Latest news with #qXR
Time of India
31-07-2025
- Business
- Time of India
No Radiologist, No Problem: Qure.ai's AI Takes the X-Ray Lead
By Staff When Prashant Warier co-founded nearly a decade ago, artificial intelligence in healthcare was largely experimental. Today, the company's AI algorithms are helping interpret millions of medical images each year — and in some countries, replacing the need for human readers in critical a conversation with ET Studios' Tech Heal moderated by Shilpa Rathnam, Warier, CEO of traced the company's origins to a specific gap in healthcare: chest X-rays — the most common and oldest form of medical imaging — were often not being read by radiologists. In India and other parts of the world, general practitioners or technicians often handled interpretation. In some cases, patients received no report at all. set out to automate that process, training AI models on a large volume of de-identified clinical images sourced through hospital partnerships. The company now works with over 100 healthcare institutions globally and has built a dataset exceeding 1.5 billion images. The company's flagship product, qXR, is a tool designed to detect abnormalities in chest X-rays — including signs of tuberculosis — in under 30 seconds. The tool is currently deployed in screening programs across several countries, including the Philippines, where mobile vans equipped with X-ray machines use qXR to produce real-time readings. Before deployment, such scans could take weeks to be interpreted. In 2021, the World Health Organization formally endorsed solution as an autonomous tool for TB detection, without the need for human review. Warier describes this as a key inflection point for the company. It is also now the most scaled use-case of autonomous AI in healthcare , with the system analyzing between 5 to 10 million X-rays annually. path to adoption has not been without challenges. AI used in clinical pathways is considered a medical device, which means every deployment must go through rigorous regulatory approval. The company spent three years obtaining its first FDA clearance in the U.S. and now holds 18 regulatory approvals, including CE certification in Europe. Each submission typically requires data from the local population and independent comparison against certified radiologists. Warier emphasizes that the role of AI is not to replace physicians but to support them. While automation can handle high-volume, routine imaging, tasks like diagnosis confirmation, treatment planning, and patient communication remain with clinicians. 'AI will be there throughout the patient journey,' he says, 'but it will be an assist.' Looking ahead, Warier expects AI to take over more foundational tasks in radiology — flagging abnormalities, creating template reports, and improving scan-to-diagnosis times. As India's digital health infrastructure evolves — particularly with the introduction of personal health IDs and medical data interoperability — Warier believes the accuracy and value of AI in healthcare will continue to rise. is currently active in 90 countries, with a growing presence in low- and middle-income regions where radiology expertise is scarce. The company's approach, Warier says, remains grounded in utility: 'We started by solving a problem nobody else was looking at.' Disclaimer - The above content is non-editorial, and ET Healthworld hereby disclaims any and all warranties, expressed or implied, relating to it, and does not guarantee, vouch for or necessarily endorse any of the content.
Forbes
22-07-2025
- Health
- Forbes
How AI Powered Med-Tech Can Scale Accessible Healthcare In India
Dr. Ashwini Kumar, Founder, CliniExperts. Helping Global Med-Tech Brands Navigate Indian Healthcare Landscape. In India, every eleven seconds, someone is diagnosed with tuberculosis. Every eight minutes, another case of cervical cancer claims a woman's life. These are more than just numbers; these are people with stories we cannot ignore. But what if technology could hear even what we cannot? Today, artificial intelligence (AI) is not just scanning slides or reviewing scans. It's doing something far more human—it's listening. It's noticing. It's learning from millions of data points and drawing conclusions with breathtaking speed. How AI-Enabled Innovation Is Rewriting The Rules Of Diagnosis Let's look at some of the tools that are already changing how we diagnose, detect and treat these critical health conditions: • Swaasa: A cough analysis tool developed for TB screening, capable of identifying distinct acoustic signatures of respiratory illnesses. It's enabling frontline health workers to screen large populations with nothing but a smartphone—no sputum, no X-ray. • qXR by An AI-driven chest X-ray solution designed to spot signs of TB, pneumonia and lung nodules quickly and reliably. Already deployed in government TB programs and mobile vans across India. • qTrack by A companion system to qXR that allows seamless integration of teleradiology workflows in underserved areas—scaling quality diagnostics to remote districts. • iBEX by Ibex Medical Analytics: A clinical-grade AI platform that supports pathologists in identifying breast cancer with high sensitivity, particularly useful in high-volume, resource-limited environments. • Arogya Arohan: An AI-based app that helps detect early oral cancers via smartphone images—vital for rural and underserved populations where regular screening is a challenge. • CerviSCAN: A visual AI-based cervical cancer screening platform, eliminating the need for complex lab-based pap smear testing in field settings. • TxGNN: An advanced graph neural network used in rare disease drug discovery and repurposing. With 49% higher precision in drug match identification, it's accelerating treatment development for conditions often left behind. • Watson For Oncology by IBM Watson Health: IBM's AI that assists oncologists by reviewing patient records and global literature to recommend personalized treatment plans—bringing world-class cancer expertise to every hospital. • Varian's AI-Enhanced