Each year approximately 2 billion chest X-rays are performed globally. They are fast, noninvasive, and relatively inexpensive radiological examinations for front-line diagnostics in outpatient, emergency, or community settings.&nbsp;

Be it serendipitous screening, opportunistic detection, or incidental identification, there is potential for AI incorporated into CXR to screen patients for disease when they are getting an unrelated medical examination.&nbsp;

AI can find these&nbsp;abnormalities and flag them to clinicians as a suspicious finding for further investigation.&nbsp;

Indeed, only 5.8%&nbsp;of eligible ex-smoking Americans undergo CT-based lung cancer screening.&nbsp;

Early global studies&nbsp;into the power of AI for incidental pulmonary nodules (IPNs) shows exciting promise.

The&nbsp;<a href="https://www.qure.ai/product/lung-nodule-management" target="_blank">qXR-LN</a>&nbsp;chest X-ray AI algorithm&nbsp;from Qure.ai is raising the bar for incidental pulmonary nodule detection. In a retrospective study performed on missed or mislabelled US CXR data, qXR-LN achieved an impressive negative predictive value of 96% and an AUC score of 0.99 for detection of pulmonary nodules.&nbsp;

The FDA-cleared solution&nbsp;serves as a crucial second reader, assisting in the review of chest radiographs on the frontal projection.&nbsp;

By harnessing the power of AI for opportunistic lung cancer surveillance, healthcare providers can adopt a proactive approach to early detection, without significant new investment, and ultimately improving patient survival rates.

Note:&nbsp;<a href="https://theimagingwire.com/2024/11/07/chest-x-ray-an-important-tool-for-early-lung-cancer-detection/">First published in Imaging Wire, November 14 2024</a>

large_Early Lung Cancer Detection-01.jpg

medium_Early Lung Cancer Detection-01.jpg

small_Early Lung Cancer Detection-01.jpg

thumbnail_Early Lung Cancer Detection-01.jpg

Early Lung Cancer Detection-01.jpg

Future of AI-Powered Incidental Pulmonary Nodule Detection

From Ancient Times to Activating AI: How scientific discoveries and inventions have shaped the field of respiratory medicine and pulmonology&nbsp;&nbsp;

From the earliest discoveries of respiratory diseases to the modern-day AI-driven innovations, the journey of lung health has been marked by significant discoveries, inventions and milestones.&nbsp;&nbsp;

Lung cancer, once considered a rare medical curiosity, has become a global health challenge. While tobacco use remains a significant risk factor, other elements such as second-hand smoking, radon, occupational hazards, pollution, and genetics also contribute to the development of lung cancer.&nbsp;&nbsp;

Innovations to understand &amp; fight against Lung Cancer&nbsp;

The first historical observation of Lung Cancer was in the 1800s by Dr Laennec, a French physician, who described lesions in the lungs that were different to tuberculosis (TB). By 1823 the Lancet Journal is launched, promoting postmortems or autopsies to identify lung cancer as a separate diagnosis to TB.&nbsp;

In 1895, Wilhelm Roentgen pioneered the production and detection of electromagnetic radiation in a wavelength, x-radiation, to be known as X-rays. It was an achievement that earned him the inaugural Nobel Prize for Physics in 1901 and its use in medical applications soon followed shortly.&nbsp;

Fast forward to today, nearly 130 years since the invention of X-ray, plain film radiography is the mainstay of frontline medical imaging, with an estimated 2 billion chest X-rays alone performed per year globally.&nbsp;&nbsp;

The popularity and versatility of X-ray has stood the test of time with a number of innovations expanding its use and application to serve a wider range of patient communities across health boundaries. This includes the move from analogue to filmless and chemical free digital radiography; portability of detector plates to refashion the traditionally fixed systems onto wheels to take directly to patient bedsides or outpatient clinics; and the shrinking of X-ray solutions into easy-to-carry systems that fit into backpacks or flight cases to reach remote and rural communities.&nbsp;&nbsp;

The invention of Computed Tomography (CT) in the the late 1960s and its application into medicine in the early 1970s revolutionized medical imaging. Unlike traditional X-rays, which provide limited two-dimensional views, CT scans offered detailed cross-sectional slice images of the lungs, allowing for more precise visualization. This positioned CT as the gold standard for lung cancer screening in many developed nations, as it can detect small nodules or abnormalities, leading to earlier diagnoses and better survival rates.&nbsp;

Low-Dose CT is still central to proactive lung cancer screening programs in place across the world for high-risk groups such as ex or current smokers. These programs have reduced lung cancer mortality by enabling earlier identifications leading to interventions.&nbsp;

The timeline of testing for and tackling rates of TB

AI powering the future of early identification of disease&nbsp;

Today, AI is transforming the landscape of lung health identification and management using medical imaging.&nbsp;&nbsp;

AI solutions, like <a href="https://www.qure.ai/product/lung-nodule-management" target="_blank">qXR from Qure.ai</a>, analyze chest X-rays, detecting the risk of even the smallest nodules that could indicate early-stage cancer. X-ray-based surveillance as part of Incidental Pulmonary Nodule initiatives, also known as incidental nodule detection, are growing in strength.&nbsp;&nbsp;

AI's applications also extend beyond detection, with solutions like <a href="https://www.qure.ai/product/lung-nodule-management" target="_blank">qCT</a> that helps with precise qualification, comprehensive characterization and 3D visualization. Recently <a href="https://www.qure.ai/news_press_coverages/qure.ai-launches-FDA-cleared-AI-solution-for-advanced-lung-nodule-quantification-on-CT-scans-at-AABIP-2024" target="_blank">qCT LN Quant gained US FDA clearence</a> to support radiologists and pulmonologists in analyzing lung nodules on non-contrast CT scans and tracking volumetric growth as part of progression monitoring.&nbsp;&nbsp;

