AI That Streamlines Workflows for Radiologists Debuts in India

India has roughly 20,000 radiologists serving a population of over 1.4 billion, indicating that the country has approximately one radiologist for every 1 lakh people. This is significantly lower than the global average of 4.2 radiologists per 1 lakh people as of 2023. The diagnostic burden is immense, with only one radiologist available to interpret every 100 scans conducted daily. 

SPARK Radiology has launched SPARK.ai, an AI integrated radiology platform designed to alleviate burnout, enhance diagnostic accuracy and streamline workflows for radiologists. The launch marks its debut in India’s healthcare technology space.

SPARK.ai recognises this critical gap and has introduced AI-powered efficiencies that promise to ease administrative workloads while enabling quicker, more accurate diagnosis.

The platform accurately detects the radiologist’s impressions from the diagnosis and incorporates them into the template. This eliminates the need for an intermediary to manually type and file the report.

“We’re here to let radiologists concentrate on the complex, life-saving decisions they’re trained for, while our technology handles the rest,” said Allison Garza, CEO of SPARK Radiology. Radiologists are at the frontline of diagnosis, yet a significant portion of their time is consumed by repetitive administrative tasks. “This platform is built to give them that time back,” he added.

The platform has been tested across independent clinics, hospitals, and diagnostic centres, and its capabilities have been refined over several months. It seamlessly integrates with existing systems such as PACS and employs a head-up display (HUD) to help radiologists document findings in real time, through automated structured reports and smart templates.

“Nothing is missed nor added by itself through SPARK.ai,” said Suresh Joel, CTO, SPARK Radiology. “It helps bridge communication gaps between the radiologist and stenographer, which has long been challenging in high-volume settings.”

“The units of measurement for kidney size and stone size can sometimes be incorrect when done manually. With ultrasound, assistance from a stenographer is available, but with CT and MRI, everything is done solely by the doctor,” Dr Asha Ouseph, a radiologist, pointed out.

The platform’s intuitive design also allows customisable templates that can adapt to individual or institutional preferences, boosting speed up to 50% and accuracy in report generation.

“Attaining the speed of voice detection with high accuracy was a key challenge, and we’ve been fine-tuning the product since November,” Garza added. “The result is a solution that not only reduces turnaround times but also improves precision.”

With India’s healthtech market projected to reach nearly $60 billion by FY 2028, the launch of Spark.ai aligns with a larger push towards AI-enabled diagnostics. By removing manual bottlenecks and reducing burnout, the platform hopes to equip radiologists to meet rising demands head-on.

Rad AI, Rayscape and Aidoc are platforms that streamline radiology workflows and automate repetitive tasks. At the same time, Spark AI stands out as one of the first solutions developed within India.

“The integration of AI into radiology is more than just an operational improvement; it’s a step towards building a more robust and scalable healthcare ecosystem,” Dr Joel said. “With this platform, diagnostic centres can expand reach, optimise resources, and ultimately deliver better patient care.”

What truly sets SPARK.ai apart is its human-centered approach. Built in collaboration with radiologists, the platform is not only functional but also deeply empathetic to the needs of those using it. Its HUD system, auto-fill capabilities and structured templates help cut through cognitive overload and streamline repetitive tasks.

“Our goal is to ensure radiologists are not bogged down by inefficiencies. Spark.ai integrates seamlessly, grows with institutional demands, and most importantly, centres around the people who use it,” Garza said.

Meanwhile, according to a study published in the Journal of Imaging Informatics in Medicine, large language models (LLMs) could potentially track for interval changes on longitudinal radiology reports. The study suggests that LLMs can effectively identify findings and monitor changes in radiology reports, all while preserving patient privacy by operating securely within an institution’s internal network. This approach would yield time savings by adding automation to a process requiring radiologists to match relevant findings manually.