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AI biomarkers, built for real-world impact and clinical trials. Meet us at ASCO 2026 | Booth #IH12.

Qure.ai Recognized Among TIME’s 100 Most Influential Companies of 2025

Qure.ai partners with Microsoft to increase access to its lung cancer detection and management suite in the U.S.

qTrack

AI-based chest X-ray (CXR) screening for tuberculosis (TB) is gaining traction but faces significant real-world challenges in implementation, such as software setup, integration into workflows, network connectivity issues, and a shortage of healthcare personnel. To tackle these issues, PATH initiated a TB screening program in Nagpur, India, utilizing qXR, an AI software tool, across eight private CXR labs that met specific technical prerequisites. The program revealed valuable insights into the operational feasibility of AI deployment in resource-limited settings, highlighting the strategies needed to overcome logistical hurdles.

Throughout the <a href="https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0000404">program</a>, 10,481 individuals were screened based on clinical history, resulting in 2,303 flagged as presumptive TB cases by either AI or radiologists. Thanks to AI, there was a 15.8% increase in TB case detection compared to traditional screening methods alone. This increase underscored the effectiveness of AI as a complementary diagnostic tool, especially in settings with limited healthcare resources.

TB continues to be a major public health issue in India, which reports nearly a third of the world’s TB cases. With India’s goal to eliminate TB by 2025, innovative approaches to diagnostics and patient care are crucial. Although CXRs are a recommended screening tool in India, limitations in resources—such as a shortage of radiologists—make consistent implementation difficult. AI solutions like qXR from Qure.ai, designed to assist in identifying TB signs in CXR images, offer promise but must overcome practical barriers in integration and use in diverse healthcare settings.

The Nagpur screening program encountered several technical and operational challenges that were addressed through strategic adaptations. Basic infrastructure, including internet access and compatible systems, proved critical for smooth AI software installation and operation. Initial reluctance from local radiology associations, driven by unfamiliarity with AI, was overcome through educational sessions, transparency, and consistent engagement with stakeholders, which ultimately built trust and acceptance of the new technology. Additionally, flexible technical setups allowed labs to address privacy concerns while accommodating AI functionality. By working closely with local partners, PATH was able to implement qXR in a way that respected the needs and limitations of each lab, leading to improved screening outcomes.

Overall, the increased TB detection rate achieved through the use of AI screening underscores the potential of AI-based tools to transform TB screening in India and other high-burden areas. However, broader deployment of these tools will require carefully designed solutions to address the real-world challenges of workflow integration, infrastructure compatibility, and stakeholder acceptance. This experience shows that with the right approach, AI can be a powerful asset in advancing TB care, particularly in resource-constrained environments.

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Introducing AI Toolkit: Simplifying AI deployments for TB Programs

<a href="https://blog.vrad.com/in-the-wild-mednax-and-qure.ai-partnership-guiding-ai-advancements">vRad</a>, a leading teleradiology provider in the U.S., has teamed up with Qure.ai to integrate AI-driven solutions in stroke care. This collaboration addresses the urgency required in treating over 795,000 stroke patients annually in the U.S., where each minute is crucial.

A stroke claims a life every 3.5 minutes, so an immediate medical response is crucial. The healthcare industry grapples with challenges such as limited capacity and increased demand for imaging, making quick and accurate decisions imperative.

Qure.ai's AI tool, <a href="https://www.qure.ai/product/qer">qER</a>, seamlessly integrates into vRad's workflows. Certified by FDA 510(k) and CE Class IIb, the tool interprets head CT scans, allowing for expedited and confident clinical decisions.

In a recent case at a rural Iowa medical center, a 68-year-old female was diagnosed and treated promptly due to AI prioritization, significantly improving her condition.

The vRad and Qure.ai partnership exemplifies how AI can revolutionize time-sensitive and critical healthcare sectors like stroke care. For more in-depth insights and metrics, download the complete case study.

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Elevating Radiology Care with Qure.ai & vRAD

No country aspires to be known for a high tuberculosis infection rate. But, unfortunately, Nigeria’s tuberculosis numbers are soaring rapidly, ranking third in the world, only behind India and China. Annually, the West African nation sees 590,000 new tuberculosis cases, and 245,000 Nigerians die of the disease every year.&nbsp;&nbsp;&nbsp;

Tuberculosis places an enormous burden on Nigeria’s public health system. Recognizing this, the National Tuberculosis and Leprosy Control Programme (NTBLCP) was set up in 1989. Multiple strategies have been implemented at the community and facility levels in public and private healthcare units. However, the system has yet to overcome the hurdles of low population coverage, lack of accessibility, inadequate resources, and delay in treatment.&nbsp;&nbsp;&nbsp;

Qure.ai, Fujifilm, and Omnia Health came together to organize a webinar on&nbsp;“Accelerating TB screening in rural communities using AI-powered technology."&nbsp;The aim was to capture how the three organizations had come together to use technology for Active case finding and how they are making a difference in the lives of people in rural Africa.&nbsp;&nbsp;&nbsp;

The distinguished panel of global speakers shared their experiences deploying mobile health tech and AI in parts of rural Nigeria struggling with little healthcare access.&nbsp;&nbsp;

The recent partnership between the Institute of Human Virology in Nigeria (IHVN), Fujifilm Healthcare Middle East &amp; Africa, and Qure.ai has been an innovative game-changer in the fight against tuberculosis.&nbsp;&nbsp;