AI is also playing a crucial role in the fight against TB. <a href="https://www.qure.ai/global-health" target="_blank">Qure.ai's qXR can detect TB</a> on chest X-rays with high accuracy, helping to identify cases early and initiate timely treatment. AI also aids in contact tracing, predicting disease spread, and optimizing resource allocation in TB control programs.&nbsp;

The future of AI in TB care includes possibilities such as AI-guided personalized treatment regimens, AI-powered adherence monitoring, and AI-driven drug discovery for novel TB therapies. As we continue to battle this antiquated disease, AI offers hope for a future where TB is effectively controlled and ultimately eliminated.&nbsp;

The Future of AI in Lung Health Care&nbsp;

The developmental trajectory of lung health, from ancient times to the AI-driven present, is a testament to human resilience and innovation. As we observe the International Day of Radiology and Lung Cancer Awareness Month, we must celebrate the progress made in lung cancer care and TB detection, while recognizing the critical role of AI in shaping the future of lung health.&nbsp;

By harnessing the power of AI, we can accelerate early detection, improve treatment outcomes, and ultimately save lives. As we stand on the brink of a new era in lung health, let us embrace AI's potential to transform the way we prevent, identify, and treat respiratory diseases, paving the way for a healthier future for all.

large_Past_Present_Future-02.webp

medium_Past_Present_Future-02.webp

small_Past_Present_Future-02.webp

thumbnail_Past_Present_Future-02.webp

Past_Present_Future-02.webp

The Past, Present & Future of Lung Cancer

A discussion of how innovations in assistive medical device technology such as robotic bronchoscopy and novel medical imaging AI can be game changers for the early detection of lung cancer and early-stage treatment planning.&nbsp;

In pulmonary medicine, early Incidental Pulmonary Nodules (IPN) detection and subsequent characterization is critical to improve patient outcomes. IPNs, often the first indicators of potential lung cancer, have traditionally been identified by a manual or keyword search facilitated review of chest X-ray and CT reports (CXRs) by radiologists or pulmonologists.&nbsp;&nbsp;

However, to err is human, and radiologists can overlook subtle nodules, leading to delayed diagnosis and treatment. This is where Artificial Intelligence (AI) is poised to revolutionize the current paradigm of lung cancer treatment, offering a powerful tool for early IPN detection and transforming the landscape of thoracic medicine.&nbsp;

AI algorithms, trained on extensive datasets of CXRs, can detect clinically relevant IPNs with unparalleled accuracy and consistency, with high sensitivity and specificity. These sophisticated algorithms can identify suspicious nodules that may be missed by human observers, in some cases in up to 70% of radiographs, significantly increasing the overall detection rate and thus casting a wider net for early lung cancer detection. The early identification of IPNs through AI augments the pulmonology funnel, thus enabling interventional pulmonologists to initiate diagnostic bronchoscopies earlier, expediting the path to definitive diagnosis and treatment.&nbsp;

The implications of AI-assisted early IPN detection are far-reaching. By putting patients into investigation pathways more quickly, surgeons, radiologists and pulmonologists can collect tissue samples for cancer cell testing at an earlier stage. This accelerated process facilitates a prompter conclusive diagnosis, allowing patients to enter the appropriate treatment pathway more swiftly, potentially even for cure. AI is no doubt a game-changer in the quest for early intervention and improved survival from the world’s deadliest cancer.&nbsp;

Maximizing medical device innovations in tune with AI&nbsp;&nbsp;&nbsp;&nbsp;

The field of pulmonology has witnessed a remarkable transformation with the advent of robotic bronchoscopy. This innovative technology combines the precision of robotics with the flexibility of traditional bronchoscopy, enabling pulmonologists to conclusively diagnose lung cancer at earlier stages. By utilizing thin, maneuverable catheters guided by advanced robotics, physicians can now access peripheral lung nodules inaccessible to CT guided biopsy with unparalleled accuracy and stability.&nbsp;&nbsp;

This minimally invasive approach not only improves diagnostic yields but also significantly reduces the time from initial nodule detection via X-ray and/or CT to biopsy, allowing patients to receive timely treatment. The technical aspects and clinical implications of groundbreaking robotic bronchoscopy is poised to revolutionize the early detection and management of lung cancer.&nbsp;

AI to enhance clinical confidence and efficiency&nbsp;

Beyond its role in early detection, AI offers many benefits that bolster clinical confidence and streamlines workflow in pulmonology. One such benefit is the standardization of nodule assessment. AI ensures consistent evaluation of CXRs, mitigating inter-observer variability amongst radiologists and pulmonologists and upholding high diagnostic accuracy. This standardization enhances the reliability and reproducibility of IPN detection across different healthcare settings.&nbsp;

Moreover, AI's rapid ability to identify IPNs accelerates subsequent diagnostic tests and interventions, expediting patient access to care. This efficiency optimizes clinical paradigms and enables pulmonologists to make informed treatment decisions more rapidly. The seamless integration of AI into the diagnostic process saves time and allows for a more streamlined approach to patient management.&nbsp;

Another crucial aspect of AI's impact on clinical confidence lies in its advanced nodule analysis capabilities. AI can generate Lung Nodule Malignancy Risk Scores, aiding in the differentiation and distinction between benign and malignant nodules. This sophisticated analysis reduces false positives and unnecessary procedures while providing invaluable clinical decision support. The risk scores incorporate nodule characteristics, such as texture and composition, and calculating doubling time of volume.&nbsp;&nbsp;&nbsp;

