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.
↗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
Ingest
Precise PositionAutomates patient positioning with an AI-enabled camera to improve positioning accuracy and consistency at scan setup.
Compute
Precise ImageUses deep-learning-based reconstruction to reduce image noise at low dose and improve diagnostic confidence.
Precise CardiacCorrects motion in cardiac images to improve image quality at high heart rates.
Orchestration
CT Smart WorkflowProvides the overall application suite KMC Manipal uses to optimize CT workflow steps from positioning through reconstruction and batching.
Precise BrainAutomatically 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."
Review how this reference was scored and trace each normalized claim back to its documented source.
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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.
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