The Top 5 AI Medical Scribes for Gastroenterology

Summary

• DeepScribe is the best overall AI medical scribe for gastroenterology in 2025. It’s trained on GI and hepatology encounters, understands complex cases like IBD flares and decompensated cirrhosis, and preserves the nuance of your clinical reasoning in the note.
• The most important criteria for GI practices are specialty depth, EHR integration depth (including endoscopy and pathology), coding and revenue intelligence (HCC, E/M, ICD-10), and customization at the clinician and practice level.
• DeepScribe delivers bidirectional EHR integration, pulling prior scopes, pathology, imaging, and labs into context and writing structured data back into discrete fields where supported.
• With GI-focused coding intelligence, DeepScribe helps capture risk-adjusting diagnoses and procedure details that are often missed in manual documentation, reducing revenue leakage.
• Other AI scribes can be a fit in specific situations, but for most gastroenterology groups—especially those juggling high procedure volume and complex chronic liver/IBD populations—DeepScribe offers the strongest blend of specialty depth, workflow fit, and ROI.

Why Gastroenterology Needs a Different Kind of AI

Most AI medical scribes on the market today are “generalist” offerings. Meaning they perform well for standard primary care visits, but frequently struggle when faced with the longitudinal complexity of gastroenterology.

For a gastroenterologist, a patient visit is never just a simple conversation. It’s a synthesis task that requires the clinician to weave together pathology reports, imaging, and previous endoscopy findings into a cohesive narrative. A generic scribe that isn’t trained on the complexities of gastroenterology might produce an accurate note in a vacuum, but it won’t ingest all of the other data that makes that encounter complex and that patient unique.

Using a generalist AI tool in a specialist workflow creates four specific risks that we’ve come to identify over nearly a decade in this space:

The “Look-Alike” Error

Generic models often struggle to distinguish historical data from current findings. For example, they may see “history of tubular adenoma removed in 2019” and document it as an active tubular adenoma today, incorrectly updating the problem list and surveillance context.

Revenue Leakage

General-purpose AI scribes often miss risk-adjusting conditions and procedural specificity, leading to lost HCC, E/M, and ICD-10 coding opportunities. Over time, these small documentation gaps compound into significant, often invisible revenue loss for GI practices.

Poor Fit With GI Workflows and Templates

GI visits follow distinct patterns like IBD and cirrhosis follow-ups, reflux management, bowel habit evaluations, and pre-/post-procedure visits. Generalist scribes often generate generic HPIs or assessments that don’t match these nuanced structures, increasing editing time and cognitive load.

Gaps in GI Terminology, Staging, and Medication Understanding

If the AI isn’t trained on GI language (MELD scores, Child-Pugh staging, variceal grading, polyp morphology, or biologics like vedolizumab and ustekinumab) it may mistranscribe or omit key details that drive clinical decisions, surveillance planning, and billing accuracy.

To deliver real ROI in 2025, an AI scribe for gastroenterology must go beyond transcription. It must synthesize longitudinal history, interpret natural verbalization into precise medical terminology, and integrate bidirectionally with GI workflows. When those capabilities are missing, the risks compound: generalist ambient AI tools misinterpret historical findings, miss coding-specific details, and fail to match GI workflows, ultimately creating more work rather than less. A specialty-trained scribe avoids these pitfalls by understanding the complexity, terminology, and documentation demands that define modern GI practice.

How to Choose an AI Scribe for Gastroenterology

When evaluating vendors, consider these four pillars to determine if a tool is truly GI-ready.

Specialty Focus

Does the AI model understand the language of the gut? Can it distinguish between surveillance intervals based on guidelines mentioned in passing? Does it correctly capture MELD or Child-Pugh scores without manual intervention? Without deep specialty understanding, your AI scribe will flounder (or worse, hallucinate) when presented with complex cases.

EHR Integration Depth

Read Capabilities: Can the AI pull the last colonoscopy report from the EHR before the visit starts so you (and the AI) have the context needed to provide accurate care and produce the corresponding documentation?

Write Capabilities: Does the AI scribe just paste a text block back into your EHR, or does it dynamically populate the discrete fields in your medical record system?

Coding Support & Revenue Intelligence

Real-time HCC Capture: Does the AI actively flag risk adjustment opportunities during the visit?

E/M Leveling: Can the AI scribe support E/M coding based on the complexity of the visit and medical decision making? 

Customization

GI documentation is both specialty-specific and highly personal. Your AI scribe should adapt to your practice’s standard templates for consults, follow-ups, and procedure notes — and learn each clinician’s preferred structure, phrasing, and level of detail over time.