AI can alleviate patient anxiety by accurately and expeditiously identifying nodules requiring further intervention, and more importantly those that do not warrant biopsy. Integrating AI-powered nodule analysis empowers pulmonologists to make more accurate and informed decisions, ensuring patients receive the most appropriate care based on their needs.&nbsp;

Furthermore, AI-generated reports offer detailed insights and visual aids that facilitate effective communication between pulmonologists and patients. These detailed and organized reports improve patient understanding and engagement in their care plan, fostering a patient-centric approach. The synergy of technology and human expertise creates a collaborative environment where the best possible outcomes can be achieved.&nbsp;

The integration of AI into pulmonology pathways holds immense promise for optimizing patient care. Early lung cancer detection through AI-assisted IPN identification can significantly improve survival rates. Moreover, AI's ability to enhance clinical decision-making, guide interventions, and optimize workflows has the power to transform the practice of pulmonology.&nbsp;

As the field of AI continues to evolve, innovative solutions like Qure.ai's <a href="https://www.qure.ai/product/lung-nodule-management" target="_blank">qXR-LN</a> are at the forefront of harnessing the potential of artificial intelligence to transform pulmonary medicine. By embracing these cutting-edge technologies, pulmonologists can usher in a new era of precision, efficiency, and patient-centric care.&nbsp;

The digital future of pulmonology is here, and AI is poised to impact the fight against lung cancer. With its ability to detect IPNs earlier, enhance clinical confidence, guide bronchoscopy interventions, and improve patient outcomes. As we stand on the precipice of this transformative era, it is an exciting time where innovation and dedication converge to shape a brighter, healthier tomorrow.&nbsp;

Fill out the form to connect with us! 

large_1000122754.webp

medium_1000122754.webp

small_1000122754.webp

thumbnail_1000122754.webp

1000122754.webp

How AI can ‘Pack a Punch’ in Pulmonology 

Silicosis is a debilitating and often fatal lung disease caused by inhaling fine particles of crystalline silica, commonly found in industries such as mining, construction, and manufacturing. According to the World Health Organization (WHO), around 38 million workers worldwide are exposed to silica dust, and approximately 230,000 new cases of silicosis are diagnosed each year globally. This disease is particularly prevalent in countries with significant industrial activities, such as China, India, South Africa, and the United States.&nbsp;

Silicosis is a severe occupational health hazard that poses significant risks to workers. The inhalation of silica dust leads to inflammation and scarring of lung tissue, resulting in impaired lung function and reduced quality of life. The disease can progress to severe respiratory failure and increases the risk of other lung infections, including tuberculosis (TB). The International Labour Organization (ILO) estimates that silicosis causes more than 10,000 deaths annually worldwide. Additionally, the economic burden of silicosis is substantial, with healthcare costs and lost productivity amounting to billions of dollars each year.&nbsp;

Incurability and the Need for Early Diagnosis&nbsp;

Silicosis is currently incurable, making early detection and intervention critical. The disease progresses in stages, which may include acute silicosis, which can develop within a few months of high exposure to silica dust, accelerated silicosis and chronic silicosis, which is the most commonly encountered stage. Early diagnosis allows for timely medical intervention, such as removing the worker from exposure and providing supportive treatments to slow disease progression. Without early diagnosis, workers are at risk of rapid deterioration and severe health complications.&nbsp;

Lung Health Disease and the Radiologist Shortage&nbsp;

AI Solutions: The Role of AI-Powered Screening Tools&nbsp;

Given the shortage of radiologists and the complexity of diagnosing silicosis, AI solutions have emerged as vital tools in healthcare. AI-powered chest X-ray screening tools can analyse medical images with remarkable accuracy and speed, identifying patterns and anomalies that may be indicative of various lung conditions, including silicosis. These tools are designed to detect signs of lung diseases such as tuberculosis and lung cancer, but they can also incidentally identify features suggestive of silicosis, such as nodules, fibrosis, and other abnormalities.&nbsp;

AI algorithms are trained on vast datasets of annotated medical images, enabling them to recognize subtle signs of disease that might be missed by human eyes, especially in the early stages. These tools can provide preliminary diagnoses or flag suspicious cases for further review by radiologists, thus enhancing the diagnostic process and reducing the workload on healthcare professionals.&nbsp;

For instance, in occupational health screenings where workers are regularly tested for lung diseases, AI-powered tools can serve as a first line of defence, ensuring that potential cases of silicosis are not overlooked. By integrating these AI solutions into routine healthcare practices, it is possible to achieve more timely and accurate diagnoses, which is crucial for early intervention and better health outcomes for workers at risk.&nbsp;

Addressing the challenge of silicosis requires a multifaceted approach. Governments and industries must enforce stringent occupational safety regulations to minimize silica dust exposure. At present, several mining companies do take precautionary measures to prevent silicosis among their workers:&nbsp;

However, challenges remain in fully implementing these preventive measures, such as the high cost, lack of awareness, and underreporting of silicosis cases. Continued efforts by mining companies, regulators, and workers are needed to eliminate this preventable occupational disease.&nbsp;

Increasing investment in healthcare infrastructure and training more radiologists are essential steps to address the current shortage. Furthermore, integrating AI solutions into routine diagnostic practices can significantly enhance early detection and improve patient outcomes. By combining these strategies, we can mitigate the impact of silicosis and protect the health of workers globally.&nbsp;

<a href="https://www.mining-technology.com/features/feature130267/" target="_blank">https://www.mining-technology.com/features/feature130267/</a>&nbsp;&nbsp;

<a href="https://occup-med.biomedcentral.com/articles/10.1186/s12995-023-00379-1" target="_blank">https://occup-med.biomedcentral.com/articles/10.1186/s12995-023-00379-1</a>&nbsp;&nbsp;