Dr. Ronke Vivian Agbaje explained the problem –&nbsp;“In the past, our efficiency targets were not being met. We had partners who have worked with AI, and it has worked for them. So, we got four portable X-ray machines integrated AI. It improved our efficiency. We can now target our screenings better, and access has improved. We have also been able to detect other lung health issues in our patients, apart from TB."&nbsp;

According to Dr. Shibu Vijayan of Qure.ai- “The transmission chain is being cut via the usage of AI. Partnerships like this are critical to achieving the Global TB Target. Post-pandemic, technology is bridging the gap, and this partnership will ensure we achieve it humanely and cost-effectively. The efficiency brought in by AI for time, resources, and the cost is unmatched by other strategies implemented in the past."&nbsp;qXR's prowess lies in its ability to detect and identify 30 distinct findings in a chest X-ray and monitor the rate of disease progression. In addition, it has been CE-certified and FDA-approved, and independent studies by the World Health Organization (WHO) have validated Qure's solutions.&nbsp;&nbsp;&nbsp;

This partnership undoubtedly brings excellent value to Nigeria’s public health setup. Leveraging the portability of the handheld digital X-ray machine can bring the health system to the patient, who can then be diagnosed at the point of care. This eases the patient's mobility and connects them to the treatment pathway faster. In addition, mass testing and active screening expedite tuberculosis detection, treatment, and notification.&nbsp;

Within Nigeria, the Xair device embedded with the qXR AI solution has been deployed in twelve states, covering rural areas and vulnerable communities. Dr.&nbsp;Bethrand&nbsp;Odume said,&nbsp;“The resources are minimal. We are using this to do screenings in prisons. We can take it to the doorsteps of patients, and that plays a big role in bridging the gap in healthcare."&nbsp;

Besides providing accurate diagnosis of chest abnormalities, Qure’s AI solution reduces the disease’s reporting turnaround time to less than one minute. This assists radiologists in reducing their workload and gives them room to connect to other radiologists for discussion or review. Moreover, qXR minimizes the need for confirmatory tests like CBNAAT or sputum tests.&nbsp;&nbsp;&nbsp;

One of the reasons for this efficiency is the opportunity for early diagnosis that AI screening enables. Before disease manifestation, the algorithm can pick up on abnormalities and reduce tuberculosis’s communicability and transmission. Furthermore, by carefully mapping the hotspot areas within the country, they can do targeted screening within the population and increase coverage. As simple as it may sound, this creates a revolutionary push to improve the overall lung health of people.&nbsp;&nbsp;&nbsp;

“There is a huge gap around trained manpower across the world. We are still fighting against TB. Africa's Fujifilm and Qure.ai association is a good example of how a large corporation and a startup can make a difference. A product innovator can join hands with a corporate that now wants to spend a lot of time on innovation. And it makes a big synergetic impact. It is impactful in private healthcare as well,"&nbsp;said Naresh Hasija.&nbsp;&nbsp;&nbsp;

“It has been phenomenal to see how much impact AI can make. Fujifilm can provide a portable x-ray device embedded with this AI. Qure.ai is a peer-reviewed solution. We are now able to see this partnership evolve. It is being applied in rural communities, and the plan is to increase access and proximity to these devices. Being able to provide more devices to more people will be our future,"&nbsp;shared Godspower Asika.&nbsp;&nbsp;&nbsp;

At the root of every public health strategy implementation is the drive to make the system efficient, patient-oriented, and accessible without compromising quality. A competent patient management system integrated with innovative solutions can significantly accelerate the population's health goals. Qure.ai's partnership with Fujifilm and IHVN is a testament to these aims, making a significant difference in the fight against the tuberculosis epidemic.&nbsp;

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Accelerating TB screening in rural communities using AI-powered technology

When it comes to stroke and traumatic brain injuries (TBI), experts say “time is brain" because avoiding treatment delays can mean the difference between life &amp; death or permanent paralysis for patients. This urgency increases the burden on radiologists.

However, powerful AI solutions like Qure's&nbsp;<a href="https://www.qure.ai/product/qer/" target="_blank">qER</a>&nbsp;assist in faster detection, risk reduction, and subsequent improvement in the quality of life.

<a href="https://aarthiscan.com/" target="_blank">Aarthi Scans and Labs</a>&nbsp;is one of India largest integrated diagnostic chains with imaging centers across India including in Tier 3 and Tier 2 cities. The organization has adopted Artificial Intelligence (AI) in their Tele-radiology reporting. Tier 3 cities usually lack neurologists in hospitals and any trauma cases need to travel for a couple of hours even to reach a tertiary hospital, which is difficult at night. The main aim of Aarthi Scans is to implement AI in their service for the night reads of Non-Contrast Head CTs (NCCT), in absence of a Neurologist or a Radiologist to aid the treatment faster for time sensitive and emergency trauma cases.

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Impact of AI in treating trauma and stroke patients

The Philippines has long been at the forefront of the fight against tuberculosis (TB), with the Philippine Business for Social Progress (PBSP) playing a key role in TB control management for over two decades. The Advancing Client-centered Care and Expanding Sustainable Services for TB (ACCESS TB) project, funded by The Global Fund to Fight Aids, Tuberculosis, and Malaria, aims to further reduce TB prevalence, incidence, and mortality by providing comprehensive quality TB care to Filipinos.

