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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.

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Qure.ai: Driving Efficient Workflows&nbsp;

The emergence and proliferation of AI in radiology is a beacon of innovation, driving an epoch of enhanced efficiency and precision that fundamentally reconfigures conventional workflows.&nbsp;

AI's integration into radiology is a monumental advancement, offering a lifeline to overtasked radiologists. Through AI-driven tools, such as those developed by Qure.ai, radiologists can expeditiously navigate through copious imaging studies. For instance, Qure's chest x-ray software, which received <a href="https://appliedradiology.com/Articles/qure-ai-nets-fda-clearance-for-ai-enabled-pneumothorax-and-pleural-effusion-triage-solution" target="_blank">FDA clearance</a> in September, demonstrated in clinical trials the ability to provide healthcare professionals with notifications in an average of 10 seconds, showcasing up to 96% sensitivity and 100% specificity for the <a href="https://radiologyai.com/news/fda-clears-qure-ais-ai-chest-x-ray-software/" target="_blank">qXR algorithm</a>​​. This proficiency is critical in acute care settings, like stroke or trauma diagnoses, where moments can dictate patient outcomes.&nbsp;

The meticulous nature of AI serves as a protective measure against diagnostic inaccuracies, often a byproduct of clinician burnout. The relentless demand for swift and precise interpretations can take a heavy toll on the most experienced professionals. Here, AI enters the fray as an unwavering adjunct, reducing the cognitive load and freeing radiologists to apply their expertise where it is most critical. Such a partnership between humans and machines is pivotal to the next frontier in healthcare delivery, enhancing patient care and clinician welfare.&nbsp;

Beyond accelerating diagnostics, AI fosters a more sustainable model for radiology. By absorbing the routine and arduous tasks, radiologists can reallocate their focus to intricate cases and patient care, reinvigorating their passion for the field. This paradigm shift points towards a harmonious profession where burnout is substantially lessened, and the intrinsic value of a medical practice is reignited.&nbsp;

As the healthcare landscape advances, the assimilation of AI into radiological practices is a testament to the profound capabilities of technology to transform patient care and the well-being of healthcare providers.&nbsp;

In the spirit of collaboration and innovation that defines the Radiological Society of North America (RSNA) 2023, a fireside chat at the Ignite AI Visionary Theater is poised to highlight the real-world implications of artificial intelligence on radiology practices. This session is scheduled for November 26th, Sunday, at 3:45 PM CST, featuring industry leader Dr. Samir Shah, Chief Medical Officer at Qure. Located at Booth #4747, this chat is set to offer a blend of groundbreaking ideas and actionable strategies with the potential to redefine the future of radiology.&nbsp;

Against such discussions, independent evaluations, like those reported in Nature, reveal that CAD software solutions, including Qure's qXR, can parallel the performance of skilled human readers in detecting TB-related abnormalities​​. Qure's qXR v3, for instance, achieved a receiver operating characteristic (ROC) area under the curve (AUC) of 0.82, signifying a <a href="https://www.nature.com/articles/s41598-021-03265-0" target="_blank">high level of diagnostic accuracy</a> comparable to other leading software​​.&nbsp;

The software also outperformed compared to the sensitivity of intermediate readers, indicating its robustness and potential for widespread clinical application​​.&nbsp;

Integrating Qure's solutions into radiology represents a significant stride in the field, showcasing AI's impact in enhancing diagnostic processes and the healthcare ecosystem.&nbsp;

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Qure.ai: Driving Efficient Workflows 

Balancing act between technology and social responsibility&nbsp;

In recent years, Artificial Intelligence (AI) has come to dominate conversations ranging from catastrophic-like scenarios to transformative potentials.&nbsp;&nbsp;

One field where AI is neither a speculative fantasy, nor a distant promise, is radiology.&nbsp;&nbsp;

From significant healthcare systems in the UK to rural clinics in Africa or Asia, radiology AI is changing the game clinically and socially. It addresses urgent challenges like radiologist shortages while enhancing clinical outcomes. Plus, its application transcends the clinical sphere, offering possibilities for broader societal impact - from early medical interventions to healthcare equity and patient trust.&nbsp;&nbsp;

AI has great promise to enhance healthcare and our understanding of what technology can achieve for humanity.&nbsp;

The Clinician’s Benefit: Alleviating Radiologist Workloads&nbsp;

Workforce well-being in radiology has been a mounting concern, with radiologist shortages expected to hit 40% by 2027, according to the Royal College of Radiologists. However, AI in radiology offers a potential solution by redistributing the burden.&nbsp;&nbsp;

A recent pilot study by <a href="https://www.qure.ai/news_press_coverages/early-findings-of-ai-study-at-frimley-health-nhs-foundation-trust-show-99-7-accuracy-in-triaging-chest-x-rays-as-normal" target="_blank">Frimley Health NHS Foundation Trust</a> in collaboration with Qure.ai provided compelling data: the qXR AI solution demonstrated a 99.7% accuracy in categorizing Chest X-rays (CXRs) as usual. This level of accuracy can potentially reduce a consultant radiologist's workload by up to 58%, translating into approximately 2 hours saved per day.&nbsp;

This is more than just a boon for the radiologists. It means they can focus their expertise on complex and critical cases, optimising healthcare's clinical and human elements.&nbsp;

