Medtronic Healthcare Florida, United States Company newsroom

AdventHealth West Florida Division

AdventHealth wanted to improve early detection of precancerous polyps and colon cancer during routine colonoscopies by reducing missed lesions and increasing adenoma detection rates.

99.7%-100% sensitivity rate
Using traditional methods, my ADR is an impressive 48%. With the implementation of GI Genius™, my ADR increased to 68%
helping reduce the chance of missed polyps by up to 50%
no additional cost to patients nor added procedural time

Reported impact

Business outcome

AdventHealth reports improved diagnostic accuracy and earlier polyp detection in colonoscopy, including a physician-reported adenoma detection rate increase from 48% to 68%, with no additional patient cost or added procedural time.

  • Productivity Improvement
  • Risk Reduction

Solution

AdventHealth West Florida rolled out Medtronic’s GI Genius™ intelligent endoscopy module across its hospitals to add AI-assisted detection to routine colonoscopy workflows. In this deployment, the input is the live visual stream generated during the colonoscopy. GI Genius processes that stream in real time and acts as a “second set of eyes,” highlighting suspected precancerous polyps and adenomas so the endoscopist can inspect them immediately during the same procedure. The article ties the module directly to higher adenoma detection rates and fewer missed lesions, while noting no added procedural time or extra patient cost. No separate storage tier, model-training environment, or downstream analytics stack is described; the implementation presented is a point-of-care clinical decision support layer embedded in existing endoscopy practice across AdventHealth’s West Florida hospitals.

Data flow

During a routine colonoscopy, the live endoscopic visual stream is processed by the Medtronic GI Genius™ module in real time. The system flags suspected precancerous polyps and adenomas for the endoscopist during the same procedure, allowing immediate clinical review and supporting higher detection rates without added procedural time.

Solution architecture

1 components · 1 layer
  1. Serving
    • Medtronic GI Genius™ intelligent endoscopy module Analyzes live colonoscopy video in real time and alerts clinicians to suspected polyps as a second set of eyes.

Architecture clues

  • Deployed across all AdventHealth hospitals in the West Florida Division
  • Presented as a clinician decision-support tool that serves as a second set of eyes
  • Real-time AI assistance is used during routine colonoscopies at the point of care

Evidence from the source

"All AdventHealth hospitals in the West Florida Division are setting a new standard in colorectal cancer detection by leveraging Medtronic’s GI Genius™ intelligent endoscopy module"
"GI Genius™ is now available at AdventHealth Hospitals across the West Florida Division at no additional cost to patients nor added procedural time."
"The Medtronic GI Genius™ system demonstrates AI's capacity to improve diagnostic accuracy by serving as a second set of eyes for clinicians"
"Using traditional methods, my ADR is an impressive 48%. With the implementation of GI Genius™, my ADR increased to 68%"
"a groundbreaking technology that uses artificial intelligence (AI) to assist during routine colonoscopies, helping reduce the chance of missed polyps by up to 50%."
Transparency Evidence & methodology 90/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth20/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AdventHealth West Florida Division

    AdventHealth West Florida Division customer story

    First-party
    https://www.adventhealth.com/news/adventhealth-west-florida-hospitals-using-ai-technology-early-colon-cancer-detection
    Source type
    Customer-published story
    Published
    Nov 20, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Nov 20, 2024 · AdventHealth · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.