Ranked: The Leading AI Scribes for Gastroenterology

1. DeepScribe

Best For: Overall, the best AI medical scribe for gastroenterology.

DeepScribe stands out for gastroenterology practices that need customizable, specialty-aware AI to capture the complexities of GI and hepatology. DeepScribe is trained on millions of specialty-specific encounters, allowing it to understand the difference between a routine screening discussion and a complex IBD flare assessment, ensuring that the nuance of your clinical reasoning is preserved.

Specialty-Specific Models: DeepScribe’s AI is fine-tuned for GI and hepatology. It accurately captures complex gastroenterology cases with ease.

Deep Customization: Unlike one-size-fits-all AI medical scribes, DeepScribe learns clinicians voice and preferences on day one. It also continuously refines outputs based on your edits, ensuring notes automatically match your personal style, tone, and structure.

Bi-Directional EHR Integration: DeepScribe ingests patient history (labs, imaging, medications, etc.) and uses it to craft more clinically relevant documentation. Plus, everything DeepScribe generates syncs to the right place in your EHR.

Coding Intelligence: DeepScribe proactively identifies and suggests HCC codes that are often missed in manual documentation, insulating your practice from revenue leakage. DeepScribe also documents E/M codes, including visit complexity and medical decision making, as well as ICD-10 codes.

2. Nuance DAX Copilot (Microsoft)

Best For: Gastroenterologists within large health systems where Epic integration and corporate IT governance are the primary requirements.

As a Microsoft subsidiary, Nuance has a significant footprint in the healthcare market. DAX Copilot offers native integration into Epic, allowing for a direct data flow within the EHR environment. For gastroenterologists employed by large academic health systems, this infrastructure support and enterprise-grade security are often key factors in organizational adoption.

Enterprise Integration: Provides established connectivity with major EHRs (specifically Epic and Meditech), supporting data management across large hospital networks.

Stability: Leverages Nuance’s long-standing speech recognition technology to deliver consistent performance in standardized clinical settings.

Considerations for GI:

Standardization vs. Agility: Designed for scale, the system generally prioritizes standardized note formats. This can limit the ability of individual GI providers to create highly customized templates for specific workflows (such as motility studies or complex IBD consults) without administrative IT intervention.

Turnaround Time: Depending on the specific deployment model, note generation may not be instantaneous, which can create workflow friction during high-volume endoscopy sessions.

3. Suki

Best For: Clinicians who prefer a voice-command interface to control their EHR and manage administrative tasks.

Suki operates as both a scribe and a digital assistant. Beyond clinical documentation, it allows users to utilize voice commands to retrieve information ("Show me the last colonoscopy report") or queue up orders. This dual functionality is designed to reduce the manual clicking associated with administrative tasks. 

Voice Command Utility: The ability to navigate the EHR and manage orders via voice offers a functional alternative to traditional keyboard-and-mouse inputs.

Coding Assistance: Includes built-in support for ICD-10 and HCC coding, assisting providers in capturing billing codes during the documentation process.

Considerations for GI:

"Assistant" vs. "Ambient": The workflow frequently relies on specific voice commands rather than purely passive listening. For GI specialists seeking an automated background experience, this command-driven approach may require more active management during the patient visit. 

Specialty Depth: While effective for general workflows, its template customization for complex hepatology or procedural nuances may require more manual configuration than a purpose-built specialty solution.

4. Abridge

Best For: Multi-specialty groups or Primary Care providers standardized on Epic

Abridge focuses heavily on the post-visit patient experience. Its technology generates both a clinical note for the provider and a simplified summary for the patient, translating medical jargon into plain language. This feature is particularly useful for improving patient comprehension of diagnoses and care plans.

Auditable AI: Features a "Linked Evidence" tool that allows users to verify the note accuracy by clicking on text to replay the corresponding segment of the audio recording.

User Interface: Offers a modern, mobile-first design that is widely cited for its ease of use and quick implementation.

Considerations for GI:

Generalist Focus: The platform is widely utilized in Primary Care and Internal Medicine. Its language models may lack the granular training found in specialist tools regarding complex GI procedure coding or specific sub-specialty terminology (e.g., specific liver staging criteria).

Template Rigidity: Similar to other broad-market solutions, it often favors standardized formats, which may restrict a specialist's ability to tailor documentation for nuanced procedural reports.

5. Commure

Best For: Practices that prioritize verified accuracy over real-time speed, or those with complex workflows that purely automated tools struggle to capture.

Commure (following its acquisition of Augmedix) utilizes a model that frequently incorporates human review. By combining AI with human medical scribes for quality assurance, the platform aims to deliver high-accuracy documentation even for non-standard or complex patient encounters.