Radiotherapy: AI integrated with imaging to optimize radiation dosage in real time, reducing side effects and improving outcomes. Each one of these tools acts as a second set of eyes, ears and clinical intuition—designed to empower doctors, not bypass them. As a result, AI is pushing 'early detection' into a new paradigm—preemptive healthcare. It's not just about catching diseases early; it's about forecasting risk, personalizing interventions and tracking real-time treatment responses. Whether it's adjusting radiation doses mid-session with Varian systems or designing treatment blueprints with Watson for Oncology, AI is becoming the quiet, indispensable co-pilot in some of medicine's most critical decisions. Where Do We Go From Here? For healthcare and technology leaders, the question is no longer whether to innovate—but how to innovate with intention. The gap between a promising AI prototype and meaningful patient impact is filled by decisions made today in boardrooms, clinics and policy circles. I believe India is uniquely positioned to lead this transformation, set an example for the world to follow suit and improve their healthcare at the same time. They possess a vast, diverse population, deep AI talent and a public health system ready for digital augmentation. But with great speed must come great scrutiny. As the tools evolve, so must the regulatory frameworks, validation mechanisms and ethical guardrails that govern them. Leading The Transformation First, build responsible partnerships. Companies must engage not only data scientists but also clinicians, regulators and public health experts from day one. Co-developing AI solutions alongside end users ensures that the final product is usable, explainable and implementable at ground zero. Second, treat regulatory strategy as an enabler, not a hurdle. Healthcare and tech leaders looking to invest in India's potential must understand India's CDSCO pathways, stay informed on global frameworks like the EU MDR for SaMD and invest early in local clinical validation. Working with experts who understand both innovation and compliance can mean the difference between a pilot that stalls and a product that scales. Finally, never lose sight of patient welfare. AI must serve people, especially the millions who rely on India's public health systems. Leaders should champion solutions that strengthen PHCs, rural diagnostics and community health—where these tools can save the most lives. Putting Patients At The Center If we embed transparency, ethics and real-world training into every stage of development, we will not just build smarter devices—we will build trust in the systems that deliver them. When innovation meets intention, technology becomes more than code. It becomes care. Because the real future of medicine isn't just AI. It's AI, access and accountability. And the entire med-tech industry will reinvent itself when we see innovation meet intention. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Time of India
21-07-2025
- Health
- Time of India
Qure.ai equipped to resist the Grim Reaper? : Qure.ai takes on the Diagnostic gaps with its Deep Learning Model.
How does qXR work? Live Events How does qER work? The qXR was launched in 2017, having been trained on millions of scans and subsequently earning a CE certification (a regulatory approval mark that shows a product meets European Union health and safety standards), WHO endorsement for TB triage, and FDA clearance, breaking ground for AI tools validated by real-world medical as mentioned prior, is an AI-powered chest X-ray interpretation tool curated by It inculcates deep learning to analyze chest radiographs and detect abnormalities such as tuberculosis, pneumonia (not excluding COVID-19), lung nodules, fibrosis, and functions by processing the image first, wherein the model analyzes the X-ray, cleans it, and then standardizes it. It then adjusts the brightness and contrast so that images from different machines are easily comparable. The model then resizes the images and crops them so they focus on the chest region. The AI model also removes artifacts such as text labels, ECG leads, or any noise that may interfere with also uses convolutional neural networks (CNN), a type of deep learning model that is able to automatically identify patterns in the chest X-ray that are linked to the diseases. This process is referred to as 'feature extraction.' CNN learns features such as opacities (the cloudy areas in the lungs), cavities (hollow areas commonly found in TB patients), consolidation (fluid-filled lung sections), and pleural effusion (fluid around the lungs).Upon having these features extracted, the deep learning model interprets if the image shows any sign of a disease. It runs the image through pre-trained classification models to garner a numerically derived result, for example, '92% chance of tuberculosis.'The platform, to make the results easily explainable, overlays a heatmap on the X-ray to show which areas are the most likely to be severely affected and which are not. The heatmap tool used for this purpose is called a 'saliency map.' This visual tool highlights regions that the AI focused on, garnering results that claim 'AI saw pneumonia in this area,' proceeding to paint that area in last step involved in this procedure is having to generate a report. The AI model is enabled to automatically generate a report in the appropriate 2018, Qure fulfilled its ambition to extend into CT-based diagnostics, subsequently introducing qER for urgent cases that involve hemorrhages, strokes, and fractures, scanning and triaging results in under ten seconds, proving itself to be an essential tool when entering the time-critical window for stroke intervention. As COVID-19 struck the world silent, Qure's qXR was used for early detection of pneumonia and viral lung patterns, helping fill the accessibility-related gaps in RT-PCR testing, particularly in resource-limited procedure begins with image acquisition and processing, where a non-contrast head CT scan is captured and the images are standardized using