Bridging Engineering and Clinical Language with AI

Imagine a room filled with two groups of people: biomedical engineers and clinicians. The engineers are deep in the world of Fourier transforms, signal processing, and coding algorithms to make sense of biomedical data. On the other side, the clinicians are focused on patients, looking for practical insights into conditions like epilepsy and wanting tools that can make their lives, and their patients' lives, better. Despite working toward the same goal, these two groups often speak different languages—one based on technology and computation, the other on symptoms, patient history, and medical treatment. This communication gap can lead to misunderstandings, slower progress, and even tools that miss the mark for real-world healthcare needs.

That's where AI, particularly ChatGPT, comes in. AI language models can act as translators between these worlds, ensuring that engineers and clinicians are on the same page. Let’s look at how this works through a practical example.

Translating Engineering Speak for Clinicians

Suppose you’re developing a web application for monitoring epilepsy using EEG signals. The engineering side of things involves complex concepts like Fourier transforms, wavelet transformations, and artifact rejection. Here’s how an engineer might explain it:

Engineering Explanation:
"Using Fourier transforms, we’ll decompose the EEG signal into its frequency components, filtering out artifacts like eye blinks or muscle noise. This will allow us to isolate high-amplitude spikes within the 3-30 Hz range, which correlate with seizure activity. Afterward, we’ll apply a wavelet transformation to further refine the feature set and enhance seizure detection sensitivity."

To a clinician, this explanation is likely confusing, or at the very least, unhelpful. They don’t need to know the mathematical details of signal decomposition. They need to know what the technology does and how it’ll help their patients. Here’s where ChatGPT steps in, rephrasing it in a way that’s more useful to the clinician:

Clinician-Friendly Translation by ChatGPT:
“We’re using methods to break down the brain’s electrical signals into patterns we can track over time, filtering out normal activities like eye blinks or muscle movements. This helps us focus on specific brainwave patterns that are known to occur during seizures. The tool will detect when these patterns arise, alerting us to seizure activity with greater accuracy.”

In this translation, the technicalities are simplified. Instead of focusing on Fourier transforms, the explanation emphasizes how the system helps track and detect seizure patterns, highlighting the benefits and avoiding unnecessary technical jargon.

Turning Clinical Needs into Engineering Goals

Now, let’s flip the scenario. Imagine the clinician explains their needs:

Clinical Explanation:
“For epilepsy monitoring, we need real-time detection of seizure onset and accurate differentiation of seizure types. Clinicians typically monitor for sharp waves and spike-and-wave discharges, which signal various seizure types. A key requirement is minimizing false alarms to avoid unnecessary interventions.”

This is a clear clinical goal, but it can be a bit abstract from an engineering perspective. Engineers need to translate these needs into actionable parameters they can work on. ChatGPT helps by turning this clinical need into engineering terms:

Engineer-Friendly Translation by ChatGPT:
“To support clinicians, the system should prioritize detecting the onset of seizures in real time and distinguishing between different seizure types based on unique waveforms. Key EEG markers, like sharp waves and spike-and-wave patterns, are particularly important. Reducing false alarms is essential, as it helps clinicians focus on real events and avoid triggering unnecessary responses.”

In this translation, the needs become engineering requirements: real-time detection, identifying specific EEG markers, and reducing false positives. These are practical, actionable goals that engineers can design towards.

Why This Matters

Having ChatGPT as a bridge between clinical and engineering language isn’t just about making conversations smoother. It’s about building better tools that serve real healthcare needs. It’s about ensuring that everyone—whether they’re designing the technology or using it at the bedside—understands what’s happening and what the goal is. The engineer knows what metrics to optimize, and the clinician knows what features will be useful.

With applications like epilepsy monitoring, where real-time insights can make a difference in a patient’s life, clarity in communication can make the difference between a useful tool and one that sits unused. ChatGPT ensures that the technology being developed is understandable, meaningful, and actually useful for those who need it most—both the clinicians and the patients they serve.

Bringing Both Worlds Together

Biomedical engineering and clinical medicine are both powerful, but they shine even brighter when they work together. The challenge has always been language. ChatGPT helps bridge that gap, translating technical jargon into practical use cases and turning clinical goals into system requirements.

It’s about making sure everyone is heard, everyone understands, and everyone’s expertise contributes to building something that truly helps people.