A crucial component of the ACCESS TB project was the deployment of mobile vans equipped with X-ray machines and AI-enabled software for the interpretation of chest radiographs. These vans, manned by trained radiographers and health workers, were intended to screen presumptive TB cases and individuals with high-risk factors by running them across different cities in the Philippines.

The ACCESS TB project has gone beyond just screening and diagnosis. It has also provided diagnostics and treatment services for both drug-susceptible and multidrug-resistant TB (MDR-TB) cases, expanding access to hundreds of MDR-TB diagnostic and treatment centers. The project has also engaged thousands of private doctors, trained thousands of healthcare providers, and cured thousands of patients through its comprehensive approach.

When the COVID-19 pandemic hit the Philippines, the National TB Control Program (NTP) had to act quickly to ensure that TB patients continued to access their lifesaving treatment. The ACCESS TB project, along with the NTP, WHO, USAID, and other partners, developed the National TB Adaptive Plan to maintain TB prevention, testing, and treatment even during the lockdowns.

One of the key innovations was the implementation of home-based treatment for drug-resistant TB (DR-TB) patients. Prior to COVID-19, these patients had to report to a facility every day to take their medications, leading to a high rate of loss to follow-up. With the adaptive plan, patients were allowed to take a month's worth of medications home, and healthcare workers conducted regular home visits to monitor their progress and collect sputum samples for laboratory follow-ups.

This shift to home-based treatment had a significant impact, reducing the loss to follow-up rate from 16% before COVID-19 to only 4% in 2020. The pandemic has accelerated the adoption of these patient-centric measures, which have proven to be effective in improving treatment adherence and outcomes.

The ACCESS TB project also expanded the role of its Specimen Transport Riders (STRiders) – motorcycle riders who not only transport sputum specimens to laboratories but also deliver medications from facilities to patients' homes. This integrated service has benefited both TB and HIV programs.

With the support of the Global Fund's COVID-19 Response Mechanism (C19RM) funding, the ACCESS TB project is now scaling up digital adherence tools, such as video observed therapy (VOT) and digital pill boxes, to further enhance patient support and monitoring. Additionally, the project is expanding the reach of mobile clinics that can perform on-site TB testing and screening, including at COVID-19 testing and vaccination sites, to ensure that no one is left behind in the fight against TB.

The Philippines' proactive response and the collaborative efforts of the NTP, PBSP, and their partners have demonstrated the resilience and adaptability of the country's TB control program in the face of the COVID-19 pandemic. By prioritizing patient-centered care and leveraging innovative technologies, the ACCESS TB project is making significant strides in improving TB outcomes and paving the way for a healthier future for the Filipino people.

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Tuberculosis Crisis in Philippines: The Mobile Radiology Answer

Lung cancer remains one of the most pressing challenges in modern healthcare, being the leading cause of cancer mortality in the UK with approximately 47,000 new diagnoses each year. What's particularly concerning is that over a third of these patients are diagnosed only after presenting as emergency cases - often at late stages when treatment options are limited. Dr. Indrajeet Das, a consultant radiologist at the University of Leicester Hospital, recently shared insights into how artificial intelligence could transform lung cancer pathways and potentially save lives through earlier detection.

The current situation is sobering: patients presenting as emergencies are five times more likely to die within a year compared to those referred through primary care. This stark difference underscores the critical importance of early diagnosis. However, several challenges stand in the way of improving detection rates.

The UK faces a significant shortage of radiologists, with one of the lowest numbers of specialists per 100,000 people in the European Union. This shortage has led many hospitals, including Leicester, to outsource up to 60% of their X-ray reporting. While outsourcing helps manage workflow, ideally hospitals would prefer to keep abnormal cases in-house for faster response and better continuity of care.

This is where AI enters the picture. Dr. Das and his team have been exploring how AI tools could help triage chest X-rays, distinguishing between normal and abnormal cases to ensure the most critical cases receive immediate attention. However, Dr. Das emphasizes that AI alone isn't enough - it needs to be integrated into a comprehensive, streamlined pathway for patient care.

The Leicester team developed an innovative "straight to CT" pathway, where chest X-rays showing suspected lung cancer are fast-tracked. Patients receive early morning CT scans that are immediately reported, allowing for rapid assessment and intervention if needed. This approach has already shown promising results, with a 25% relative reduction in emergency presentations despite an increase in overall lung cancer diagnoses.

To validate the potential of AI in this pathway, Dr. Das's team partnered with Qure.ai, conducting a retrospective analysis of 1,448 GP-referred X-rays. The results were impressive: the AI system showed excellent ability to detect abnormalities and correctly identified eight out of ten confirmed lung cancer cases. While it missed two subtle cases, the system's performance suggested it could be valuable as a prioritization tool.

The success of this initial validation has led to a larger multi-center study funded by SBRI (Small Business Research Initiative), involving several leading UK hospitals. The goal is to achieve the ambitious target of reporting all routine or urgent chest X-rays within 24 hours, potentially revolutionizing the speed of lung cancer diagnosis.