<a href="https://nhrc.nic.in/sites/default/files/NHRC_Interventions_on_Silicosis_27122016.pdf" target="_blank">https://nhrc.nic.in/sites/default/files/NHRC_Interventions_on_Silicosis_27122016.pdf</a>&nbsp;&nbsp;

<a href="https://www.msha.gov/sites/default/files/Alerts%20and%20Hazards/HH%20Cards/HH-7%20Tips%20for%20Preventing%20Silicosis.pdf" target="_blank">https://www.msha.gov/sites/default/files/Alerts%20and%20Hazards/HH%20Cards/HH-7%20Tips%20for%20Preventing%20Silicosis.pdf</a>&nbsp;&nbsp;

<a href="https://www.cdc.gov/niosh/docs/92-107/default.html" target="_blank">https://www.cdc.gov/niosh/docs/92-107/default.html</a>&nbsp;

World Health Organization (WHO). Silicosis: Global burden and epidemiology (<a href="https://www.who.int/health-topics/silicosis#tab=tab_1" target="_blank">WHO</a>)&nbsp;

International Labour Organization (ILO). Silicosis: A major occupational health issue. (<a href="https://www.ilo.org/global/topics/safety-and-health-at-work/lang--en/index.htm" target="_blank">ILO</a>)&nbsp;&nbsp;

<a href="https://www.sciencedirect.com/science/article/pii/S2666623521000325" target="_blank">https://www.sciencedirect.com/science/article/pii/S2666623521000325</a>&nbsp;

<a href="https://www.mdpi.com/2227-9032/11/16/2260" target="_blank">https://www.mdpi.com/2227-9032/11/16/2260</a>&nbsp;&nbsp;

large_image.webp

medium_image.webp

small_image.webp

thumbnail_image.webp

image.webp

Silicosis: An Urgent Occupational Health Hazard 

Lung cancer is the <a href="https://www.who.int/news-room/fact-sheets/detail/lung-cancer" target="_blank">leading cause</a> of cancer-related deaths worldwide, with <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5627048/" target="_blank">approximately 75% of patients</a> detected in late-stage disease. A later diagnosis means that the opportunity for therapeutic or surgical intervention is low, and survival is limited. However, early detection has the potential for improved survival, <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7950268/" target="_blank">over 90%</a> if treatment starts at cancer stage 1.&nbsp;

Traditional lung cancer screening initiatives have helped tackle rising cancer rates since their inception, but calling specific patient cohorts for screening based on age or smoking history is only part of the lung cancer detection jigsaw.&nbsp;

Casting the net wider in lung cancer detection&nbsp;

AI can be a highly accurate tool for opportunistic lung cancer surveillance. According to a study published in the journal <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689183/" target="_blank">Diagnostics</a>, Qure’s qXR chest X-ray solution achieved a high negative predictive value of 99.8% for detecting lung nodules. It also demonstrated an impressive 91.5% AUC (Area Under Curve) score for identifying lung nodules, showcasing its potential to accurately detect early signs of lung cancer from routine X-rays.&nbsp;

AI to inspire the redesign of patient pathways&nbsp;

A <a href="https://www.qure.ai/news_press_coverages/major-milestone-met-in-ai-powered-fight-against-lung-cancer" target="_blank">partnership between Qure and AstraZeneca</a>, the global biopharmaceutical company, has made considerable progress in the fight against lung cancer on a global scale. This collaboration is testament to the transformative potential of AI, innovating beyond traditional lung cancer screening profiles and improving patient outcomes on a large scale. It's a glimpse into the future of healthcare.&nbsp;&nbsp;

Nipun Jain, Head of International A.Catalyst Network at AstraZeneca states, "Our partnership with Qure.ai represents a critical step in our commitment to the EDISON Alliance's 1 billion Lives Challenge. Lung cancer remains a major global health concern, and it is through applying AI to screen for lung cancer risk in millions of routine X-rays, that it will provide a low-cost, high-impact way of ensuring at-risk patients enter the right diagnostic and care pathway. This milestone exemplifies our A.Catalyst Network approach to expanding access to digital healthcare and driving sustainable change in the communities we serve."&nbsp;&nbsp;

Initial findings of <a href="https://www.nhsggc.scot/nhsggc-trials-ai-powered-x-rays-in-fight-against-lung-cancer/" target="_blank">RADICAL, a pioneering UK AI-powered trial to diagnose lung cancer earlier</a> from GP or primary care physician referred chest X-rays in an outpatient hospital setting, have also shown much to be optimistic about. Time to treatment turnarounds have reduced from 64 days to 25 by triaging chest X-rays with AI and referring patients for a CT scan within 24 hours. This saves considerable time to potentially improve patent outcomes and quality of life.&nbsp;&nbsp;

Professor David Lowe, Professor of Health Innovation at the University of Glasgow and Emergency Consultant at NHS Greater Glasgow and Clyde in Scotland states, “Worldwide healthcare systems have a significant challenge in the detection of lung cancer. At present, nearly 50% of patients present with advanced or stage 4 cancer, leading to poorer outcomes. If we can spot cancer earlier, by speeding up the time and accuracy of the 100,000 chest X-rays performed each year with our study at NHS Greater Glasgow &amp; Clyde, we can improve time to further imaging, and subsequent treatment. AI will help orchestrate benefits for the whole patient care pathway.”&nbsp;

AI to optimize oncology clinical trials and care&nbsp;&nbsp;

There is much potential for AI to also push the boundaries in clinical oncology care, research and pharmaceutical drug development / trials.&nbsp;&nbsp;