In addition, a study conducted in <a href="https://www.mdpi.com/2075-4418/12/11/2724#:~:text=This%20study%20took%20place%20in,chest%20X%2Drays%20was%20good" target="_blank">Diagnostics </a>processed 65,604 chest X-rays from June 2021 to March 2022. The study's results affirmed AI's prowess: a high negatively predicted value (NPV) of 98.9% was achieved. 'Negatively Predicted Value' here refers to the likelihood that a negative test result correctly identifies the absence of a condition. In simpler terms, if the AI deems an X-ray uncritical, there's a 98.9% chance it is uncritical. The study also found that the turnaround time for diagnosing X-rays decreased by about 40.63% when using AI.&nbsp;

Early Diagnosis, Early Action: A Boon for Public Health&nbsp;

As AI takes on more of the radiological workload, there's an opportunity to redefine the role of human radiologists to focus on more complex and nuanced aspects of patient care. This could include additional training for radiologists in areas like patient communication or interpreting AI-generated data.&nbsp;&nbsp;

By adapting the skills of the existing workforce, healthcare systems can create a synergistic human-AI interaction that enhances public health outcomes.&nbsp;

The notion that early diagnosis leads to better patient outcomes is elementary in medical practice. AI's ability to accurately flag critical results comes as a game-changer. The same qXR AI identified all cases of cancer, even catching nodules at risk for lung cancer that might traditionally go unnoticed.&nbsp;

Early intervention saves lives and considerably reduces the strain on healthcare resources. When illnesses are detected early, the need for invasive surgeries, long-term medication, and extended hospital stays diminishes.&nbsp;

Another study in the <a href="https://journals.lww.com/abhs/pages/default.aspx" target="_blank">Advances in Biomedical and Health Sciences</a> found qXR to have exceptionally high specificity and sensitivity rates.&nbsp;&nbsp;

Specificity (0.90) measures the AI's ability to correctly identify typical cases, while sensitivity (1) gauges its ability to detect criticalities. These rates outperform even trained radiologists, whose sensitivity rates fell between 0.74 and 0.76.&nbsp;&nbsp;

Regarding detecting malignant nodules, qXR's sensitivity ranged from 0.95 to 1.00, and specificity between 0.96 and 0.99, suggesting the AI's adeptness at identifying cancerous cells.&nbsp;

Bridging the Healthcare Gap: AI and Social Equity&nbsp;

While the application of AI in established healthcare systems has been discussed, its potential in remote or underprivileged regions is crucial for levelling-up social equity.&nbsp;&nbsp;

AI can make quality diagnostics accessible and affordable in areas lacking specialised radiologists. This democratisation doesn't just mean broadening healthcare access; it signifies an essential step towards health equity across different groups of people.&nbsp;

Healthcare inequities are striking even in advanced economies. In a study by <a href="https://www.health.org.uk/news-and-comment/news/major-study-outlines-wide-health-inequalities-in-england" target="_blank">The Health Foundation</a>, it was reported that for most of their lives, people in the poorest areas of England, on average, have more diagnosed illness over 10 years earlier than those in the richest areas.&nbsp;

Socioeconomic status, geographical location, and ethnicity still affect health outcomes. AI offers an equalizing force. For instance, Qure’s efforts include deploying AI resources in low-and middle-income countries (LMIC)s like Nepal, where geographical and resource constraints severely limit healthcare access.&nbsp;

Beyond corporate social responsibility, AI radiology solutions directly relate to initiatives to close health disparity gaps in Western countries. The capabilities of AI in radiology can speed up patient screenings in community-based healthcare settings or play a role in ‘find and treat’ strategies making preventative care more widely accessible.&nbsp;

Trust in Technology: A Patient-Centric Approach&nbsp;

Patient trust is a complex issue, often mired by misinformation and scepticism on the general topic of AI. Initiatives to inform patients about the role of technology in their healthcare and robust protocols for data ethics will go a long way in fostering trust.&nbsp;&nbsp;

Performance monitoring of AI technologies, as emphasized by Dr. Neelan Das of <a href="https://www.digitalhealth.net/2023/07/east-kent-hospitals-procures-qure-ai-through-nhs-sbs-framework/" target="_blank">East Kent Hospitals</a>, will be critical to sustained success and public acceptance.&nbsp;

While AI won't wave a magic wand to resolve all issues in healthcare, it offers a step in the right direction. It allows medical professionals to engage more deeply with the aspects of care that require human expertise and empathy, thus improving outcomes.&nbsp;&nbsp;

As AI in radiology proves its efficacy and reliability, the challenge will be to integrate it responsibly into our healthcare systems to benefit clinicians and the broader society.&nbsp;

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AI for good - How activating radiology AI can enhance public health & social equity

The destruction wrought by wildfires is increasingly evident to the modern eye – one that observes the fire, its distant smoke, and the harmful microscopic particulate matter that is harmful to lungs when inhaled. &nbsp;