What makes this approach particularly promising is its holistic nature. Rather than simply deploying AI as a standalone solution, the Leicester team has focused on integrating it into a comprehensive pathway that includes rapid CT scanning, expert reporting, and prompt clinical assessment. This integrated approach addresses not just the initial detection but the entire patient journey.

While some radiologists remain skeptical about AI's role in healthcare, the early results from Leicester suggest that when properly implemented as part of a well-designed pathway, AI tools can help address the critical challenges of radiologist shortages while potentially improving patient outcomes through earlier detection and treatment of lung cancer.

As we await the results of larger prospective trials, one thing is clear: the future of lung cancer detection likely lies in the thoughtful integration of AI tools with streamlined clinical pathways, supported by dedicated healthcare professionals working together to catch cancer earlier and save more lives.

Channel Partners: GE Healthcare, Magentus

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Determining the Value of AI in Lung Cancer Pathways | Dr Indrajeet Das, Leicester NHS Trust

In Baran, Rajasthan, the fight against tuberculosis (TB) has seen notable advancements thanks to AI-driven solutions in medical diagnostics. Tuberculosis remains a widespread health challenge in India, particularly in rural areas where access to healthcare is limited. At Baran District Hospital, Qure.ai’s AI-powered chest X-ray tool, qXR, has played a transformative role since its deployment in 2018, enhancing TB detection through rapid and reliable image analysis. This technology supports the hospital’s overburdened healthcare staff, allowing them to triage patients more efficiently while addressing a large volume of cases.

For many patients traveling from remote villages, delayed or missed diagnoses pose a significant risk, often leading to untreated individuals unknowingly spreading TB within their communities. By integrating qXR into the hospital’s daily workflow, the AI system serves as a triage tool, identifying TB-suspected cases in minutes and promptly directing them for further testing. This reduces patient dropouts and speeds up the overall diagnostic process, providing a more seamless route from diagnosis to treatment.

The results at Baran District are impressive. TB notification rates rose from 67.8% to 90.14% within months of qXR’s introduction, a substantial increase attributed to the tool’s accuracy and speed in identifying cases, including asymptomatic ones that might otherwise be overlooked. Treatment enrollment time decreased by nearly three days, and the dropout rate for presumptive cases fell significantly. These improvements have empowered Baran District’s healthcare providers, enabling them to deliver timely care while reducing the risk of further TB transmission.

With AI support, Baran District Hospital has optimized its TB screening and treatment process, showcasing how innovative technology can amplify healthcare access and improve outcomes in resource-limited settings. The collaboration between Qure.ai and Baran's medical team highlights a scalable model for rural and under-resourced hospitals tackling TB, ultimately underscoring the potential for AI to impact global health challenges positively.

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Streamlining TB Diagnostics in Baran, Rajasthan (India)

Fujifilm and Qure.ai have partnered with Nigeria’s Institute of Human Virology (IHVN) to improve tuberculosis (TB) screening in rural communities through advanced technology. This initiative, supported by USAID, involves deploying Fujifilm’s portable, battery-powered Xair X-ray machines with Qure.ai’s AI-based imaging software to identify TB cases in hard-to-reach areas.

In Sub-Saharan Africa, including Nigeria, TB is a leading cause of death, often co-occurring with HIV. The Xair device and Qure.ai’s AI algorithms offer an efficient solution for TB testing in remote locations by providing portable, accurate, and rapid diagnostics. This allows healthcare teams to screen more individuals and detect TB earlier, even in places with limited electricity or infrastructure.

Dr. Chukwuma Anyaike from Nigeria’s Federal Ministry of Health lauded the success of this technology-driven approach, which has already proven effective in piloted areas. Dr. Aderonke Agbaje from IHVN highlighted that this partnership brings vital healthcare access to communities with challenging terrain, marking a significant advancement in their ongoing TB outreach.

Over the next five years, USAID and IHVN aim to scale up TB detection and treatment across Nigeria. This project not only improves patient access to care but also incorporates contact tracing for TB cases, helping reduce transmission and strengthen Nigeria’s national TB reporting.

Godspower Asika of Fujifilm emphasized Fujifilm’s commitment to making its technology accessible in Nigeria, with its Xair device enabling continuous screening without electricity. Qure.ai’s CEO Prashant Warier expressed enthusiasm for using AI to expand healthcare access, noting that Qure’s software can assess chest X-rays within a minute, identifying TB and other lung diseases accurately.

This partnership between IHVN, Fujifilm, and Qure.ai aims to enhance TB detection, treatment, and reporting in rural Nigeria, reducing transmission and supporting innovation in TB care. Together, these organizations are working to bring life-saving diagnostics and care to underserved communities, advancing universal healthcare access in Nigeria.

