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Kasturba Medical College (KMC) Manipal

With growing demand for CT studies, KMC Manipal needed to reduce diagnostic turnaround time and manual workflow bottlenecks in patient positioning, brain-scan batching, image quality, and dose management while maintaining high-quality patient care.

90-100 CT scans daily, up from 70-80
Now we scan 90-100 patients a day, with less overtime from staff.
This can easily save us 1-2 minutes per case.
Up to 80% lower radiation dose

Reported impact

Business outcome

KMC Manipal increased daily CT throughput, reduced manual time and staff overtime, improved image quality and diagnostic confidence, lowered radiation dose, and delivered images and reports to clinicians more quickly.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction
  • Scalability

Solution

KMC Manipal deployed Philips CT Smart Workflow across its CT exam process as an operational layer spanning acquisition, reconstruction, and review. At the ingest stage, Precise Position uses an AI-enabled camera at the scanner to automate patient positioning and improve centering consistency, reducing manual handling by staff and students. During processing, Precise Image applies deep-learning-based reconstruction to reduce image noise at low dose and improve low-contrast detectability, while Precise Brain automates batching of brain scans for faster trauma workflows and Precise Cardiac corrects motion artifacts in cardiac studies at high heart rates. The resulting images are delivered more quickly to radiologists and then to referring clinicians as reports. Philips supported rollout with an on-site innovation manager and clinical scientist, and KMC Manipal helped clinically validate the workflow in routine practice and research.

Data flow

Patients are positioned for CT acquisition using Precise Position's AI-enabled camera, then the acquired scan data is processed by Precise Image for deep-learning reconstruction and, where relevant, by Precise Brain for brain-scan batching or Precise Cardiac for motion correction. The resulting images and faster reports are delivered to KMC Manipal radiologists and referring clinicians for diagnosis and patient care.

Solution architecture

5 components · 3 layers
  1. Ingest
    • Precise Position Automates patient positioning with an AI-enabled camera to improve positioning accuracy and consistency at scan setup.
  2. Compute
    • Precise Image Uses deep-learning-based reconstruction to reduce image noise at low dose and improve diagnostic confidence.
    • Precise Cardiac Corrects motion in cardiac images to improve image quality at high heart rates.
  3. Orchestration
    • CT Smart Workflow Provides the overall application suite KMC Manipal uses to optimize CT workflow steps from positioning through reconstruction and batching.
    • Precise Brain Automatically batches brain scans so radiologists can analyze urgent studies more quickly.

Architecture clues

  • Implementation was completed in just a few weeks.
  • KMC Manipal had been using CT Smart Workflow for three years at the time of reporting.
  • Philips provided an on-site innovation manager and clinical scientist during rollout.
  • Precise Image uses a deep-learning neural network for CT reconstruction.
  • Precise Position operates at image acquisition with an AI-enabled camera.

Evidence from the source

"Now we scan 90-100 patients a day, with less overtime from staff."
"Precise Image, an AI-based reconstruction technique that uses the power of a deep-learning neural network for improved clinical confidence."
"Precise Position automates patient positioning through an AI-enabled camera designed to increase positioning accuracy and user-to-user consistency."
"This can easily save us 1-2 minutes per case"
"Using the power of AI, CT Smart Workflow provides the radiology department at KMC Manipal a comprehensive suite of applications to optimize CT workflows at every step."
"We can save up to 20 ml of contrast per angio scan."
"automatic batching of brain scans using Precise Brain, which has saved up to 5 minutes per scan."
Transparency Evidence & methodology 95/100 reference value · 91% 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 value95/100
  • 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
    6 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence91%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency10/10
    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. Philips

    Philips - How KMC Manipal serves more patients with AI-enabled CT workflows - Case study | Philips

    First-party
    https://www.philips.com/a-w/about/news/archive/case-studies/how-kmc-manipal-serves-more-patients-with-ai-enabled-ct-workflows.html
    Source type
    Vendor-published customer story
    Published
    Jun 12, 2025
    Supports
    • Customer
    • Region
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Business outcome
    • Metrics
    • Architecture
    • Publication date
    • evidence quotes

Date evidence

  • Published on source page Jun 12, 2025 · Kasturba Medical College (KMC) Manipal · First-party
    Open date source
Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.