According to research from <a href="https://link.springer.com/article/10.1007/s13665-022-00293-7" target="_blank">Current Pulmonology Reports</a>, smoke from wildfires contains a harmful mixture of fine particles (PM2.5) and hazardous gases that invade the respiratory and circulatory systems. Exposure to such particulate matter can result in a range of consequences. These consequences span from short-term irritation and wheezing to long-term declines in lung function. Diseases like asthma, bronchitis, and pneumonia become aggravated, and connections to heart disease and mental health add layers to this complex problem.&nbsp;

Wildfires, a natural occurrence often intensified by human activities and climate change, are a growing concern in North America, prompting government officials and health leaders to deliberate and propose ideas for adapting to and combatting such weather conditions. &nbsp;

In June 2023, North American cities were blanketed by thick smoke from Canadian wildfires. As reported in The Lancet, officials were quick to recognize and communicate the health risks. New York City's Health Commissioner, Ashwin Vasan, detailed how particulate matter from the smoke can affect those with existing health conditions, emphasizing the need to stay indoors and to wear masks if outdoor activity couldn’t be avoided. &nbsp;

Last August, the U.S. Environmental Protection Agency (EPA) published guidelines urging health workers to anticipate smoke events and tailor disease management plans accordingly. &nbsp;Measures like stocking up on government-approved N95 respirators and creating personal contingency plans indicate our growing understanding of the issue.&nbsp;&nbsp;

The Lancet states that since 2001, exposure to wildfire smoke has increased, with projections showing a growing trend. But the response from governments, researchers, and community leaders demonstrates a determination to address the problem head-on. Kristie Ebi of the University of Washington insists on an "all-of-society approach," promoting better management across various sectors and social groups –one that is open to the adoption of new tools and strategies for progress.

AI as an aid in fighting wildfire-induced health risks&nbsp;&nbsp;

An expanding body of research demonstrates AI’s ability to competently aid doctors in detecting lung malignancies early – a feature which could be a game-changer in the fight against wildfire-induced lung health risks. &nbsp;

AI’s potential in medicine has seen a rise, offering early detection and interventions in lung illnesses. For example, the deep learning algorithm,<a href="https://www.qure.ai/product/lung-nodule-management"> qCT-Lung</a> from Qure.ai, demonstrates an example of AI’s capabilities in identifying pulmonary nodules, among other critical health markers. &nbsp;

According to research from the journal <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9548727/" target="_blank">Chest</a>, during the COVID-19 crisis in India, qCT-Lung was used on chest CT scans, successfully detecting nodules and even identifying lung cancer cases. The synergy of human expertise and AI-driven insights marks a significant advancement in healthcare.&nbsp;

Practitioners do not confine the integration of AI in healthcare to cancer detection. There is a growing interest in tailoring AI to address wildfire-induced lung diseases, specifically involving assisting healthcare professionals in early diagnosis and providing personalized care. Using real-time data from wildfire occurrences can further enhance the management of health impacts.&nbsp;

Research from <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0204155" target="_blank">PLOS ONE</a> probed the capacity of a deep learning algorithm in analyzing routine frontal chest radiographs (CXR) from adult patients. Researchers applied the algorithm to 874 frontal CXRs from 724 individuals, targeting pulmonary opacities, pleural effusions, and other chest irregularities.&nbsp;

Around 42% of the analyzed CXRs exhibited no abnormalities, with the rest divided between single and multiple irregularities. Notably, the analysis by the deep learning model was on par with human evaluations, showing no substantial statistical difference in detecting all abnormalities, thus accentuating the technology’s adeptness in mirroring human precision.&nbsp;

The research also yielded insights into the effectiveness of DL, as measured by the area under the curve (AUC). Deep learning’s effectiveness ranged between 83% and 93%, closely paralleling the 69% to 92% range for human radiologists. Even more fascinating was that the lowest point noted in the study did not diminish the overall robustness of deep learning’s capabilities but highlighted specific areas for further refinement and research.&nbsp;

Moreover, the finding that the presence of chest wall implanted devices could influence the accuracy of the DL algorithm offers an opportunity for enhancing the technology, tailoring it to accommodate even these complex scenarios.&nbsp;

Integrating AI into medical practice is no longer confined to theory, particularly in the critical domain of lung cancer screening. Further research from <a href="https://journal.chestnet.org/article/S0012-3692(22)02701-5/fulltext" target="_blank">Chest</a> sheds light on a promising collaboration between traditional radiological techniques and AI, offering an exciting pathway toward enhanced diagnostic accuracy.&nbsp;

This research sought to compare how radiologists use the standardized Lung-RADS classification to categorize lung nodules with how an AI algorithm calculates the malignancy risk for those same nodules. Chest researchers studied 210 adult patients, employing human expertise and machine learning to distinguish between malignant and benign nodules.&nbsp;

The AI algorithm ventured beyond mere imitation, assigning malignancy risk scores based on a diverse range of factors, such as the size and shape of the nodules, family history of lung cancer, and the patient's smoking history. The results were telling; AI-generated risk scores were notably higher for malignant nodules, clearly differentiating them from benign ones.&nbsp;

This comparison between traditional Lung-RADS classification and AI scores was not merely an academic exercise. The AI's ability to predict malignancy was comparable to that of the Lung-RADS system. &nbsp;These close results highlight the potential synergy between human intuition and algorithmic precision. The implications of this study are profound, suggesting that AI doesn't merely mimic human expertise but augments it. AI-generated malignancy risk scores demonstrate significant sensitivity in identifying cancerous nodules through low-dose CT scans for lung cancer screening. &nbsp;