(<a href="https://www.youtube.com/watch?v=3KClgtX3vgY">VIDEO LINK</a>)&nbsp;

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On Ground with IHVN and Fujifilm in Nigeria

The impact of AI at The Royal Bolton NHS Hospital, UK&nbsp;

Royal Bolton Hospital, part of Bolton NHS Foundation Trust, was the first NHS trust to adopt radiology AI, deploying Qure’s qXR solution in April 2020.&nbsp;&nbsp;

It was the height of the COVID-19 pandemic with the hospital experiencing a heavy footfall of symptomatic patients. qXR was used by both clinicians and radiologists for the reporting of chest X-rays as a second read to ensure no potential cases were missed and to support clinical decision-making for better patient outcomes.&nbsp;

The Royal Bolton Hospital in Farnworth, Greater Manchester is home to Bolton NHS Foundation Trust, providing healthcare services for people in the Metropolitan Borough of Bolton and the surrounding areas. The Trust was the first in the UK to deploy qXR, an imaging interpretation tool developed by Qure.ai, to help medical staff effectively monitor the extent and progression rate of COVID-19 in patients through the automated analysis of chest X-rays.&nbsp;

The initial months of 2020 saw an alarming spike of COVID-19 cases in the UK. It resulted in an immediate impact on the NHS, medical and social care systems as they faced the challenge of ensuring adequate testing. Patients suffering from the illness placed unprecedented demands on acute care services, particularly on intensive care units (ICUs) and the already burdened medical workforce. This resulted in a substantial rise in mortality rates.&nbsp;

As in other parts of the UK, Bolton faced an influx of COVID-19 patients and needed a system in place to improve efficiency in prognosis and alleviate the workload of the hospital staff.&nbsp;&nbsp;

COVID-19 clinically presents itself as consolidation, which are accumulations of fluid and/or tissue in pulmonary alveoli preventing gas exchange or ground-glass opacity, and through nodular shadowing. It primarily affects peripheral and lower areas of the lungs.&nbsp;

Qure’s deep learning-based automated chest X-ray interpretation platform – qXR – can detect, localise, and quantify a total of 29 findings, including COVID-19 related lesions. It had been trained and tested using a growing database of X-rays from diverse sources.&nbsp;&nbsp;

The solution gave 91% sensitivity and 77% specificity in predicting COVID-19 changes on chest X-rays. qXR could also monitor the extent and rate of progression of the infection, creating graphs showing the percentage area of lung affected and tracking changes on subsequent X-rays.&nbsp;

During the peaks of the pandemic, when PCR testing turnaround time was more than 24 hours, NHS clinicians relied on qXR to interpret chest X-rays reviews for the rapid assessment and triage patients into high, medium and low priority categories.&nbsp;&nbsp;

qXR gave almost instantaneous feedback to clinicians, providing out-of-hours clinical decision support and assisting accurate evaluation by clinicians with less expertise in chest X-ray reviews. By providing workflow support to improve efficiency and increase reporting capacity, qXR can also helped mitigate any shortage of trained radiologists.&nbsp;

Bolton NHS Foundation Trust had been exploring the use and potential benefits of AI in support of medical diagnoses and treatments for a considerable amount of time. However, the COVID-19 pandemic accelerated this process and resulted in the collaboration with Qure.ai.&nbsp;&nbsp;

Deploying qXR in the field of radiography helped the hospital staff manage the diagnosis and treatment of patients and also established the hospital’s reputation as a leader and pioneer in the development of the use of AI as part of clinical workflow.&nbsp;&nbsp;

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The impact of AI at The Royal Bolton NHS Hospital, UK

Haris Shuaib, Head of Clinical Scientific Computing at Guy’s and St Thomas’ NHS Foundation Trust hopes so. In a freewheeling conversation with Reena Rajan, Head of Marketing at Qure.ai, they touch upon facilitating technology into the healthcare workflow and how a boring walk triggered the London Medical Imaging &amp; Artificial Intelligence Centre for Value Based Healthcare’s AIDE initiative that’s currently being used for Stroke care and Neonatal care. Haris also discusses how the partnership with Qure.ai is a way to demonstrate how AI solutions can have meaningful impact in hospital ops and patient outcomes and the endless potential applications of AI in healthcare.

A boring hospital corridor walk sparked what would become a groundbreaking healthcare AI initiative. In an engaging conversation with Reena Rajan of Qure.ai, Haris Shuaib, Head of Clinical Scientific Computing at Guy's and St Thomas' NHS Foundation Trust, reveals how his reluctance to walk between two connected hospitals led to transformative change in healthcare technology.

Five years ago, as a trainee seeking a way to transfer scanner data for analysis, Shuaib wrote a simple automation program. This seemingly modest solution caught his colleagues' attention, who saw potential for their own analytical needs. That small efficiency tool evolved into AIDE, now a major Office of Life Sciences-funded program spanning 10 teaching hospitals across London and the southeast. Built on free, open-source technology, this platform has become a comprehensive solution for integrating AI into clinical workflows securely and efficiently.

As Transformation Lead for the London AI Centre, Shuaib's work extends far beyond the original automation tool. His team tackles the complex challenge of translating cutting-edge technology into practical healthcare solutions. This involves comprehensive staff training, developing robust policies that align with national regulations, and ensuring new technologies enhance rather than disrupt existing care pathways. The goal is seamless integration that benefits both healthcare providers and patients.

The partnership with Qure.ai, established through the NHS XAI award, represents a crucial step forward in demonstrating AI's real-world impact. While AI solutions have shown impressive results in academic papers - including Qure.ai's publications in The Lancet - Shuaib emphasizes the need to prove their practical value to commissioners. The focus now is on demonstrating tangible improvements in hospital operations and patient outcomes, moving beyond technical validation to meaningful implementation at scale.