preprocessing, much like how qXR does chest X-rays, which are 2D, CT scans are 3D data volumes wherein multiple image slices are placed atop one another. Therefore, in contrast to qXR, qER uses 3D CNN to process the scan in full. The model then identifies patterns across the multiple layers, aiming to spot signs of bleeding within the brain tissue (intraparenchymal hemorrhage), bleeding between the brain layers (subdural/epidural hematoma), and bleeding in the brain's surface vessels (subarachnoid hemorrhage and cranial fractures).Further, there exists the process of abnormality detection, where each abnormality is scored based on probability, localization (highlighting the exact region in the brain), and urgency (how quickly a radiologist or ER team should be intervening). The abnormal areas, upon being identified, are then highlighted using the previously mentioned saliency maps, giving radiologists visual cues about what the AI saw and qER detects the critical issues and instantly alerts the radiologist or the ER team for momentary its product suite proving clinical value in both routine screenings and emergency care, 's technological strides soon began translating into significant investor interest and global company's financial backing has fueled its expansion and also global outreach. In September 2024, closed a $65 million Series D round to deepen its presence in the United States and advance AI foundation models and acquisitions. As of November 2024, it had raised $125 million, with the company now worth around $264 million in the market, backed by major investors. The company portrays strong growth wherein its revenues are skyrocketing at 60–70% annually, and now serves approximately 15 million patients each year, with around 25% of its revenue coming from the US climbed up the pedestal in the industries and geographies. Today, its solutions are deployed at over 3,100 sites in more than 90 countries, including NHS hospitals in the UK, major centers in the US, mobile X-ray vans in the Philippines, and even equine ambulances in Lesotho, supporting around 25 million people worldwide. Investors believe this global footprint highlights the company's relevance across markets. 'Qure is making quality healthcare accessible in the US and Europe as well as globally in Asia, Africa, and Latin America," added Dev Khare, Partner, startup has also made clinical partnerships with industry giants and public health services, embedding tools into public health programs, screening drives, and pharmaceutical ahead, is working toward profitability in the next financial year, with plans for an IPO within two years, throwing light onto its ambition to bring AI into mainstream healthcare diagnostics. As global healthcare costs rise, diagnostic backlogs grow, and workforce limitations persist, product line featuring qXR, qER, stroke triage tools, and patient management apps is becoming a pivotal offering in modern shift is especially relevant amid widening diagnostic gaps and rising demand for radiology services across both developed and underdeveloped regions. 'AI helps to overcome healthcare bottlenecks, from imaging reporting backlogs to workforce shortages, not only in Western societies like the US and Europe but also in developing nations,' said Prashant Warier, co-founder and CEO of conclude, stands not just as a startup but as a healthcare infrastructure builder: democratizing radiological expertise, enabling early diagnosis of life-threatening diseases, and empowering clinicians worldwide. Its trajectory from AI-powered X-ray and CT tools to near-term IPO ambitions marks it as one of India's most significant contributions to global healthcare innovation.
India Today
01-05-2025
- Health
- India Today
AI spots lung cancer signs in 5 million people, enables early diagnosis
A new technology screened chest X-rays of 5 million people across 20 countries and successfully detected early signs of lung cancer using artificial AI algorithm, developed through a collaboration between AstraZeneca and Indian health-tech company analyed scans conducted in Asia, the Middle East, Africa, and Latin America - regions where access to advanced diagnostic tools is often a global biopharmaceutical company headquartered in the UK, is known for developing life-saving treatments in oncology, cardiovascular, and respiratory diseases. It partnered with to deploy AI tools in resource-constrained settings, making early cancer detection more accessible. The screening program used proprietary algorithm, qXR, which analysed chest X-rays to identify abnormalities, particularly high-risk lung nodules that could indicate early-stage AI then flagged these cases for follow-up diagnostic tests like CT scans. So far, nearly 50,000 people with suspected nodules have been referred for further testing, enabling earlier diagnosis and faster intervention."Reaching the five million scan milestone demonstrates the power of digital innovation in transforming cancer care. AI-enabled tools like qXR are proving to be a cost-effective way to screen for lung cancer where radiologists or advanced imaging facilities may not be readily available," said Ti Hwei How, Vice President of International Oncology at CEO Prashant Warier noted that this collaboration has helped scale their AI technology in real-world clinical environments. "This milestone shows how AI can bridge the gap in healthcare access and strengthen systems through faster diagnosis," he cancer remains the leading cause of cancer-related deaths worldwide, particularly in low- and middle-income countries. In these regions, where screening programs are often lacking, technology like qXR offers a promising way forward.A recent study presented at the European Lung Cancer Congress 2025 found that qXR successfully identified high-risk nodules in 54.1% of cases, highlighting its utility as a frontline screening this, AstraZeneca and are now working with local governments and health agencies to expand the reach of AI-powered screening and bring timely lung cancer diagnosis to even more underserved Watch
