KMC Manipal Shows AI-Enabled CT Workflows Serve 30 More Patients Daily

While much of the conversation around AI in radiology focuses on research papers and conference presentations, Kasturba Medical College (KMC) at Manipal Hospital has delivered something more tangible: real-world numbers. Their AI-enabled CT workflows have increased daily patient throughput by 20 to 30 cases per clinician, without any drop in diagnostic accuracy.

How they did it

The implementation centered on integrating AI-assisted tools directly into the CT reading workflow rather than treating them as separate applications. The AI handles initial image processing, automated measurements, and preliminary flagging of abnormalities. Radiologists then review these AI-prepared cases, spending their cognitive effort on interpretation and clinical correlation rather than on repetitive measurement tasks.

The key insight from the Manipal experience is that the time savings did not come from AI replacing radiologists. It came from AI eliminating the mechanical, repetitive portions of the workflow: windowing, measuring, counting, and formatting. These tasks consume a surprising amount of a radiologist reading session but require relatively little clinical judgment.

The numbers in context

Serving 20 to 30 additional patients daily per clinician is a significant gain for Indian healthcare. In a country where the radiologist-to-population ratio remains well below WHO recommendations, and where many patients wait days or weeks for imaging reports from overburdened departments, even modest per-radiologist efficiency improvements aggregate into meaningful system-level impact.

If this model were replicated across major teaching hospitals in India, the collective increase in reporting capacity could be equivalent to adding hundreds of new radiologists to the workforce, without the decade-long training pipeline that actually producing new radiologists requires.

Challenges of replication

The Manipal model worked partly because the institution had the IT infrastructure, administrative support, and change management culture to implement AI workflows properly. Many Indian hospitals, particularly government facilities and smaller private centers, lack the PACS infrastructure, network bandwidth, and technical support staff needed for similar deployments.

The hardware requirements are also worth noting. AI-assisted CT workflows demand capable GPU servers for inference, reliable high-speed networking between the scanner and PACS, and integration middleware that plays well with existing systems. These are not insurmountable barriers, but they represent real costs that smaller facilities may struggle to justify.

Lessons for the field

The Manipal experience offers three practical takeaways for Indian radiology departments considering AI adoption. First, start with workflow automation rather than diagnostic AI, because the return on investment is faster and easier to measure. Second, involve radiologists in the implementation from day one, since workflow changes imposed without clinician input consistently fail. Third, measure outcomes rigorously, because "it feels faster" is not the same as demonstrable throughput improvement.

The data from Manipal suggests that AI in Indian radiology is moving from pilot projects to operational deployments. The question is no longer whether AI works in the Indian clinical context, but how quickly other institutions can follow.