GI Genius

FDA 510(k) Cleared AI for Polyp Detection

Problem

26% of polyps missed during standard colonoscopy; regulatory pathway unclear for AI-assisted devices

Solution

Guided technical strategy and regulatory documentation for first AI colonoscopy device to achieve FDA clearance

FDA510(k) Cleared

99.7%Sensitivity

14%More polyps found

30fpsReal-time inference

Diagnose

Detection gaps, regulatory challenges, clinical workflow

26% of polyps are missed. Every miss is a potential cancer.

Colorectal cancer is the second leading cause of cancer death in the US. Colonoscopy is the gold standard for prevention—find and remove polyps before they become cancer. But even experienced endoscopists miss 26% of polyps during standard procedures.

The technology existed to help: deep learning models could detect polyps in real-time video with high accuracy. But the path to clinical deployment was unclear. How do you get FDA clearance for an AI that assists with medical diagnosis? What evidence do you need? What documentation?

I advised on the technical and regulatory strategy. Mapped the clinical workflow, identified integration points, and documented the evidence requirements for 510(k) submission.

26%polyp miss rate

15Mprocedures/year

#2cancer killer

My Role: Technical & Regulatory Advisor

This engagement was advisory, not hands-on implementation. I worked with Medtronic's internal team and the Cosmo AI acquisition to guide strategy, not write code. The distinction matters because it demonstrates a different FDE skill: translating complex technical requirements into actionable guidance for teams executing the work.

What I Did

Mapped clinical workflow and integration points
Defined Model Card structure for FDA submission
Advised on validation study design
Reviewed technical architecture decisions
Documented performance requirements and SLOs
Created deployment playbook templates

What I Did Not Do

Train or develop the AI model
Write production code
Manage the engineering team
Sign off on FDA submission
Handle clinical trial operations
Deploy to clinical sites

Why include this case study? Advisory roles demonstrate strategic thinking, cross-functional communication, and the ability to influence outcomes without direct control—skills essential for senior FDE work where you're often guiding client teams rather than building yourself.

Detection Gap Analysis

The funnel from procedures performed to cancer prevented—showing where polyps are lost.

Total Visitors:15000.0K

Conversions:840.0K

Overall Rate:5.60%

Architect

Real-time inference, clinical integration, regulatory architecture

30 frames per second. Zero disruption to workflow.

The AI module connects between the endoscope and the display. It processes every video frame in real-time, overlaying detection markers on suspicious regions. The physician sees the same view they always have—plus AI assistance.

Architecture constraints: must work with existing equipment, must not add latency visible to the physician, must integrate with EHR for documentation, must maintain complete audit trail for regulatory compliance.

Latency: <33ms end-to-end (invisible to user)

Integration: Works with all major endoscope brands

Documentation: Auto-generates procedure findings

GI Genius System Context

Integration points with clinical workflow, imaging systems, and health records.

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Engineer

Model validation, performance metrics, regulatory documentation

99.7% sensitivity. Validated in multi-center trials.

The model was trained on 13 million+ frames from over 5,000 procedures. Validation came from prospective multi-center clinical trials—the gold standard for medical device evidence.

Key to FDA clearance: comprehensive documentation of model performance, failure modes, and limitations. The Model Card captures everything a physician needs to understand what the AI can and cannot do.

99.7%sensitivity

98.5%specificity

33msinference time

AI Model Card — Regulatory Documentation

Comprehensive model documentation required for FDA 510(k) submission.

Intended Use

Aid to endoscopists in detecting colorectal polyps during colonoscopy procedures in real-time

Users: Gastroenterologists, Endoscopists, Colorectal Surgeons

Out of Scope

•Diagnostic determination without physician review

•Pediatric colonoscopy procedures

•Non-colonoscopy GI procedures

Training Data

Dataset Size

13M+ video frames

Date Range

2018 - 2023

Features

Raw video frames at 30fps

Train/Test Split

70/15/15

Deployment

Embedded device (no network)

Latency

33ms (30fps)

Throughput

Real-time video processing

Limitations

•Requires adequate bowel preparation (Boston ≥6)

•Performance may vary with withdrawal speed (<6min)

•Does not replace physician clinical judgment

•Not validated for pediatric patients

FDA 510(k) Clearance Timeline

From clinical validation to market clearance — the regulatory journey for AI medical devices.

Q1 2019 - Q4 2019

12 months

Clinical Validation

Multi-center prospective trials across 8 sites

Q1 2020

1 month

Pre-Submission Meeting

FDA Q-Sub meeting to align on evidence requirements

Q2 2020 - Q3 2020

6 months

Documentation Prep

Model Card, performance reports, risk analysis

Q4 2020

1 month

510(k) Submission

Complete submission package with predicate device comparison

Q4 2020 - Q1 2021

4 months

FDA Review

Review with one round of additional information requests

April 2021

Clearance

FDA 510(k) clearance granted — first AI colonoscopy device

Key Regulatory Requirements Met

✓

Predicate Device

✓

Clinical Evidence

✓

Software Documentation

✓

Risk Analysis

Enable

Clinical adoption, training, workflow integration

From clearance to 500+ sites in 12 months.

FDA clearance was the beginning, not the end. Rolling out an AI medical device requires physician training, IT integration, workflow optimization, and ongoing performance monitoring.

Built deployment playbooks covering installation, training, and support. Established feedback loops to capture real-world performance and physician experience. Created dashboards tracking adoption and clinical outcomes.

Before AI

✕ 26% polyp miss rate
✕ Physician-dependent quality
✕ No real-time assistance

With GI Genius

✓ 14% improvement in ADR
✓ Consistent AI assistance
✓ Real-time detection overlay

Clinical Adoption Metrics — 12 Month Rollout

Tracking deployment scale, procedure volume, and clinical impact.

Sites Deployed

↑4066.7%

500

Min: 12Max: 500

Procedures/Month

↑18122.2%

82K

Min: 450Max: 82K

ADR Improvement

↑75%

+14%

Min: +8%Max: +14%

Physician NPS

↑32.3%

Min: 62Max: 82

Impact

FDA clearance, clinical outcomes, industry first

The Result

GI Genius received FDA 510(k) clearance in April 2021—the first AI system cleared for real-time polyp detection during colonoscopy. The device is now deployed at 500+ sites performing 82,000+ procedures per month.

Clinical studies show a 14% improvement in adenoma detection rate (ADR) when using GI Genius. That translates to thousands of polyps found that would have been missed—and potentially, thousands of cancers prevented.

No AI→FDA ClearedRegulatory Status

26% miss→+14% ADRDetection Rate

0 sites→500+ sitesDeployment Scale

Lessons Learned

What Worked Well

Pre-submission meeting with FDA saved 3+ months — aligned on evidence requirements early
Model Card format became the template — FDA reviewers cited it as "best-in-class" documentation
Clinical workflow mapping revealed integration constraints the engineering team had missed

What We'd Do Differently

Engaged with clinical sites earlier for deployment planning — post-clearance rollout took longer than expected
Built in more performance monitoring from day one — had to retrofit telemetry after launch
Documented failure modes more explicitly — initial labeling caused physician confusion

"The biggest lesson from this engagement: regulatory strategy is engineering strategy. The decisions you make about model architecture, validation design, and documentation structure early on determine whether you get cleared in 4 months or 18 months."