The applications already in place show promising diversity. At King's College Hospital, AI assists with stroke care, while at Guy's and St Thomas', the focus includes neonatal care applications. But this is just the beginning. Shuaib envisions AI applications expanding into numerous areas: early detection of brain tumors, tumor differentiation, addressing COVID-19 backlogs, optimizing surgery times, and managing waiting lists. The potential seems boundless, though he cautions that meaningful healthcare transformation takes time and requires close collaboration between technology providers and medical professionals.

Perhaps most intriguingly, Shuaib's ultimate vision for AI in healthcare is surprisingly modest: he wants it to become as unremarkable as an MRI machine. Despite the quantum mechanics behind magnetic resonance imaging being revolutionary, MRI machines are now simply part of routine healthcare - reliable, trusted, and unexciting to all except the technologists who truly understand their complexity. 'Only the technologists should know how cool it really is,' he says, looking forward to the day when AI in healthcare becomes wonderfully ordinary - a standard tool that quietly saves lives and improves care without fanfare.

This vision of normalized excellence represents a mature approach to healthcare technology - where innovation is measured not by its novelty, but by its reliable service to patient care. Through initiatives like AIDE and partnerships with companies like Qure.ai, Shuaib and his team are steadily working toward this future, one automated process at a time. (<a href="https://www.youtube.com/watch?v=hCzRJcxuuAQ">LINK TO FULL CHAT</a>)

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Can the use of AI become as conventional as that of MRI machines?

Samantha Davey and David Evans from Medica Group recently shared insights into Medica’s integration of AI technology into their teleradiology workflows. Medica, one of the largest teleradiology companies in the UK, provides critical diagnostic support to over 100 NHS hospitals. With over 500 radiologists and operations spanning the UK, Europe, and beyond, Medica has steadily grown from its early days in the town of Battle to a publicly listed company, now based in Hastings. Today, Medica is widely known for both its elective reporting services, which offer flexible 24- to 48-hour turnaround times, and its 24/7 emergency service, NightHawk, which guarantees rapid reporting within 23 minutes.

David, Medica’s Functional Architect, described the company's evolution and dedication to innovation. Having worked at Medica since 2008, David played a key role in integrating Qure.ai’s QER AI technology into Medica's emergency NightHawk portal, a system designed to support acute care by prioritizing urgent cases like intracranial hemorrhage detection. Medica's decision to adopt Qure.ai’s QER technology was driven by a commitment to pioneering healthcare technology that benefits both clinicians and patients, with a specific focus on improving speed and accuracy in acute settings. 

The collaboration with Qure.ai began after careful planning and pilot testing. Samantha, Medica’s Head of Program Delivery, noted that the process included feedback from Medica’s radiologists, which helped guide the integration. She explained that Medica’s priority was to ensure QER’s impact on workflow efficiency, particularly by allowing radiologists to prioritize critical cases more quickly. This integration with NightHawk enables the Medica team to identify high-priority cases like potential hemorrhages flagged by QER, allowing immediate action and faster decision-making for frontline healthcare professionals.

For organizations considering teleradiology partnerships, Samantha and David stressed the importance of selecting a clinically led provider with robust governance. Medica’s approach is to act as a strategic partner rather than simply a service provider, aiming to supplement a hospital’s existing radiology resources. This focus on clinical oversight, supported by Medica’s in-house technology team, underscores the company’s commitment to patient outcomes. Medica's model provides radiologists with full access to a patient's medical history and prior imaging, creating a seamless experience that aligns closely with in-house clinical practices.

NightHawk’s emergency reporting service epitomizes Medica’s dedication to rapid, clinically governed diagnostics. Each night, NightHawk’s radiologists manage high volumes of cases, providing consultative support to emergency doctors and advising on the necessity of imaging when appropriate. In situations where urgent findings are detected, radiologists notify clinicians directly, ensuring that critical cases receive immediate attention. Integrating QER into NightHawk's portal has enhanced Medica's ability to prioritize these cases even more effectively, as the AI flags potential issues in real time. This allows Medica to deliver on its commitment to high-speed, accurate reporting, even with substantial data transmission requirements across wide networks.

Looking forward, both David and Samantha highlighted that Medica’s partnership with Qure.ai continues to evolve as they fine-tune their AI integrations to optimize response times. By innovating with AI and maintaining a clinician-centered approach, Medica reinforces its role as a leading teleradiology partner for hospitals across the UK and beyond.

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How qER helps UK’s largest Telerad firm Medica Group Plc

The COVID-19 pandemic has impacted everyone in some way but people with prior maladies or those who developed some amidst this pandemic have had to endure the most of it. A reduced focus on the incidence, prevalence, and spread of TB has drastically the screening of TB.

The TB prevalence survey conducted by the South African Medical Research Council, Human Sciences Research Council, and National Institute for Communicable Diseases on behalf of the Department of Health of South Africa has given a disturbing result that there are a higher number of people with TB in South Africa than previously estimated. More people need to be diagnosed, which can be done by testing more people who are at a higher risk of contracting TB. This is effective but expensive as well in the traditional manner. That is why the South African National Department of Health has joined hands with Qure.ai and&nbsp;<a href="https://youtu.be/6n76sNTOJWE">LTE Medical Solutions</a>&nbsp;to leverage innovative technology and screen TB in high-risk communities aiding in early diagnosis of the disease.

LTE Medical Solutions provided an integrated TB screening and diagnosis solution including mobile medical clinics, imaging equipment, Qure qXR AI and Healthcare Framework TB software. Qure's qXR AI software can detect signs of TB in less than 1 minute and patients with a high TB score can then be directed to undergo confirmatory tests.