This collaborative approach may signal a new era in early cancer detection, where technological innovation enhances human insight. In this merging of minds and machines, both are essential contributors to a potentially life-saving process.&nbsp;

Chest X-rays, renowned for their ability to delineate the anatomical structures of the chest, can identify abnormalities, but they might not be the ideal tool for detecting small particulate matter, especially if it's dispersed throughout the lungs. This technology primarily visualizes structures by differences in density, such as bones appearing white and air appearing black. Particulate matter may not present a significant density difference to be distinguished easily, especially if it's microscopic.&nbsp;

However, alternative imaging modalities might offer greater insight. A CT scan, for example, provides more detailed images and can detect smaller changes in tissue density. In cases of pneumoconiosis—a disease frequently observed in those who have inhaled wildfire particulates—the inhaled particles can lead to fibrotic changes in the lung tissue, which can be visualized on a CT scan.&nbsp;

In these instances, the inhaled particles themselves might not be directly visible. However, the secondary effects they cause become the markers for their presence. And these include fibrosis, nodules, or other structural alterations. A skilled radiologist could infer the presence of particulate matter based on these characteristics with the augmentation of AI software such as qCT-Lung, enabling them to see more patients and prioritize critical cases.&nbsp;&nbsp;

Wildfires represent more than mere destruction; they highlight a nuanced health crisis that extends far beyond the visible landscape. The promising advances of AI provide a compelling answer, detecting, diagnosing, and potentially mitigating the respiratory impacts of wildfires.&nbsp;

The combination of human insight and technological innovation offers a promising future. AI's role in addressing the challenges posed by wildfires and lung health is a compelling testament to progress, showcasing an effective response and a vision of what might be.&nbsp;

The potential of AI in transforming the landscape of medicine and health is immense. This new chapter in diagnostics and treatment is not just a medical footnote but a significant stride toward improved care and understanding. As we grapple with the multifaceted effects of wildfires, technology offers a vision of hope and a path to a more responsive and empathetic healthcare system.&nbsp;

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AI's Rising Importance in Combating Wildfire-Related Lung Health Issues

In the complex domain of American healthcare, radiologists have always been invaluable guides.&nbsp;But the tribulations of a pandemic world—lockdowns that led to formidable backlogs and supply&nbsp;chain failures—have posed challenges. Even as technicians departed for greener pastures amid&nbsp;soaring inflation, a phenomenon branded as "the great resignation," radiologists managed an&nbsp;unprecedented feat: they increased productivity.&nbsp;

Radiologists are diagnosing more than ever, signaling both the resilience and vulnerability of a&nbsp;healthcare system at its limits. This increased workload places radiologists as vital and&nbsp;precarious elements in an overstressed healthcare landscape.

Radiological fatigue is less an indictment of the individual radiologist and more a signal flare,&nbsp;illuminating a system teetering at the edge of its capacities.

Drawing a parallel with the familial role of a parent, radiologists face a similar dilemma: the&nbsp;ceaseless demand for reasonable care at the expense of their well-being.

In this crucible of perpetual care, radiologists grapple with a pervasive sense of duty akin to&nbsp;parental responsibility. The desire to step back and recharge isn't solely a matter of&nbsp;self-preservation. Still, it is tinged with disquieting guilt—the feeling of momentarily&nbsp;abandoning a cohort of colleagues also wading through their states of weariness.

Hence, radiological fatigue is not a personal failing as it is an indicator of systemic stress points. It serves as a glaring reminder that even the most dedicated practitioners are not impervious to the human need for rest, a need made even more pressing by the weight of ethical duty and communal responsibility. In a field that depends on unwavering accuracy, the consequences of ignoring these human limitations can be both profound and dire.

Radiological fatigue is indeed an occupational hazard, but it's not a reflection of individual shortcomings; rather, it exposes systemic limitations in the high-stakes field of radiology. The phenomenon catalyzes examining the inherent challenges within the discipline rather than questioning the resilience of radiologists.

Work-related fatigue has long been in radiology, serving as a reliable precursor to burnout among professionals in the field. Notably, a piece in the <a href="https://www.ajronline.org/doi/full/10.2214/AJR.17.18613#sec-3">Journal of Roentgenology</a> has dedicated itself to thoroughly examining this issue. The consensus among researchers is that radiological fatigue is a widespread phenomenon, not restricted by age or experience level. However, it seems more pronounced in younger practitioners.

Perhaps more troublingly, this fatigue does more than just dampen the spirits of its victims—it directly impinges on the quality of their work. For example, readings conducted during the latter stages of a workday exhibited a notable drop in accuracy. Additional factors like suboptimal lighting conditions only compound the problem.

The pernicious impact of radiological fatigue extends beyond the boundaries of the professional lives of radiologists; it constitutes a tangible threat to patient care. Errors stemming from fatigue aren't merely procedural lapses; they're missteps that could irrevocably alter the trajectory of a patient's health.&nbsp;

Facing an unsustainable status quo, Efficiency Artificial Intelligence (eAI) emerges as a potential remedy -- a calculated approach to augment human expertise and address challenges like radiological fatigue. This is rooted in the concept that our systems are "broken in the right places," presenting both vulnerabilities and opportunities for efficiency gains.