Verified Output: The inclusion of human review provides a layer of verification, ensuring that complex narratives are captured correctly.

Broad Connectivity: The platform supports a wide range of EHRs and is often viable for environments using legacy systems or those not yet on cloud-based infrastructure.

Considerations for GI:

Speed & Scalability: Reliance on human review can result in longer turnaround times compared to fully automated AI. For GI providers requiring immediate chart closure after a clinic session, this latency can be a bottleneck.

Cost & Model: The human-in-the-loop approach is generally more resource-intensive than pure AI software, and it typically lacks the instant "pre-charting" synthesis capabilities found in fully automated platforms.

Making the Right Choice for Your GI Practice

Ultimately, the “best” AI scribe for gastroenterology depends on your specific situation. Consider the following before choosing:

• EHR platform
• Practice size and structure
• Mix of clinic vs. procedure volume
• Documentation complexity
• Available IT resources
• Appetite for customization vs. turnkey simplicity

Then, determine your primary pain points:

• If after-hours charting is the problem, prioritize platforms with proven time-savings in high-complexity visits.
• If coding accuracy and revenue capture are the priority, focus on solutions with strong AI coding intelligence.
• If synthesizing longitudinal data (IBD, chronic liver disease, prior scopes, and pathology) is the bottleneck, evaluate depth of EHR integration and contextual awareness.

When you evaluate vendors:

• Request demos using real gastroenterology cases, not generic primary care scenarios.
• Bring real cases: IBD, decompensated cirrhosis, recurrent polyps with dysplasia, GI bleed consults, etc.
• Ask for pilot data and references from other GI practices or specialty practices, ideally with similar size and EHR setup to yours.

Finally, assess the vendor’s commitment to specialty development. General-purpose AI may handle straightforward visits reasonably well, but often struggles with GI’s layered complexity. Purpose-built, specialty-aware AI starts stronger and improves faster because the underlying models truly understand your field.

Ultimately, the right ambient AI medical scribe should feel almost invisible: it captures complex GI conversations, incorporates diagnostic and procedural context, supports accurate billing, and gives you back time to focus on patients rather than documentation.

A Roadmap to Implementing Ambient AI in a GI Practice

Phase 1: The Pilot (Strategic Selection) Don't pilot with your easiest provider. Start with a high-volume provider or a complex IBD specialist to truly test the AI's capability. Metrics to watch include "Time to Chart Closure" and "After-Hours Documentation."

Phase 2: Template Tuning Use the AI's customization features to map your specific macros (e.g., "Normal Colonoscopy Follow-up") so the output feels like you wrote it.

Phase 3: Revenue Audit Compare E/M coding levels pre- and post-implementation. Look for improved capture of comorbidities (HCCs) in chronic liver/GI patients.

Experience the Best AI Solution for Gastroenterology

If after-hours charting is cutting into your personal time or you’re struggling to capture the full complexity of your GI and liver patients in your documentation, then t’s worth seeing what specialty-specific AI can do.

DeepScribe is built for specialties like gastroenterology, where diagnostic complexity, procedural volume, and longitudinal context define high-quality care.

Request a demo to:

• Walk through your real clinic and procedure workflows
• See how DeepScribe handles your most complex GI cases
• Explore how specialty-aware AI could work in your environment

FAQ

Do AI scribes work for GI procedures and complex cases? 

Yes. Purpose-built platforms like DeepScribe are explicitly designed for diagnostically intensive cases. They capture structured documentation for complex procedures—including Colonoscopies (with polypectomy/EMR), EGDs, and ERCPs—ensuring findings, clinical interpretation, and billing codes are captured with a level of detail that generic AI cannot match.

How do AI medical scribes integrate with gastroenterology EHR systems?

The best tools integrate bidirectionally. They don't just push text; they pull longitudinal context (prior colonoscopy reports, pathology, lab trends) to enrich the current note and write structured data back into discrete EHR fields, ensuring the patient's full GI journey is tracked accurately.

Can AI scribes capture HCC codes for GI and liver patients?

Yes. Leading platforms utilize coding intelligence to flag HCC-eligible conditions like chronic liver disease, cirrhosis, and malabsorption. DeepScribe ensures these are documented with the necessary ICD-10 specificity to support accurate risk adjustment and reimbursement.

How long does it take to implement an AI scribe in a gastroenterology practice?

Typically 2–12 weeks. A focused pilot with GI and hepatology champions usually runs 3–6 weeks. Choosing a vendor like DeepScribe, which offers deep customization and pre-trained specialty models, significantly accelerates adoption compared to generic tools that require extensive training.

‍