TAMESIDE Hospital has started using new technology to help doctors detect diseases quicker. The bosses at Tameside and Glossop NHS Integrated Care Foundation Trust – which runs the Ashton-based hospital – say the artificial intelligence (AI) tool will improve efficiency and the quality of care within lung cancer detection.
Other hospitals across Greater Manchester are adopting this innovative tool, which will soon be rolled out at seven NHS trusts across the region over the next few months. This development is part of a partnership between Greater Manchester Cancer Alliance, the Greater Manchester Imaging Network, and global health tech firm Annalise.ai.
The AI tool can detect up to 124 findings on chest radiographs using an AI-powered chest X-ray decision-support system. This means it can read chest X-rays more effectively. When it identifies potential lung cancer cases, the information is relayed to the medical provider within under a minute. This rapid response allows healthcare professionals to prioritize the review of suspicious chest X-rays, which can help detect – or rule out – cancer more quickly.
The tool is being integrated through Sectra Imaging, which is known for providing imaging IT solutions to health systems worldwide, and is funded through the Artificial Intelligence Diagnostics Fund. The urgency for quicker cancer detection is especially pronounced in Greater Manchester, where lung cancer rates are 24 percent higher than the national average and life expectancy is below the overall English average.
This initiative is part of broader efforts led by the Greater Manchester Cancer Alliance, aiming to improve cancer outcomes for the region’s 2.8 million residents. “We are proud to welcome this promising technology,” stated Dilraj Sandher, the Medical Director at Tameside and Glossop NHS Integrated Care Foundation Trust. “This tool is excellent for supporting our clinicians, allowing for early detection of lung cancer among patients.”
Chris Sleight, Chief Officer at Greater Manchester Imaging Network, expressed enthusiasm over the advancement: “We are thrilled to have secured funding to enable faster diagnosis of chest X-rays for our patients. This is significant progress toward improving the early detection of lung cancer and thereby enhancing patient prognosis.”
Meanwhile, across the globe, the conversation about AI’s role in disease detection continues. On November 21, 2024, Malaysian Health Minister Datuk Seri Dr. Dzulkefly Ahmad indicated the Health Ministry's openness to experimenting with artificial intelligence for cancer detection within public hospitals. He emphasized the importance of ensuring safety, effectiveness, and cost-efficiency before fully integrating these tools.
Dr. Dzulkefly noted, “AI technology holds significant potential for enhancing healthcare systems, particularly by diagnosing diseases like cancer at earlier stages. Our Malaysian Health Technology Assessment Section has already assessed several AI applications related to cancer detection and is evaluating AI’s effectiveness for lung cancer screenings.”
Field tests are currently underway at the National Cancer Institute, with plans to implement similar trials at Hospital Cyberjaya, Hospital Kajang, and Hospital Putrajaya. Initial findings from these assessments highlight the AI algorithms’ improved sensitivity and lower false-positive rates, especially among junior radiologists, and could serve as a secondary evaluation tool for enhancing accuracy.
Looking at advances closer to home, the University of Cincinnati Cancer Center is also tapping artificial intelligence to bolster early lung cancer detection. Featured recently on Local 12, experts discussed how AI is transforming their diagnostic capabilities. "It's like searching for one specific star among countless others,” explained Dr. Sangita Kapur, highlighting how AI software 'cleans up' CT scan images, emphasizing potentially cancerous nodules.
Not only does this cutting-edge system speed up result delivery, but it also boosts the likelihood of identifying early cancer signs among those at risk. UC’s approach follows the guidelines from the United States Preventive Services Task Force, which recommends annual screenings for lung cancer through low-dose computed tomography for adults aged 50 to 80 who have significant smoking histories.
Meanwhile, research published in ERJ Open Research reveals ground-breaking findings where analyzing urine samples with artificial intelligence can foresee flare-ups in chronic lung disease patients. This method allows patients to conduct daily dipstick tests, reminiscent of rapid lateral flow tests, and share the results digitally with healthcare professionals. It gives the remarkable capability to predict exacerbations up to seven days before symptoms emerge, which could drastically tailor treatment and possibly reduce hospitalization rates.
Conducted on patients with chronic obstructive pulmonary disease (COPD), the study aimed at identifying molecular changes correlates with worsening symptoms.