While the situation in radiology is disquieting, it doesn't necessarily consign us to a realm of insurmountability.&nbsp;

The judicious deployment of AI might be indispensable to navigating this labyrinth of labor shortages and escalating burnout. However, it's worth tempering optimism with an astute assessment of the technological landscape. AI tools in radiology exhibit a developmental heterogeneity—some are nascent.&nbsp;

In contrast, others are sophisticated enough to handle basic workflows, collate data for nuanced biomarker analysis, and even modestly augment human capacity in routine tasks. Thus, the evolution of these tools must prioritize advancing beyond this 'basic' stage to fully realize their potential in mitigating the manifold pressures beleaguering the field.

A.I.'s ability to handle vast data, streamline workflows, and augment human capabilities could ease the burden on radiologists.&nbsp;

By automating some tasks, A.I. can potentially liberate radiologists to focus on more complex and critical aspects of patient care. Integrating A.I. into the radiological workflow represents not merely a technological advancement but a strategic alignment that could reshape the future of radiology.

Dr. Giovanni Lorenz, a cardiac radiologist, emphasizes the significance of automation beyond the interpretation of images.&nbsp;

He notes that the “preloading” or “orchestrating” events—such as automating the protocol, scheduling, and pre-authorization processes—are crucial for streamlining patient flow through the radiological suite. Dr. Lorenz believes that these automated tasks, supported by emerging eAI tools, contribute to significant gains in economy and efficiency.&nbsp;

These optimizations, often overlooked in discussions about AI in radiology, are task-oriented solutions that facilitate radiologists in focusing on the complex, interpretive aspects of their work.&nbsp;

A study published in <a href="https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(18)31645-3/fulltext#seccestitle150">The Lancet</a> illustrates a potential utility in applying deep learning algorithms to head trauma and stroke diagnostics.&nbsp;

By analyzing over 300,000 head C.T. scans, researchers developed algorithms that detect critical findings such as intracranial hemorrhages, calvarial fractures, midline shifts, and mass effects. The algorithms achieved impressive accuracy across different datasets, reaching levels comparable to human experts. The crucial insight of this study –which is but <a href="https://www.qure.ai/evidences">one among dozens of studies that provide similar empirical grounds for hope</a>-- lies in its illumination of A.I.’s potential to automate the radiological process –permitting doctors to focus on critical cases by marking which scans are most urgent.

Dr. Lorenz further emphasized the evolving role of AI in shaping the future of medical imaging. “As the final authority, I tailor AI-generated draft reports to meet specific medical needs. This capability lets the report transcend the limitations of the immediate clinical setting, serving both on-demand and preventative care needs. In this ever-complex landscape of medical data, AI acts not merely as a tool, but as a co-pilot helping us.”

While Artificial Intelligence is a groundbreaking tool that promises considerable improvements, it should not be viewed as a magic wand or panacea. Artificial Intelligence does more than merely augment computational capabilities. It has the potential to redefine how tasks are allocated and performed. For instance, AI algorithms can handle routine analyses of standard examinations, allowing radiologists to focus on more complex cases.&nbsp;

This streamlines processes, enhances productivity and has been empirically validated for its agreement with human expertise and time efficiency. However, for AI to realize its full potential, it needs inputs from multiple disciplines—radiologists contributing clinical experience, administrators overseeing economic considerations, and tech innovators providing technological solutions.

While investing in AI research is crucial, this must happen within a comprehensive framework that prioritizes human well-being. By fostering interdisciplinary collaboration and welcoming technological advances, the healthcare sector can write a new chapter—a narrative focused not on burnout but on innovation, efficiency, and human welfare.

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Combating Radiological Burnout in the U.S.: Exploring A.I. Solutions in Healthcare

Demand for access to the front door of healthcare services for severe injuries and life-threatening conditions has reached unprecedented levels in the UK. Accident and Emergency (A&amp;E) or Emergency Departments (ED) are continually challenged to meet the&nbsp;operational standards set in 2010 that at least&nbsp;<a href="https://www.kingsfund.org.uk/projects/nhs-in-a-nutshell/waiting-times">95% of patients attending A&amp;E should be admitted, transferred, or discharged within four hours.</a>

While performance data in summer months paint a relatively optimistic picture, come the winter, when patient profiles change to a more significant proportion of older people attending and requiring emergency admission, the<a href="https://inews.co.uk/news/hospitals-england-critical-incidents-a-and-e-departments-struggle-2054842">&nbsp;scenes of critical winter A&amp;E incidents</a>&nbsp;of backed-up ambulances and trolley waits in corridors may return.&nbsp;

The&nbsp;<a href="https://www.nuffieldtrust.org.uk/resource/a-e-waiting-times">reasons for A&amp;E pressures are complex</a>, from a perfect storm of elements impacting the flow of patients across the whole health and care continuum. Yet incremental departmental gains can be achieved by implementing innovative digital health and Artificial Intelligence (AI) systems.&nbsp;

Creating calm and clarity in critical situations&nbsp;

No patient or family relative wants to be in a hectic, loud, and busy emergency environment for hours on end. And the pressures of patient demands have also taken their toll on staff, who find working in an increasingly challenging environment draining, with retention and recruitment rates being challenged.&nbsp;

Can AI be a panacea for streamlining throughput and supporting clinicians with rapid diagnostic decision-making and case prioritization?