Recently, Qure.ai and Project Data Sphere, a nonprofit initiative of the CEO Roundtable on Cancer, announced <a href="https://www.qure.ai/news_press_coverages/qure-ai-and-project-data-sphere-combine-big-pharma-insights-with-deep-ai-to-drive-cancer-care-forward" target="_blank">a partnership to augment tumor assessments using AI</a>. The collaboration aims to increase efficiency and consistency in evaluating the effectiveness of cancer treatments. The focus of the collaboration is ‘autoRECIST’. It will address the critical need for automating and standardizing tumor response assessments. Initiated in consultation with the US FDA and stakeholders from across the pharmaceutical industry and academia, it will aim to enhance cancer treatments and research.&nbsp;&nbsp;&nbsp;

AI for discussion at ASCO &amp; ESMO 2024

Qure’s end-to-end AI-powered lung cancer care portfolio includes CE and FDA certified solutions to support the early identification, precise measurement, efficient management and monitoring of lung nodules. These help to address critical gaps in healthcare plus propel developments in the pharmaceutical or life sciences industry.&nbsp;&nbsp;

For example, the AI solution <a href="https://www.qure.ai/product/lung-nodule-management" target="_blank">qXR-LN</a> (lung nodule detection) has <a href="https://appliedradiology.com/Articles/qure-ai-nets-fda-clearance-for-ai-powered-chest-x-ray-lung-nodule-solution" target="_blank">FDA clearance</a> for identifying and localizing lung nodules on chest X-rays. It also shows malignancy-risk scores and can serve as a crucial second reader for physicians, assisting in the review of frontal chest radiographs to assist with referral into CT pathways. In a study involving 40 sites across the United States, qXR-LN achieved an impressive AUC of 94% for nodule detection.&nbsp;

As the global oncology community converges on Chicago then Barcelona to share and discuss the latest clinical cancer research and innovations impacting patient care at the 2024 American Society of Clinical Oncology (ASCO) meeting, 31 May to 4 June and European Society for Medical Oncology (ESMO), 13 to 17 Sept, opportunities for AI abound. Early detection and treatment advances using digital health innovations will drive research techniques and improve patient quality of life – and this matters in the global fight against lung cancer.

Want to meet at ESMO??

large_IMG_0414.webp

medium_IMG_0414.webp

small_IMG_0414.webp

thumbnail_IMG_0414.webp

IMG_0414.webp

Why Early Lung Cancer Detection Matters

AI to optimise lung cancer pathways&nbsp;

A recent roundtable discussion, chaired by Professor Sir Mike Richards, Chair of the UK National Screening Committee brought together experts to discuss the <a href="https://newmarket-strategy.com/wp-content/uploads/2023/12/Transformation_of_the_lung_cancer_pathway.pdf" target="_blank">transformation of the lung cancer pathway in the NHS</a>, highlighting the crucial role of AI and other digital health innovations to redesign healthcare. It suggested that new technologies should be seen as an opportunity to streamline existing processes and championed solutions that detect lung nodules from chest X-rays or determine malignancy risk or monitor growth via CT, particularly when used in partnership with Cancer Alliances and NHS Trusts. This approach improves early detection rates and streamlines the diagnostic process, reducing the burden on healthcare professionals.&nbsp;

Many high-profile UK studies are now looking into improving the diagnostic pathway for lung cancer:&nbsp;

Furthermore, a <a href="https://www.qure.ai/news_press_coverages/major-milestone-met-in-ai-powered-fight-against-lung-cancer" target="_blank">pioneering partnership between Qure and AstraZeneca</a> has made considerable progress in the fight against lung cancer on a global scale. This collaboration is testament to the transformative potential of AI, innovating beyond traditional lung cancer screening profiles and improving patient outcomes on a large scale. It's a glimpse into the future of healthcare.&nbsp;&nbsp;

The latest figures suggest 2 million people in 28 countries have been screened using Qure's AI-powered chest X-ray analysis for the incidental detection of high-risk lung nodules. This proactive lung health AI commitment casts the net wider, beyond traditional lung cancer screening based on age or smoking history profiles. The initiative has found over 220,000 lung nodules, identifying close to 3,000 potential early lung cancer cases.&nbsp;&nbsp;

AI to create triage efficiencies for neurocritical CT&nbsp;

AI can also help healthcare professionals make timely decisions and ensure that patients with Traumatic Head Injuries (TBI) or suspected Strokes receive prompt attention. This is particularly important in the context of triage in acute or frontline emergency care where high patient numbers and resource shortages can impact timely patient care.&nbsp;&nbsp;

The ACcEPT (Assess the Clinical Effectiveness in Prioritising CT Heads) study commenced during winter 2023/24 at NHS Greater Glasgow and Clyde, the first of four planned NHS sites across the UK, utilising <a href="https://www.qure.ai/product/qer" target="_blank">qER, Qure’s head CT AI</a>. In the first initial weeks, qER analysed 651 non-contrast head CTs, detecting 128 head injuries including cranial fractures, intracranial haemorrhage (ICH), mass effect and mid-line shift within the brain. The intracranial pathologies were identified and prioritised by the qER AI solution as critical findings for radiologist review, confirmation, treatment planning and / or discharge out of the Emergency Department.&nbsp;

Tackling NHS acute imaging workloads through teleradiology has also been powered by AI to redesign processes and flow. Medica, a leading UK teleradiology provider, uses qER as its <a href="https://www.qure.ai/impact_stories/medica-qure-partnership" target="_blank">24/7 emergency reporting diagnostic tool to support the delivery of acute care</a> in many NHS Trusts.&nbsp;&nbsp;

Redesigning healthcare for better patient outcomes&nbsp;

Optimising lung cancer pathways and creating triage efficiencies for neurocritical CT with the power of diagnostic AI will help redesign care and treatment pathways. The key to AI activation and deployment will be the cross collaboration of stakeholders to share the challenges, champion the successes and steer the NHS ecosystems safely and securely into the future of improved patient care.&nbsp;&nbsp;