Since the beginning of the COVID-19 induced lockdown in South Africa, testing rates for TB have dropped by up to 50% and technologies such as qXR CAD AI can significantly improve TB screening in community and primary healthcare settings which can aid in reducing the workload of the healthcare workforce amidst this pandemic.

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AI makes TB screening more accessible in South Africa

Dr. Indrajeet Das, a consultant radiologist at the University Hospitals of Leicester NHS Trust, recently shared his insights on the transformative role of artificial intelligence (AI) in radiology and patient care. As part of one of the UK’s largest acute healthcare providers, his work covers a wide range of areas, including respiratory and cardiac imaging, serving a population nearing one million across Leicester and Rutland. Leicester’s NHS Trust also functions as a key referral and research center, especially in cancer and cardiovascular services, which puts Dr. Das at the forefront of integrating new technologies like AI in clinical practice.

Dr. Das discussed how AI is shaping a new era in radiology, especially with tools like Qure.ai’s qXR, an AI application designed to interpret chest X-rays for various conditions, including lung cancer. He described AI’s progression as “an exciting revolution,” and while the technology is still in its early stages in Leicester, there is great promise. Dr. Das emphasized the importance of using AI in clinical trials that offer real-world evidence, explaining how collaboration with technology partners like Qure.ai helps refine AI’s effectiveness in clinical settings.

One of the critical initiatives discussed was the National Lung Cancer Optimal Pathway, which aims to significantly reduce the time between diagnosis and treatment of lung cancer. Dr. Das pointed out that early detection can often make the difference in outcomes, particularly in a disease with notoriously late-stage symptoms. The pathway initiative seeks to cut diagnosis times from an average of 62 days to under 49 days. To achieve this, the pathway recommends that chest X-rays suspected of showing lung cancer be reviewed within 24 hours, followed by a CT scan within 72 hours. Dr. Das suggested that implementing AI in radiology could help meet these tight deadlines, accelerating the diagnostic process and improving outcomes.

Reflecting on his early experience with qXR, Dr. Das mentioned a retrospective study of around 1,000 chest X-rays, where qXR demonstrated high specificity and sensitivity. The AI tool quickly flagged eight out of ten confirmed lung cancer cases, pinpointing their location accurately and performing faster than traditional reporting methods. He noted that had qXR been used in real-time, it could have expedited reporting and potentially led to quicker intervention.

Dr. Das is optimistic about the future of AI in the NHS, not only for cancer detection but also for conditions like tuberculosis, pneumothorax, and trauma injuries. He believes that AI can play a critical role in flagging urgent cases, helping radiologists prioritize cases when resources are stretched. This prioritization could improve patient outcomes by ensuring those in most critical need are attended to sooner.

For colleagues hesitant about AI, Dr. Das reassured that AI isn’t intended to replace radiologists but rather to support them, much like a car’s lane-keeping feature prevents accidents without taking over the driver’s role. He sees AI as a safety net that enhances diagnostic accuracy and efficiency, supporting radiologists by catching potential issues and allowing them to focus more effectively on complex cases. He is hopeful that with adequate governance, data protection, and research, AI could evolve into a new standard of care, offering a transformative tool for the healthcare system.

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AI could become the new standard of care for patients

In an enlightening discussion, Kingshuk Misra, Public Health Business Development Lead at Qure.ai, and Matsie Lerefolo, Clinical Manager at LTE Medical Solutions, delve into the transformative impact of Qure.ai's solutions in driving Universal Lung Health. The conversation highlights the critical role of innovative technologies and strategic partnerships in tackling tuberculosis (TB) and other lung diseases.

Qure.ai's commitment to Universal Lung Health is exemplified through their integrated chest X-ray solution, qXR, which is specifically designed for tuberculosis detection. Misra and Lerefolo explore the features and advantages of qXR, emphasizing its potential to revolutionize TB diagnosis and management. By leveraging artificial intelligence (AI) algorithms trained on vast datasets of chest X-rays, qXR enables rapid and accurate identification of TB-related abnormalities, even in resource-limited settings.

One of the key advantages of qXR is its ability to streamline the diagnostic process, reducing the time from initial screening to definitive diagnosis. The AI-powered solution assists healthcare professionals in prioritizing cases that require immediate attention, ensuring timely intervention and treatment initiation. Moreover, qXR's intuitive interface and seamless integration into existing workflows make it user-friendly and accessible to healthcare providers at various levels of expertise.

Misra and Lerefolo also emphasize the crucial role of mobile solutions in extending the reach of lung health initiatives, particularly in community outreach programs. By bringing screening and diagnostic capabilities directly to underserved communities, mobile solutions enable early detection and timely management of TB and other lung diseases. The discussion highlights the importance of accessible and portable technologies in bridging the gap between healthcare services and those who need them most.

Throughout the conversation, the power of partnerships emerges as a central theme in tackling TB and other lung diseases. Misra and Lerefolo underscore the significance of collaboration between technology providers, healthcare organizations, and community stakeholders. By leveraging the strengths and expertise of each partner, comprehensive and sustainable solutions can be developed and implemented effectively.

