. Accuracy signals reliability: if an AI system consistently gets the “what” right (e.g., the correct transcription, the correct detection), clinicians and organisations are more likely to adopt and integrate it into practice. For example, qualitative research into ambient AI scribe tools found that physicians cited concerns around accuracy and editing burden, even while recognising promise in the tools [3].

Thus, accuracy is foundational. Without it, nothing else matters.

Yet, despite impressive advances in narrow task accuracy, healthcare AI increasingly encounters what might be called a performance plateau. Gains in standard metrics (e.g., AUROC, sensitivity/specificity, Precision/Recall/F1) do not always translate into improved clinical impact. Reviews of AI in critical care, for instance, show that while diagnostic accuracy continues to improve, demonstrable outcome gains remain limited [4]. Similarly, the Peterson Health Technology Institute noted that while ambient AI scribes show promise, “evidence demonstrating time savings or improved clinical efficiency remains limited” [5].
In effect, a model may hit 95% accuracy in a controlled dataset, but when deployed in clinical practice, the incremental value of going from 95% to 97% may flatten if the model lacks a deeper understanding of why the data looks the way it does. It has mapped the contours, but not fully captured the landscape.

Why context becomes the differentiating factor

This is where context matters. In healthcare, context includes a patient’s baseline physiology, their comorbidities, patterns of change over time, clinician workflow, health-plan coverage rules, conversational nuance, and even social-behavioural dynamics. These features often live outside structured data tables or standardised note templates.
For example:

• A blood pressure of 150/90 is concerning in one adult, but in another with longstanding hypertension and organ adaptation, it may reflect their baseline.
• How pain is communicated is tied to cultural norms and experience, so that 2 patients who report their pain as 5/10 may be experiencing very different levels of discomfort.
• A vital sign trend (e.g., a slow drift in heart rate over days) may matter more than a single reading—and detecting that trend often requires access to longitudinal context, which isn’t always easily encoded.
  
  

Context is difficult to engineer and integrate for several reasons:

• Data fragmentation & heterogeneity: Patient context often lives in disparate systems – EHRs, prior admissions, narrative text, voice conversations, many of which are unstructured or poorly linked (if documented at all).
• Baseline variability: What constitutes “normal” for one patient may diverge significantly from textbook norms – AI must model individual baselines, not only population norms.
• Temporal and behavioural nuance: Patients and their physiology evolve; conversations include hesitations, tone shifts, interruptions – all of which are rarely captured in structured form.
• Workflow/roles complexity: Clinical decisions happen in high-pressure, multi-team environments; AI models trained on static datasets may not account for real-world operational workflows.
• Semantic/ontological depth: True contextual understanding requires models that interpret not only words or measurements but intent, causality, and significance, requiring richer representations such as ontologies or knowledge graphs, which remain difficult to operationalise at scale.
  
  

For the clinician: An AI system that is accurate for narrow tasks offers confidence in its reliability. But the clinician must still interpret the output in context of the patient. The most valuable AI will augment, not replace, that judgement, ideally by surfacing context or flagging where it lacks the requisite data or confidence.

For the patient: The promise of AI is better, faster, safer care. Yet if we over-emphasise accuracy metrics without also addressing context, we risk systems that look high performing but fail to capture what matters for that patient in that moment. Patients and providers both need transparency about what the system knows, and equally, what it doesn’t know.

How this applies at Suki

At Suki, we view this duality – accuracy and context – as central to the next phase of progress. Our ambient clinical intelligence platform is designed not just for transcription accuracy, but for capturing the meaning of encounters through deep EMR integration, structured data capture, and workflow awareness. By connecting voice data to the broader patient record, we aim to make documentation both precise and contextually relevant. As our products expand into areas such as revenue cycle automation, accuracy ensures reliability, but context ensures relevance and trust [6][7].

The Next Frontier: Context-Aware Health AI

Accuracy has carried health AI a long way. But the next generation of progress will come from systems that also understand context. Not simply what was said or recorded, but what it means within the broader story of care.

In healthcare, context isn’t a single data source. It lives in a patient’s prior visits, vital sign trends, lab results, and in the conversational nuance between clinician and patient. To deliver genuine understanding, AI must connect these fragments, linking what’s heard with what’s known. In this regard, integrated ambient tools offer a unique opportunity to bridge the gap between what is in the record and what is said in the doctor’s office.

Ambient clinical intelligence is a critical step towards more intelligent health systems. By listening to the natural rhythm of clinical conversations, ambient systems capture a layer of meaning that structured templates often miss. They reduce the administrative burden of documentation and preserve the narrative flow of a clinical encounter. But ambient AI alone is not enough. To truly become context-aware, these systems must draw from a wider ecosystem, one that integrates deeply with electronic medical records, reflects longitudinal patient data, and adapts to the realities of clinical workflow.

At Suki, this challenge is something we work on every day. Deep EMR integration gives us the foundation for understanding a patient’s history, but the next layer comes from how that information is structured and connected. That’s where our Clinical Knowledge Graph (CKG) comes in. A CKG provides a way to organise clinical data into relationships: linking symptoms to diagnoses, problems to medications, visits to prior history, and conversational cues to relevant clinical concepts. Instead of treating each utterance in a visit as an isolated datapoint, the CKG situates it within a patient’s broader clinical narrative.

This kind of structure directly addresses the limitations we see in accuracy-only systems. A model might transcribe that a patient “feels worse today,” but without context, it cannot tell whether this represents a meaningful change. When linked to the CKG, which encodes previous encounters, baseline physiology, chronic conditions, and recent clinical events, the same statement becomes interpretable. The system can understand not just what was said, but relative to what.

By grounding ambient audio in structured longitudinal knowledge, the CKG helps close the gap between measurement and meaning. It becomes possible to distinguish a true clinical change from a patient’s normal pattern, to flag relevant comorbidities when a symptom is mentioned, or to recognise when a conversational detail has billing, care-gap, or follow-up implications. Building this requires more than model performance: it depends on interoperability, semantic enrichment, workflow alignment, and clinically grounded data relationships; all prerequisites for moving ambient AI from transcription toward true contextual understanding.

Closing Thought

Every clinician learns, often quite early in their career, that accurate information is not the whole story; that data, however precise, can never fully capture the patient before you. The art of care lies in interpreting the space between measurement and meaning, between what is recorded and what is real. As AI becomes part of everyday clinical practice, our challenge is the same: to build systems that unite those perspectives: precision anchored in context, automation guided by understanding. When accuracy and awareness move in tandem, technology will not replace the human act of care, but reflect it more faithfully.

References

1. S. Shankar et al. Performance evaluation of AI models in clinical prediction. Digital Health, 2024.ScienceDirect
2. Stanford Institute for Human-Centered AI. MedAgentBench: Real-world benchmarks for healthcare AI agents, 2025.Stanford HAI
3. S. Keshav et al. Physician Perspectives on Ambient Clinical Documentation Tools. JAMA Network Open, 2025.JAMA Network
4. S. Banerjee et al. Artificial Intelligence in Critical Care: Enhancing Decision-Making and Patient Outcomes. Healthcare Bulletin, 2025.Healthcare Bulletin
5. Peterson Health Technology Institute. Adoption of AI in Healthcare Delivery Systems: Early Applications & Impacts. March 2025.PHTI
6. MobiHealthNews. Suki Enhances Ambient AI to Advance Clinical Coding. May 2024.MobiHealthNews
7. Suki AI. The Rise of Ambient AI: A Gamechanger for Primary Care and Physician Burnout. Suki Blog, 2024.Suki.ai