Meet Qure.ai this summer to hear more about how AI can redesign healthcare. We’ll be at <a href="https://www.ukio.org.uk/" target="_blank">UKIO</a> in Liverpool, 10-12 June 2024, stand B8/9 and at the <a href="https://www.nhsconfedexpo.org/" target="_blank">NHS ConfedExpo</a> in Manchester, 11-12 June 2024 stand D5a.&nbsp;

large_Optimising LC and Neuro.webp

medium_Optimising LC and Neuro.webp

small_Optimising LC and Neuro.webp

thumbnail_Optimising LC and Neuro.webp

Optimising LC and Neuro.webp

Optimising Lung Cancer Pathways and Neurocritical CT Triage with AI  

Large Language Models (LLMs) are becoming more common in healthcare. This is changing medical informatics, diagnosis, and patient care significantly. These models, trained on extensive datasets, offer unprecedented capabilities in understanding and processing complex medical information.&nbsp;&nbsp;

Adjusting these models for specific diseases, like lung cancer, shows a tailored approach to healthcare analytics. This ensures high accuracy and relevance in their results. LLMs can greatly improve the work life of healthcare workers. This, in turn, can raise the quality of care for patients.

Foundational Models and Fine-Tuning in Healthcare&nbsp;

Foundational models serve as the bedrock for specialized LLMs in healthcare. These models are trained on a vast corpus of data, encompassing a broad spectrum of knowledge. This comprehensive training, as suggested by <a href="https://arxiv.org/abs/2302.14691" target="_blank">research from Cornell University</a>, equips them with a general understanding of language and concepts, making them versatile in various applications. However, the versatility of foundational models is a double-edged sword. While they possess extensive knowledge, the breadth of their training can sometimes lead to diluted expertise in specific domains, such as medical specializations. This is where the concept of fine-tuning becomes paramount.&nbsp;

Expanding Roles of LLMs in Medical Fields&nbsp;

The role of LLMs in healthcare is multifaceted and continually evolving.  LLMs can assist healthcare professionals in diagnosis by providing rapid, data-driven insights. They can analyze patient data, compare it with vast medical records, and suggest possible diagnoses or flag potential issues requiring further investigation. This capability is precious in areas with limited access to specialist medical knowledge or where healthcare systems are overwhelmed.&nbsp;

These models can recommend tailored treatment plans by analyzing a patient's medical history, genetic information, and current health status. This approach could lead to more effective treatments with fewer side effects, as the recommendations are based on a comprehensive analysis of relevant data. LLMs also hold significant promise in medical research, particularly in understanding complex diseases and developing new treatments. By processing and analyzing vast amounts of research data, these models could identify patterns, propose hypotheses, and could even in the future predict the efficacy of new drugs. This could accelerate the pace of medical research, potentially leading to quicker discoveries and innovations in healthcare.&nbsp;

Another critical area where LLMs can contribute is in public health. Models trained on epidemiological data could aid in predicting disease outbreaks, understanding transmission patterns, and formulating public health strategies. This is particularly relevant during global health crises, such as the COVID-19 pandemic, where timely and data-driven responses are crucial.&nbsp;

LLMs: Bridging Gaps and Setting New Standards in Healthcare&nbsp;

According to the journal <a href="https://www.nature.com/articles/s43856-023-00370-1" target="_blank">Nature</a>, LLMs have demonstrated considerable potential in enhancing patient care through their ability to augment core medical competencies. Notably, these models possess substantial semantic medical knowledge, enabling them to engage in medical reasoning. This capability is exemplified by their performance in medical licensing exams, indicating a proficiency that can be further enhanced through fine-tuning with specific medical data.&nbsp;&nbsp;

Furthermore, one of the transformative applications of LLMs in healthcare is improving communication between healthcare staff and patients. By simplifying complex medical jargon and facilitating translations, LLMs can bridge language barriers and make medical knowledge more accessible. This is particularly significant for conditions associated with social stigma, where digital tools like chatbots can offer discreet and informative assistance. The ongoing advancement of LLMs promises to overcome current technical constraints and limited acceptance by healthcare practitioners.&nbsp;

Another critical role of LLMs is in easing the documentation burden on clinicians. By converting unstructured notes into structured formats, LLMs can streamline administrative tasks, allowing healthcare providers to devote more time to patient care. This functionality can extend to clinical trials, where accurate and efficient documentation is crucial. In medical research, LLMs can democratize access to scientific evidence while presenting risks of misinformation and facilitating scientific misconduct. Their ability to summarize and critically appraise up-to-date, high-quality, peer-reviewed evidence can significantly aid researchers. However, the quality of these summaries depends on the training data.&nbsp;&nbsp;

The integration of LLMs in medical education heralds a shift in teaching methodologies. While they can serve as valuable tools in providing customized educational content and facilitating interactive learning experiences, there is a risk of undermining critical thinking skills.&nbsp; Integrating LLMs into healthcare is an exciting development, heralding a new era of innovation and efficiency in medical practice. These models offer remarkable potential to enhance medical knowledge, facilitate communication, and optimize administrative processes. Their application spans various aspects of healthcare, promising significant improvements in patient care, research, and education.&nbsp;