AI to help speed up chest X-ray reporting

In the Southeast of England,&nbsp;East Kent Hospitals serve a patient population of over 700,000 people. It is now&nbsp;<a href="https://www.ekhuft.nhs.uk/news/ai-software-helps-speed-up-x-ray-reporting/">using qXR AI software to help check chest X-rays for patients in its emergency departments.</a>

The solution can spot potential issues almost instantly and alert doctors, reducing any likely delay in starting treatment. It means issues such as cancer, collapsed lungs and tuberculosis can&nbsp;possibly be detected quickly without waiting for a radiologist or expert reporting radiographer to review the images.

Dr.&nbsp;Neelan Das, Consultant Interventional Radiologist,&nbsp;and AI lead for the Trust,&nbsp;said there were clear benefits for patients and emergency department staff, “It [AI] has real potential to improve patient care and outcomes and to reduce delays in our emergency departments. The technology allows for faster interpretation and targeted reporting&nbsp;of the images so that any abnormalities can be flagged&nbsp;immediately&nbsp;for the doctor to review. Our&nbsp;initial&nbsp;testing has shown that it is at least as sensitive as a senior radiologist and when it is rolled out fully across our emergency departments it could analyse thousands of x-rays every month.”&nbsp;

AI to analyse head CTs for faster decision-making

Similarly, the potential of AI for neurocritical patients admitted following trauma incidents or suspected strokes is gaining momentum to deliver faster decision-making and timely treatment planning.&nbsp;&nbsp;&nbsp;

In partnership with Blackford, Milton Keynes University Hospital NHS Foundation Trust has just gone live with qER, Qure’s neurocritical AI.&nbsp;&nbsp;&nbsp;

The Trust sees and treats more than 400,000 outpatients and over 100,000 emergency department patients each year, in addition to delivering a wide range of elective (planned) and non-elective procedures. It will use Qure’s qER to automate the detection of critical and life-threatening abnormalities in head CT scans, including intracranial haemorrhages (ICH) caused by trauma, accidents, or strokes. This will aid the triage of critical cases, within three minutes, in the emergency department, especially during out-of-hour shifts when a larger team may not be available.

Andrew Scott, PACS Manager / Imaging IT Systems Lead Imaging Department of Milton Keynes University Hospital NHS Foundation Trust&nbsp;explains,&nbsp;“Our collaboration with Blackford and Qure.ai empowers our Emergency Department with AI to swiftly&nbsp;identify&nbsp;critical head CTs scans, enabling us to prioritise and&nbsp;provide&nbsp;timely&nbsp;care to those who&nbsp;require&nbsp;it most. By harnessing the power of AI, we can improve clinical workflow efficiency therefore ensuring that we deliver the safest and most effective care for&nbsp;all of&nbsp;our patients.”&nbsp;&nbsp;

AI to swiftly identify normal or abnormal MSK fractures&nbsp;

Suspected bone fractures make up a good proportion of patients waiting in the Emergency Department. So, imagine a scenario where X-rays are read automatically by AI and cases are flagged to clinicians as normal for quick reviews and patients are discharged out of the busy department, or as abnormal, requiring next-stage care or referral.&nbsp;&nbsp;

qMSK aids in detecting bone fractures and joint dislocations of multiple body parts on X-rays in less than a minute.&nbsp;&nbsp;

Patient waiting times at the front door of UK healthcare, and departments beyond, will not be solved overnight. Yet with continued strategic focus nationally, and&nbsp;<a href="https://www.gov.uk/government/news/21-million-to-roll-out-artificial-intelligence-across-the-nhs">NHS confidence in AI&nbsp;</a>as tool to support patients and clinicians, the future presents some compelling efficiency and value gains.&nbsp;

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AI on the frontline of A&E

As winter knocks at the UK’s door later this year, the dedicated&nbsp;NHS&nbsp;(National Health Service) braces for the onslaught of challenges that accompany the season, carrying much more than a freezing chill. Beyond the dropping mercury, a flurry of concerns bears down upon the NHS, from seasonal illnesses to cold-induced health conditions and a spike in accidents and injuries.

This mix of adversity triggers a ripple effect: increasing hospital footfall, depleting bed availability, and exacerbating wait times. The perennial impact of these winter woes also reaches healthcare personnel, who may find themselves or family members under the weather, thereby stretching NHS resources and workforce even further.

The revered NHS celebrates its 75th anniversary this year. Yet, this moment of celebration is tempered by the thought that approximately 7.4 million individuals currently&nbsp;<a href="https://www.bma.org.uk/advice-and-support/nhs-delivery-and-workforce/pressures/nhs-backlog-data-analysis">await treatment</a>&nbsp;across the UK. Amid the more acute cases, the plight of cancer patients is cause for concern.

Despite the volume of patient referrals exceeding pre-pandemic figures, the number of those seen by a specialist within two weeks of an urgent GP referral for suspected cancer continues to fall short of expectations. The NHS’s goal of ensuring 93% of patients are seen within this timeframe has remained unachieved since May 2020, with the figure standing at a mere 77.7% in April 2023.

