India has long struggled with a shortage of radiologist. With only an estimated 10,000–15,000 radiologists serving a population of 1.4 billion, the healthcare system faces mounting pressure to deliver timely and accurate diagnoses. However, recent advancements in artificial intelligence (AI) may offer a much-needed solution.
New Study Highlights AI’s Potential in Radiology
A recent, yet-to-be-peer-reviewed paper titled ‘Autonomous AI for multi-pathology detection in chest X-rays: A multi-site study in the Indian healthcare system’ presents promising findings. Authored by Kalyan Sivasailam, CEO of 5C Network, and collaborators, the study demonstrates how autonomous AI can significantly reduce radiologists’ reporting times—by nearly 50%.
Why AI Is Critical in Medical Imaging
According to the paper, the overwhelming workload on radiologists makes it essential to introduce AI support systems. “AI as an aid will help lessen the workload while increasing the accuracy of analyses,” the authors emphasize.
To address this, the research team developed an autonomous AI system trained on over 5 million chest X-rays sourced from healthcare facilities across India. The system incorporates advanced architectures like vision transformers, faster R-CNN, and U-Net models (including attention U-Net, U-Net++, and dense U-Net) to detect, classify, and segment 75 distinct pathologies—from pneumonia to complex lung conditions.
Exceptional Accuracy in Identifying Pathologies
The AI tool demonstrated remarkable performance in identifying normal and abnormal chest X-rays:
- Precision: 99.8% of AI-flagged “normal” X-rays were indeed normal.
- Recall: 99.6% of all actual normal X-rays were correctly identified.
- Negative Predictive Value: When the AI labeled an X-ray as normal, there was a 99.9% likelihood that it was truly normal.
Furthermore, the tool accurately detected specific pathologies in up to 98% of cases and achieved over 95% accuracy in identifying most conditions.
Pinpointing Abnormalities with Precision
In addition to classification, the AI tool generates bounding boxes around suspected pathology regions, allowing radiologists to visualize the location and extent of abnormalities. It also performs pixel-level segmentation, offering detailed insight into the shape and spread of each condition.
“By automating the initial analysis and flagging potential issues, the AI enables radiologists to concentrate on more complex cases,” explains Sivasailam.
Efficiency Without Compromising Expertise
While the AI system doesn’t eliminate the need for radiologist oversight, it enhances efficiency. Sivasailam clarifies, “Radiologists still validate the AI’s output. However, if reading an X-ray typically takes two minutes, AI can reduce that time by 50–60%.”
As reported by thehindubusinessline, this efficiency gain allows radiologists to spend more time on challenging or abnormal cases, ultimately improving the quality of care.
Understanding Autonomous AI in Healthcare
So what exactly is autonomous AI?
Sivasailam defines it as a system capable of independently performing tasks using integrated AI capabilities—like perception, decision-making, and language generation—with minimal human input.
In a medical context, it works like this:
An X-ray is fed into a system that uses computer vision to detect abnormalities. If it finds any, this output is passed to a language model, which then generates a structured diagnostic report. The radiologist simply validates this report, reducing manual effort while maintaining clinical accuracy.
The Road Ahead: AI as a Partner, Not a Replacement
While AI won’t replace radiologists, it stands to become a powerful partner in improving healthcare delivery. With high accuracy, faster turnaround times, and detailed visualizations, autonomous AI has the potential to transform radiology in resource-strained systems like India’s.