This technological advancement represents a substantial leap forward in making healthcare more accessible and practical. With their sophisticated data processing and analysis capabilities, LLMs are poised to play a crucial role in the future of medicine. As we continue to explore and harness the capabilities of LLMs, the prospects for their contribution to healthcare are boundless. This innovative time holds great promise for improving the quality of patient care and advancing medical knowledge, setting a new standard for healthcare in the digital age.&nbsp;

large_image (15).png

medium_image (15).png

small_image (15).png

thumbnail_image (15).png

image (15).png

Role of Large Language Models (LLMs) in Healthcare

<a href="https://www.auntminnie.com/imaging-informatics/artificial-intelligence/article/15623418/6-ways-ai-will-enhance-musculoskeletal-imaging" target="_blank">Musculoskeletal (MSK) radiology</a> is rapidly evolving, with artificial intelligence (AI) playing an increasingly important role. AI-powered tools can <a href="https://jmai.amegroups.org/article/view/5742/html#:~:text=AI%20and%20ML%20advances%20for,by%20potentially%20improving%20patient%20outcomes." target="_blank">improve the efficiency, accuracy, and accessibility of MSK imaging</a> while providing new insights into diagnosing and treating MSK conditions.&nbsp;

<a href="https://mss-ijmsr.com/artificial-intelligence-in-musculoskeletal-radiology-past-present-and-future/#:~:text=Based%20on%20the%20studies%20discussed,this%20field%20%5BTable%201%5D." target="_blank">One of the most essential benefits of AI in MSK radiology</a> is its ability to improve diagnostic accuracy.&nbsp;&nbsp;

AI is also making MSK imaging more accessible to patients. Radiologists can use AI to develop new imaging protocols that reduce scan time and radiation exposure. This is particularly important for patients who require frequent imaging, such as those with chronic MSK conditions.&nbsp;

The relationship between AI and MSK radiology is anchored in <a href="https://pubmed.ncbi.nlm.nih.gov/33983500/#:~:text=Artificial%20intelligence%20and%20deep%20learning,Keywords%3A" target="_blank">myriad applications that span</a> image reconstruction, tissue segmentation, workflow augmentation, and disease detection​.&nbsp;

The following outlines the significant avenues through which AI is sculpting the future of MSK radiology.&nbsp;

AI, particularly deep learning, has driven innovation in osteoarthritis, advancing image classification, lesion detection, cartilage segmentation, and beyond.

One of the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416509/#:~:text=Magnetic%20resonance%20imaging%20,for%20accelerated%20MRI" target="_blank">significant drawbacks</a> of MRI as a diagnostic tool has been the lengthy data acquisition time. However, AI has addressed this challenge head-on by accelerating MRI acquisition and reconstruction, thus reducing the long acquisition times that were previously a bottleneck.&nbsp;&nbsp;

This acceleration is crucial for large-scale longitudinal studies and emergency diagnostics where time is of the essence​​.&nbsp;

The 3D nature and soft-tissue contrast of MRI, which are indispensable for osteoarthritis research, are being <a href="https://www.nature.com/articles/s41584-021-00719-7#:~:text=The%203D%20nature%20and%20soft,The%20recent" target="_blank">significantly enhanced</a> with the application of AI. AI facilitates the elucidation of disease pathogenesis and progression, making MRI an invaluable tool in osteoarthritis research. This enhancement in imaging quality provides a clearer view of the anatomical structures and the pathological changes occurring in osteoarthritis​.&nbsp;

AI enables the automated detection of anatomically relevant health markers at the hip, knee, and ankle. This facilitates radiological monitoring of the progression of <a href="https://link.springer.com/referenceworkentry/10.1007/978-3-030-64573-1_292#:~:text=AI%20relies%20on%20deep%20learning,as%20osteoarthritis%20of%20the%20knee" target="_blank">various bone diseases</a>, such as knee osteoarthritis, thus providing a longitudinal view​.&nbsp;

The application of AI, particularly machine learning and deep learning, is known to improve multiple stages of MRI, including acquisition, processing, and post-processing steps. This, in turn, minimizes the chances of diagnostic errors and enhances the overall efficacy and efficiency of osteoarthritis research​​.&nbsp;

Enriching Disease Understanding and Diagnosis&nbsp;

The <a href="https://pubs.rsna.org/doi/full/10.1148/ryai.2019180057" target="_blank">automated models</a> developed through AI have shown promise in staging knee osteoarthritis severity from radiographs, where the performance of these models has been comparable to that of musculoskeletal radiologists.&nbsp;

This automation ensures high diagnostic accuracy, critical for determining the appropriate treatment interventions.&nbsp;

Using AI and machine learning algorithms, <a href="https://www.nih.gov/news-events/nih-research-matters/machine-learning-detects-early-signs-osteoarthritis" target="_blank">researchers</a> have identified early signs of osteoarthritis three years before diagnosis through MRI scans. This capability is essential for understanding the disease's developmental trajectory and identifying individuals who would likely benefit from early interventions.&nbsp;

AI improves the overall quality and efficiency of imaging, which is crucial for a more accurate diagnosis. It aids in assessing the appropriateness of imaging orders, thereby improving image quality, patient centricity, imaging efficiency, and, eventually, diagnostic accuracy.&nbsp;&nbsp;

Particularly in cases of advanced knee osteoarthritis requiring surgical intervention like Total Knee Arthroplasty (TKA), <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296289/" target="_blank">AI and machine learning modeling</a> serve as indispensable tools in pre-surgical planning, patient selection, prediction of disease progression, and estimation of treatment outcomes. This is critical for tailoring the surgical approach to the individual needs and conditions of the patient, ensuring optimal post-surgical results.&nbsp;

<a href="https://www.semanticscholar.org/paper/Studying-osteoarthritis-with-artificial-to-applied-Kovacs/2a6c6e9b3a1e8e7eae930f0f432c0e8d5a14d097" target="_blank">Deep learning methods</a> have been employed to improve the understanding of osteoarthritis.&nbsp;&nbsp;

AI transcends conventional diagnostic paradigms by facilitating a more comprehensive understanding of osteoarthritis by discovering novel image features indicative of short-term and long-term progression. This multi-faceted insight is crucial for a more patient-specific prognostic approach, ensuring the diagnostic deliberations are nuanced and rooted in a deeper understanding of the disease.&nbsp;