Exacerbating these challenges, the&nbsp;<a href="https://www.rcr.ac.uk/clinical-radiology/rcr-clinical-radiology-workforce-census-2022">NHS radiologist workforce</a>&nbsp;found itself under-resourced by around 29% in 2022, a gap that could widen to a daunting 40% by 2027. Together with escalating patient wait times and looming pressures on the healthcare system as we inch closer to year-end, a formidable healthcare tempest appears to be brewing for winter.

But before the cold north winds have a chance to sweep through the UK healthcare system, the government has potentially presented the NHS with the greatest 75th anniversary gift – the greenlight for&nbsp;<a href="https://www.gov.uk/government/news/21-million-to-roll-out-artificial-intelligence-across-the-nhs">substantial funding</a>&nbsp;towards the deployment of Artificial Intelligence (AI), offering a glimmer of hope for both clinicians and patients.

In England alone, over 600,000 chest X-rays – a key tool in diagnosing lung cancer, the top cause of cancer-related deaths in the UK – are conducted each month. The bold initiative to&nbsp;<a href="https://www.digitalhealth.net/2023/07/nhs-england-sets-out-seven-short-term-digital-priorities/">incorporate AI solutions</a>&nbsp;for analysing these chest X-rays under the AI Diagnostic Fund represents a significant stride towards progress.

The integration of AI into the NHS’s daily operations promises a host of benefits for lung cancer patients, including:

1. Smoother and more efficient flow of patients through the diagnostic imaging pathway to catch up on backlogs and ease waiting times for screening and symptomatic cases.

2. Promoting early diagnosis, thereby requiring less invasive treatment regimes, reducing treatment costs, and improving life expectancy.

3. Easing system strains by addressing workforce shortfalls.

Of course, the scope of AI solutions extends beyond lung cancer detection. Stroke&nbsp;<a href="https://www.nhs.uk/conditions/stroke/diagnosis/">diagnosis</a>&nbsp;is extremely time-sensitive; the proverbial&nbsp;<a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5440901/">‘Golden Hour’</a>&nbsp;is critical in ensuring better outcomes for patients. And, the NHS has<a href="https://qure.ai/news_press_coverages/qure-ais-brain-ct-ai-wins-funding-from-the-nhs/" target="_blank">&nbsp;</a><a href="https://qure.ai/news_press_coverages/qure-ais-brain-ct-ai-wins-funding-from-the-nhs/">funded the use of AI solutions</a>&nbsp;for stroke as well. Moreover, Frimley Health NHS Foundation Trust has deployed an AI-enabled chest X-ray screening solution to enhance reading efficiency and improve reporting turnaround times. Implementing the AI solution&nbsp;led to a reduction&nbsp;&nbsp;in radiologist workloads by over 58%, which translates to a timesaving of approximately 2 hours daily.&nbsp;

The recently unveiled&nbsp;NHS Long Term Workforce Plan&nbsp;shines a spotlight on ‘Digital and technological innovations’. It proposes that harnessing digital and technological advancements is vital for the healthcare system. Growing evidence supporting AI application raises high hopes, with AI predicted to boost service efficiency, particularly in diagnostic support, administration, predictive health analytics, patient triage, and preventative healthcare. AI is also poised to expedite drug discovery and design through enhanced patient recruitment and data analysis.&nbsp;

&nbsp;As we celebrate the NHS’s 75th anniversary, it is crucial to appreciate the relentless dedication and unwavering commitment that have carried this institution to its current standing. Equally, we should recognise the transformative potential of Artificial Intelligence to shape the NHS’s trajectory in the forthcoming decades. Here’s to the promise of AI, the enduring spirit of the NHS, and to many more years of healthcare excellence, pioneering innovation, and the highest standards of care. May the NHS continue to weather the harshest of winters, guided by the beacon of technological advancement.&nbsp;

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A New Frontier in Healthcare - The NHS at 75 and the Role of AI

Shamie Kumar describes how AI fits into a radiology clinical workflow and her perspective on how clinical radiographers could use this to learn from and enhance their skills.

AI in radiology and workflow&nbsp;

We all know that AI is already here, actively being implemented and used in many trusts in seeing its real world value supporting radiology departments to solve current challenges.Often this is focused on benefits to radiologist, clinicians, reporting radiographers, patients, and cost savings, but what about clinical non-reporting radiographers undertaking the X-ray or scans – can AI benefit them too?Let’s think about how AI is implemented and where are the AI outputs displayed?

If the AI findings are seen in PACS, how many radiographers actually log into PACS after taking a scan or X-ray? Good practice is seen to have PACS open to cross-check images that have been sent from the modality. Often this doesn’t happen for various reasons but maybe it should be a part of the radiographers’ routine practice, just like post-documentation is.

Given the view it does happen, radiographers will have the opportunity to look at the AI outputs and potentially take away learnings on whether the AI found something that they didn’t see initially or whether there was a very subtle finding. We all know people learn through experience, exposure, and repetition, so if the AI is consistently picking up true findings, then the radiographer can learn from it too.

But what about when AI is incorrect – could it fool a radiographer, or will it empower them to research and understand the error in more detail?

