India has just about 22 radiologists per million people – far below the nearly 200 per million in the United States. The shortfall is most visible outside large cities, where access to specialist care remains limited and uneven. For patients in smaller towns, this often translates into long, anxious waits, sometimes days just to confirm whether chest pain is pneumonia or a fracture needs urgent attention.

X-rays are frequently shared over WhatsApp and sit in queues, waiting to be read by radiologists who may be located in entirely different states. Bengaluru-based 5C Network is attempting to close this gap through an AI-powered teleradiology platform that reduces diagnostic turnaround time from over 48 hours to under 30 minutes, while maintaining expert-level accuracy.

Its platform runs the entire radiology workflow end-to-end, combining multimodal AI with human expertise to deliver faster, more consistent interpretations at scale. Today, it serves more than 1,500 healthcare facilities across 300 cities and processes over 10,000 scans each day.

“AI can pre-analyse scans in 10–20 seconds before radiologist validation,” says Kalyan Sivasailam, founder and CEO of 5C Network, calling it “a major leap in time-to-diagnosis, especially in emergencies and rural care.” The company has analysed over 10-11 million scans so far and works with a network of more than 400 radiologists.

Four of India’s ten largest hospital systems already use its platform, while much of its recent growth has come from Tier 2 and Tier 3 cities such as Hubli, Salem, Nanded and Kota – regions where the radiologist shortage is most acute. In many such cases, district hospitals that earlier waited days for reports can now access them within hours, supported by AI-driven quality checks.

The company operates in an increasingly competitive space alongside players such as Qure.ai, DeepTek and Synapsica. Most of these firms focus on building AI models that detect specific abnormalities – flagging conditions like tuberculosis, lung cancer or stroke risk to assist clinicians.

Qure.ai, for instance, develops deep-learning tools that can identify critical findings in X-rays and CT scans in under a minute, helping prioritise cases and support early diagnosis at scale. 5C Network’s approach, however, is structurally different. While many competitors operate as AI layer providers – offering detection and triage tools that still rely on in-house radiologists – 5C positions itself as a full-stack radiology platform.

It combines AI with a distributed network of radiologists to deliver final, signed reports, effectively outsourcing the entire diagnostic workflow for hospitals that lack specialist capacity. Unlike point solutions, 5C spans the entire imaging journey – from scan acquisition to final report.

At the core of this system is Bionic, an AI co-pilot designed to assist radiologists at multiple stages. It identifies abnormalities, generates structured reports and validates them for potential errors, acting as a second layer of review.

Reading a scan is a cognitively demanding task, involving pattern recognition, anatomical navigation, clinical reasoning and language generation – often all at once. Under high workloads, radiologists tend to move quickly once they reach a “good enough” interpretation.

The bigger risk, Sivasailam argues, is omission rather than misinterpretation, with secondary findings often missed due to limited cognitive bandwidth. To address this, 5C offloads repetitive and time-intensive tasks to AI. Its system produces a structured pre-read as soon as a scan arrives – highlighting findings, measurements and potential pathologies – allowing radiologists to focus on validation and clinical judgment rather than drafting reports from scratch.

A continuous learning loop strengthens this process: every correction made by a radiologist is fed back into the system, refining the models over time. Despite its progress, last-mile adoption – especially in the public healthcare system – remains a challenge due to slow procurement cycles.

To address this, 5C Network is embedding its AI directly into imaging hardware. In partnership with BPL Medical Technologies, it has compressed its models to run on as little as 16GB of RAM, enabling on-device deployment without internet connectivity. The aim is to make AI-driven diagnostics more accessible in primary health centres and district hospitals, where specialist availability remains limited.