Muneeb Ali, Chief Technology Officer, Eon
These days, AI is everywhere. It finishes our text messages and suggests our next Google search. Most of these tools are powered by Large Language Models (LLMs), which use vast amounts of data to predict what comes next.
A good guess is fine for a casual query. In a clinical setting, it's not. When AI is used to interpret radiology reports and identify incidental findings, precision isn't optional — it's critical. Clinical data is buried in complex narratives, and when AI gets it wrong, it creates a real burden for clinicians and risk for patients.
Early on, we used approaches from traditional Natural Language Processing (NLP) — a model that enables basic identification of clinical terms and findings within unstructured text — to extract findings from radiology reports. On paper, performance looked strong. In practice, it wasn’t enough.
False positives created unnecessary work. Missed context led to incorrect conclusions. And over time, clinicians began to lose trust in the system.
That experience led to a clear realization: clinical AI doesn’t fail because it lacks intelligence — it fails because it lacks precision, context, and consistency.
For an AI tool to be trusted in a clinical setting, it has to perform reliably under real-world conditions. That requires five non-negotiable capabilities:
At Eon, we didn’t want to build a better "guesser." We built something fundamentally different: a proprietary Computational Linguistics (CL) model designed specifically for how clinicians document and interpret findings.
Unlike generalized AI tools, our model is deterministic, not probabilistic. It doesn’t generate possibilities, it extracts exactly what is present in the report. The same input produces the same output every time. It doesn't wonder what might come next; it extracts exactly what is documented. Nothing more, nothing less.
It extracts findings and the full clinical context surrounding them, turning narrative reports into structured, meaningful data without introducing interpretation or guesswork.
It breaks down reports into sections so it doesn't confuse a patient’s past medical history with a current finding. It also understands medical shorthand and synonyms, ensuring that a "lung mass" and a "pulmonary nodule" are tracked correctly across time. For example, a traditional model might flag a “nodule” simply because the word appears in the report even if the sentence reads “no evidence of a nodule.”
Our model understands that distinction, ensuring only clinically relevant findings are surfaced.
And all these capabilities together mean it can yield better information from any radiology report without forcing radiologists to change how they dictate. The computational linguistic model operates at greater than 99% precision in incidental lung nodule findings, meaning fewer than one false negative or positive for every 100 radiology reports.
Because our model is built on clinical logic, not just language patterns, it achieves:
While we started with pulmonary nodules, we’ve expanded to multiple disease-specific models, each designed to interpret how findings are described in a specific clinical domain, capturing the full context and rich clinical detail required to accurately identify and track those findings over time.
Lung
Breast
Cardiovascular
Pancreas
Kidney
Thyroid
Liver
Eon’s CL model doesn't ask radiologists to change how they work or how they dictate. It’s designed to work the way the reports are written today, eliminating the need for structured templates or changes to clinical workflow.
It transforms unstructured clinical narratives into precise, structured data that can be trusted and acted on.
In healthcare, trust isn’t a feature. It’s a requirement.