As with many things in life, nothing is 100% and this includes AI in terms of false positive and false negatives. The radiographers have the opportunity to research erroneous findings in more detail to enhance their learning, but do they actually have time to undertake additional learning and steps to interpret AI?

CPD, self-reflection, learning through clinical practice are all key aspects of maintaining your registration, and self-motivation is often key to furthering yourself and your career. The question remains: are radiographers engaged and self-motivated to be part of the AI revolution and use it to their professional benefit with potential learnings at their fingertips?

There have been a few recent publications that share insight on how AI is perceived by radiographers, what is their understanding, training and educational needs.

Many Universities like City University London and AI companies like Qure.ai are taking the initial steps in understanding this better and taking active efforts in filling the knowledge gap, training and understanding of AI in radiology.

Radiographers who are key part of any&nbsp;radiology pathway, are yet to see the real-world evidence on whether AI can upskill radiographers, but there is no doubt this will unfold with time.

Akudjedu, T. K. K. N. M., 2022. Knowledge, perceptions, and expectations of Artificial intelligence in radiography practice: A global radiography workforce survey. Journal of Medical Imaging and Radiation Sciences.Coakley, Y. M. E. C. M. M., 2022. Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging. Radiography International Journal of Diagnostic Imaging and Radiation Therapy, 28(4), pp. P943-948.

Malamateniou, K. P. W. H., 2021. Artificial intelligence in radiography: Where are we now and what does the future hold?.&nbsp;Radiography International Journal of Diagnostic Imaging and Radiation Therapy,&nbsp;27(1), pp. 58-62.

Kumar, D., 2022.&nbsp;CoR endorsed CPD Super User Training by Qure.ai.

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Can We Up skill Radiographers through Artificial Intelligence?

Shamie Kumar describes her perspective on how radiography has evolved over time, the impact radiographers can have in detecting abnormal X-rays and reflects how she views fast approaching AI in advancing current skills.

Often one of the first courses a newly qualified radiographer attends is the red dot course. This course demonstrates pathologies and abnormalities often seen in x-rays some obvious, others not, giving radiographers the confidence to alert the referring clinician and/or radiologist that there is something abnormal they have seen.&nbsp;

The red dot system is a human alert system, often 2 pairs of eyes are better than one and assist with near misses. How this is done in practice can vary between hospitals, in the era of films the radiographer would place a red dot sticker on the film itself before returning it to clinician or radiologist. In the world of digital imaging this is often done during ‘post documentation’ a term used once the&nbsp;x-ray is finished, the radiographer will complete the rest of the patient documentation to suggest the x-ray is complete, ready to be viewed and reported. As part of this process the radiographer can change the status of the patient to urgent along with a note for what has been observed. From this the radiologist knows the radiographer has seen something urgent on the image and the patient appears at the top of their worklist for reporting and, so the radiologist can view the radiographer’s notes.&nbsp;

Artificial Intelligence (AI) is moving at a pace within healthcare and fast approaching radiology departments, with algorithms showing significant image recognition in detecting, characterisation and monitoring of various diseases within radiology. AI excels in automatically recognising complex patterns in&nbsp;imaging data providing quantitative assessments of radiological characteristics. With the numbers for diagnostic imaging requests forever increasing, many AI companies are focusing on how to ease this burden and supporting healthcare professionals.&nbsp;

AI triage is done by the algorithm based on abnormal and normal finding’s this is used to create an alert for the referring clinician/radiologist. It can be customised to the radiologist, for example colour-coded flags, red for abnormal, green for normal, patients with a red flag would appear at the top of the radiologist worklist. For the referring clinicians who don’t have access to the reporting worklist, the triage would be viewed on the image itself with an additional text note suggesting abnormal or normal.&nbsp;

What does AI do that a radiographer doesn’t already? AI is structured in the way it gives the findings for example a pre-populated report with its findings or an impression summary and its consistent without reader variability. So, the question now becomes what AI can do beyond the red dot system, here the explanation is straightforward, often a radiographer wouldn’t go to the extent of trying to name what they have seen, especially in more complex x-rays like the chest where there are multiple structures and pathologies. For example a radiographer would mention, right lower lobe and may not go beyond this, often due to confidence and level of experience.&nbsp;

AI can fill this gap, it can empower radiographers and other healthcare professionals with its classification of pathologies identifying exactly what has been identified on the image, based on research and training of billions of data sets with high accuracy.&nbsp;

The radiographers may have the upper hand with reading the clinical indication on the request form and seeing the patient physically, which undoubtable is of significant value, however the red dot system has many variables specific to that individual radiographer’s skills and understanding. It is also limited to giving details of what they have noted to just the radiologist, what about the referring clinician who doesn’t have access to the radiology information system (RIS) where the alert and notes are? Do some radiographers add a text note on the x-ray itself?&nbsp;

Yes, AI is a technological advancement of the red dot system and will continue to evolve. It is structured in how it gives the findings, it does this consistently with confidence. Adding value to early intervention, accurate patient diagnosis, contributing to reducing misdiagnosis and near misses. AI is empowering radiographers, radiologist, referring clinicians and junior doctors by enhancing and leveraging their current knowledge to a level where there is consistent alerts and classified findings that can even be learned from. This doesn’t replace the red dot system but indeed enhances it.&nbsp;

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