Enhancing Workflow and Clinical Operations&nbsp;

AI is identified as a pivotal tool that could <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7881801/" target="_blank">significantly affect every step</a> in the imaging value chain. The early integration of AI into radiology has already demonstrated its potential value, particularly in MSK imaging. The journey begins with placing the imaging requisition, traversing the entire imaging cycle, and ensuring well-oiled operational machinery.&nbsp;

The benefits of AI extend to workflow support, which is paramount for optimizing the efficiency and productivity of radiological practices. AI and deep learning offer multiple avenues of workflow support, including image reconstruction and transformation, tissue segmentation, and disease detection, which are intrinsic to <a href="https://www.semanticscholar.org/paper/Deep-learning-and-its-applications-to-machine-Pelt-Goh/eaed97bd8e46646dcbf35d32a98a4009c9a1d5e1" target="_blank">MSK radiology</a>.&nbsp;

Specifically, in spine imaging—a crucial part of MSK radiology—the incorporation of AI has led to significant advancements. Innovations rooted in AI and big data have considerably impacted musculoskeletal radiology, hinting at a future where a holistic approach could redefine every element of the <a href="https://pubs.rsna.org/doi/10.1148/ryai.2019180057" target="_blank">radiology service value chain, from request to report</a>.&nbsp;

From the broader radiology perspective, AI applications are being developed to span all steps of the imaging chain, from test ordering to report communication.&nbsp;

AI applications' traction across the radiology space underscores a promising horizon where workflow and delivery are redefined, thereby catalyzing a more efficient and streamlined operational framework in <a href="https://pubs.rsna.org/doi/10.1148/ryai.2019180057" target="_blank">MSK radiology</a>.&nbsp;

Amidst the evolving landscape of MSK radiology, the emergence of specialized AI tools drives nuanced advancements in diagnostic accuracy and operational efficiency.&nbsp;&nbsp;

A notable example technologically in this trend is Qure.ai's <a href="https://www.qure.ai/product/qmsk" target="_blank">qMSK software</a>, engineered to augment the detection and localization of bone fractures and joint dislocations across multiple body regions on MSK X-rays, delivering insights in under a minute​.&nbsp;

The comprehensive nature of qMSK extends to accommodating 15 anatomical views, thereby fostering a holistic understanding of potential musculoskeletal anomalies. This breadth of analysis is executed with a processing time of less than 20 seconds, testifying to the software's computational alacrity. Having been trained on over one million scans, qMSK exudes a sensitivity exceeding 0.9 in detecting signs of fractures, underscoring its diagnostic precision​​.&nbsp;

Moreover, qMSK is engineered to transcend conventional diagnostic thresholds by mitigating the 'satisfaction of search' bias - a phenomenon where the identification of one anomaly may overshadow or inhibit the detection of others.&nbsp;&nbsp;

By facilitating the recognition of multiple fractures, qMSK amplifies the diagnostic gaze, fostering a more comprehensive understanding of the patient's condition.&nbsp;

This capability is further enhanced by qMSK's proficiency in working across multiple views, thereby reducing diagnostic errors associated with overlooking subtle fractures​​.&nbsp;

The clinical journey of a patient in MSK radiology often necessitates a longitudinal examination of their condition, particularly in the context of fracture healing.&nbsp;

qMSK is tailored to track the progress in fracture healing, thus providing a dynamic diagnostic narrative pivotal for personalized patient care. Furthermore, the software significantly curtails the processing time to under 20 seconds per scan, translating to substantial time savings and augmented workflow efficiency​when compounded over numerous cases.&nbsp;

In a domain where every second can be critical, qMSK's deployment flexibility resonates profoundly.&nbsp;

The software can be deployed on the cloud or on-premises, offering a versatile solution that aligns with the varied infrastructural and operational frameworks across different healthcare settings​​.&nbsp;

The articulation of qMSK's capabilities reflects a broader narrative of how AI bridges the technical and operational difficulties in MSK radiology by enhancing diagnostic acuity, streamlining the workflow, and catalyzing a more patient-centric approach.&nbsp;

Regulatory Recognition and Trajectory Forward&nbsp;

The institutional recognition of AI's efficacy is highlighted by the increasing number of AI products being cleared by the Food and Drug Administration (FDA) for utilization in clinical radiology, encompassing tasks such as quantification, identification, and even diagnosis.&nbsp;

This legitimizes AI's role in clinical radiology and hints at a trajectory of deeper integration shortly, with a <a href="https://pubmed.ncbi.nlm.nih.gov/37087138/#:~:text=There%20is%20a%20rapidly%20increasing,in%20neuroimaging%20and%20musculoskeletal%20radiology" target="_blank">broader spectrum of AI applications awaiting regulatory approval for clinical deployment</a>​​.&nbsp;

Integrating AI in MSK radiology represents an enhancement and a fundamental transformation in diagnosing and treating musculoskeletal disorders.&nbsp;&nbsp;

This transformative alliance between AI and MSK radiology sets the stage for marked improvements in diagnostic precision, operational fluidity, and patient-centric care quality.&nbsp;

large_MSK radiology.webp

medium_MSK radiology.webp

small_MSK radiology.webp

thumbnail_MSK radiology.webp

MSK radiology.webp

The Importance of AI in MSK Radiology

According to the <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1071277/" target="_blank">Western Journal of Medicine</a>, snowboarding injury rates have significantly increased, ranging from 3.5 to 40 per 1,000 snowboarding days. &nbsp;

Insights onArtificial Intelligence &Healthcare Technology

Insights on
Artificial Intelligence &
Healthcare Technology