Cutting Through the Noise: What Differentiates AI Scribe Tools?

Cutting Through the Noise: What Differentiates AI Scribe Tools?

According to Harvard research, when artificial intelligence worked independently to diagnose patients, it achieved 92% accuracy, while doctors using AI assistance reached only 76% — barely better than the 74% accuracy they achieved without AI support.

This clearly highlights one of the major dilemmas in healthcare: the human factor remains critically important, even despite the impressive capabilities of artificial intelligence.

AI has long been integrated into healthcare: from analyzing MRI scans to diagnosing eye diseases (Google DeepMind), to automating medical documentation (IBM Watson Health). These early steps helped automate routine processes, but they only scratched the surface of what AI could offer.

The real breakthrough came with the emergence of AI Scribe Tools — solutions that are not just optimizing separate tasks, but fundamentally transforming the interaction between doctors and patients. The COVID-19 pandemic acted as a powerful accelerator: overwhelmed healthcare systems urgently needed tools that could reduce administrative burdens and allow doctors to focus more on patient care.

Since 2020, dozens of startups have entered the market — including DeepScribe, Augmedix, Notable Health, Ambience Healthcare, among others — offering AI-based solutions to automatically create medical notes after patient consultations. Today, there are over 100 medical AI Scribe Tools, and the number keeps growing.

However, alongside rapid expansion came a significant challenge.

The easier it became to launch a new AI Scribe Tool, the harder it became to truly stand out. Most tools deliver nearly identical functionality, and simply building "yet another transcription AI" no longer impresses doctors or investors.

The real challenge today is not merely to automate text creation but to address real clinical needs and deliver meaningful practical value.

This critical issue sets the stage for our exploration: what truly differentiates modern AI Scribe Tools — and where could the next major breakthrough emerge?

A Market Full of Similarities: Why Most AI Scribe Tools Look Alike

Even though interest in AI Scribe Tools keeps growing, many of these products feel almost the same. From their names and websites to what they actually do — it’s hard to tell them apart. And honestly, this isn’t surprising.

Most Tools Use the Same Tech

Most AI Scribe Tools in healthcare are built on the same basic technologies — speech recognition (ASR), natural language processing (NLP), and large language models (LLMs). These tools turn what doctors say into text, organize that text into sections like "Symptoms" or "Plan," and ideally connect it with a patient’s medical record.

Still, a few companies have tried to push these technologies further. For example, DeepScribe combines speech recognition with custom-trained clinical models to capture not just words, but the context behind patient conversations. Ambience Healthcare focuses on real-time assistance, offering suggested notes and follow-up actions during the consultation itself.

Developers often use the same APIs, like Google Speech-to-Text or Microsoft Azure. So even if each company says their tool is “unique,” the core of it is usually built on the same kind of system.

Everyone Expects the Same Features

Hospitals and clinics — the main users of these tools — usually want the same things: clear transcripts, automatic formatting, and easy connection to their systems. Because of that, most companies don’t try to go far beyond the basics. They create tools that are safe, reliable, and similar to what’s already on the market.

Also, in medicine, there’s not much room for risky ideas. A bug in the system could lead to serious mistakes, so many teams choose simple and stable solutions, especially with rules like HIPAA and GDPR to follow.

So Many Products, So Little Difference

Another reason tools look alike is that the market grew too fast. In just a few years, over 100 different AI Scribe Tools showed up. A lot of them were made quickly just to catch the wave of attention around AI. Because of that, many products ended up being almost copies of one another — with the same features and promises, just in different words.

And the growth still continues. New projects launch almost every month. Why? Because it’s easier than ever to build something — with open models, ready-made libraries, and easy access to APIs, you don’t need a big team or a lot of time to get started.

Are the Differences Just Marketing?

To stand out, some teams try to change the look or style of their tool. They use bright websites, new buzzwords, or say they “reinvent documentation.” But in the end, most tools still do the same three things: transcribe, organize, and connect to the record system.

While most differences are cosmetic, a few companies have introduced deeper innovations. DeepScribe, for instance, doesn't just transcribe; it organizes notes automatically into SOAP format and adapts to each doctor's unique speaking style over time. Ambience Healthcare focuses on real-time support, offering suggested notes and actions during the consultation.

This fragmented landscape was the focus of a recent episode of Digital Health Inside Out. In the discussion, Brendan Keeler framed the diversity of tools with a fitting analogy:

> “Not everyone needs a Ferrari, and some people needed a Prius, and some people get diluted into buying the lemon.”

The panel also raised important concerns about how most tools rely on the same foundational LLMs and ASR engines, often offering only a “thin wrapper” of value unless they evolve.

As Alex LeBrun, co-founder of NABLA, noted:

> “It’s not just about summarizing conversations — a true scribe integrates deeply with EHRs, handles structured coding, and proactively supports the clinician with workflows.”

The full panel discussion offers a deeper industry perspective and is available here: [YouTube – Digital Health Inside Out]

Conclusion

After diving into the world of AI Scribe Tools, it’s clear that this space is growing fast — maybe even too fast. So many tools are being created, but most of them still look and feel the same. They use the same technologies, offer similar features, and promise to make doctors’ lives easier. But the real question is: how many of them actually do?

It seems like building a basic scribe tool has become easy. The hard part now is building something that truly makes a difference — not just by writing down what was said, but by helping doctors work smarter and focus more on the patient.

From what I’ve seen, there are already a few companies trying to break out of the pattern and offer something more meaningful. Maybe the next big step isn’t about doing more, but about doing it better — with real understanding of how doctors actually work.

This question — why do we need 126 different AI scribe tools? — was at the core of the expert panel discussion in the Digital Health Inside Out episode which was mentioned before. The consensus was that this abundance is not just noise — it’s a reflection of a highly fragmented healthcare ecosystem, where each solution fills a specific niche or adapts to a particular specialty. Some tools, as the speakers pointed out, are mere wrappers over generic LLMs, while others strive for deep EHR integration, clinical coding, and decision support. The variety may seem overwhelming, but it highlights how diverse the real needs across healthcare workflows truly are.

There’s still room for something new here. And maybe, just maybe, the next truly helpful tool hasn’t been built yet.