The partnership between Qure.ai and LTE Medical Solutions serves as a compelling example of how synergies between innovative technologies and healthcare expertise can drive meaningful change. By combining Qure.ai's cutting-edge AI solutions with LTE Medical Solutions' deep understanding of the healthcare landscape, the collaboration aims to enhance the accessibility, efficiency, and effectiveness of lung health services.

As the discussion unfolds, viewers gain valuable insights into the multifaceted approach required to address the global burden of TB and other lung diseases. The integration of AI-powered solutions like qXR, coupled with the deployment of mobile screening units and the forging of strategic partnerships, represents a comprehensive strategy for advancing Universal Lung Health.

The conversation between Kingshuk Misra and Matsie Lerefolo serves as a rallying call for stakeholders across the healthcare spectrum to unite in the fight against TB and other lung diseases. By embracing innovative technologies, fostering collaborations, and prioritizing community outreach, significant strides can be made in improving lung health outcomes worldwide.

For those eager to learn more about the transformative potential of Qure.ai's solutions and the power of partnerships in driving Universal Lung Health, the full discussion is available to watch. This thought-provoking exchange promises to inspire and inform, offering valuable insights into the future of lung health management and the critical role of AI in shaping it.

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From TB to Universal Lung Health – AI & the power of collaboration

Artificial Intelligence (AI) is redefining precision diagnostics and early detection of multiple diseases, including cancer, neuro-critical conditions, cardiovascular myopathy, and many more. In radiology, AI is used varyingly – its application is being experimented with across the healthcare dissemination pathway to identify the optimal implementation and deployment methodology.&nbsp;&nbsp;

AI solutions, while being highly agile in screening and detection of abnormalities, can also be successfully deployed to optimize radiologists' workflows, facilitate quantitative radiology, and assist in the triage of critical cases. Finally, with the pandemic also weighing in on the current healthcare infrastructure, we can conclude that AI-enabled healthcare will eventually replace legacy infrastructure. &nbsp;&nbsp;

The&nbsp;<a href="https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30160-6/fulltext">Lancet reports</a>&nbsp;that medical investigations leveraging computer-aided diagnostics (CAD) have shown excellent accuracy, sensitivity, and specificity for detecting minor radiographic abnormalities, potentially improving public health. It further states that AI could identify imaging pattern changes that are not readily amenable to human identification.&nbsp;&nbsp;

Earlier this year, the United Kingdom declared its goal of harnessing digital expertise and Artificial Intelligence (AI) to drive transformation within the NHS and social care. Similar adoption models are being discussed in hospitals and healthcare networks across the globe. At present, the workload on the radiology workforce is staggering, with a radiologist required to read a scan every 3-4 seconds. An AI-enabled workflow can alleviate this issue. Moreover, it can help bring down the intra and inter-reader variations. AI solutions can also help use radiology to detect numerous abnormalities early that can directly impact patient prognosis. &nbsp;&nbsp;

While the need for intelligent technology has been established, questions on how, what, and where, remain an open-ended discourse across the medical fraternity. As a result, not much historical data will come into play when an organization decides to implement AI within the system, which is where the collective knowledge of clinicians, radiologists, technologists, and AI solution providers come together. It is the premise of Qure.ai's eminent&nbsp;<a href="https://youtu.be/l8zRXDlhJ2w">panel of experts</a>&nbsp;on AI implementation including:&nbsp;&nbsp;

Step # 1: Identify and isolating the current bottlenecks

It can vary across the set-up of your organization, access to domain experts, and the use case you have identified. For example, at a teaching hospital, AI-enabled data mining may not be a priority as much as resolving the resource crunch and workforce planning. Whereas if you belong to academia, then this becomes your priority. A significant step in seamless implementation is to assemble the right team to implement the project and see to it – end to end. Deploying AI is not a pet project but a large-scale deployment that should integrate with your long-term goal. Getting all the stakeholders, including data governance, IT services, and the chief executive members, on the same page will help in the long run. &nbsp;&nbsp;

Step #2: Identify the right AI vendor for you&nbsp;&nbsp;

With multiple vendors available for similar solutions, industry-approved, clinically validated products should be your go-to option. Though AI is now being sold as a pattern recognition solution, building trust for the technology within the workforce should be your priority to enable them to rely on it as needed. Deploying AI for triage and acting as quality assurance is an excellent place to start. Check their peer-reviewed data and the status of their CE/FDA clearance. Lastly, run a pilot and get your success metrics clearly defined before any deployment. Any implementation is gauged as a success when well-defined success criteria are in place. Having a clear end goal – lives saved, time saved and/or money saved, needs to be obvious to understand the ROI of the solution.&nbsp;&nbsp;

Step #3: Evaluate if the solution solves for your business use case&nbsp;

If you are a teleradiology company looking for AI solutions, a whole new set of questions arises. While most points discussed above, hold true for a teleradiology context as well, like data privacy and FDA/CE clearance, and the host of peer-reviewed data to back an AI solution's precision and read accuracy, a teleradiology company will face more challenges when it comes to integrating the solution across their deployed sites. Unlike a hospital, where the deployment is singular, a teleradiology company will require a customized integration plan for every client site. The challenge heightens if they have multiple PACS vendors in place. When choosing a vendor, they will depend on the vendor's training data's transparency, integration agility, and customizable solutions and markers. 

Step #4: Check for Regulatory Compliance&nbsp;&nbsp;

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