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Tuesday, February 17, 2026 9:30 PM

Medical AI

AI boosts breast cancer detection in routine scans, landmark trial shows

Artificial intelligence can significantly improve the detection of breast cancer during routine screening, according to findings from a world-first clinical trial released on Friday. The results point to AI as a potential solution to both diagnostic gaps and rising workload pressures faced by radiologists globally. Published in The Lancet, the study is the first completed randomised controlled trial to rigorously evaluate AI-assisted breast cancer screening. Conducted in Sweden, the trial followed more than 100,000 women who underwent routine mammography in 2021 and 2022. Participants were randomly divided into two groups. One group had their scans reviewed by a single radiologist supported by an AI system, while the other followed the standard European protocol of double reading by two radiologists. The outcomes showed that the AI-assisted approach identified 9% more cancer cases than the conventional method. Importantly, over a two-year follow-up period, women in the AI-supported group had a 12% lower incidence of “interval cancers” — cancers detected between regular screening rounds, which are often more aggressive. The benefits were consistent across age groups and breast density levels, and the rate of false positives remained comparable between both groups. Kristina Lang, senior author of the study and a researcher at Lund University, said the findings indicate that large-scale adoption of AI-supported mammography could ease staffing pressures in radiology departments while improving early cancer detection. However, she emphasised that any rollout must be done carefully, with ongoing evaluation and oversight. Experts cautioned that AI should complement, not replace, human expertise. Jean-Philippe Masson, head of the French National Federation of Radiologists, noted that radiologists must validate AI-generated findings, as the technology can sometimes flag benign tissue changes as cancer. He also pointed out that high costs and concerns around overdiagnosis have slowed AI adoption in countries like France. Stephen Duffy, emeritus professor of cancer screening at Queen Mary University of London, who was not involved in the research, said the trial adds to growing evidence that AI-assisted screening is safe. However, he flagged that the reduction in interval cancers was not statistically significant and called for longer follow-up to assess whether outcomes between the two groups eventually converge. Earlier interim results from the trial, published in 2023, showed that AI nearly halved the time radiologists spent reviewing mammograms. The AI system used in the study, Transpara, was trained on over 200,000 mammography exams from 10 countries. Breast cancer remains the most commonly diagnosed cancer among women worldwide. According to the World Health Organization, more than 2.3 million women were diagnosed with the disease and around 670,000 died from it in 2022. Source: PTI

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JSS AHER Collaborates with Google Research to Advance AI-Powered Healthcare

In a significant leap for India’s medical research landscape, JSS Academy of Higher Education and Research (JSS AHER), Mysuru, has joined forces with Google Research on a pioneering artificial intelligence (AI) healthcare initiative. The outcomes of this collaboration were recently published in two prestigious papers in the journal Nature, underscoring the project’s global relevance and impact. At the heart of the research lies the development of the Articulate Medical Intelligence Explorer (AMIE) — an innovative AI system designed by Google Research to improve diagnostic precision and enhance communication between doctors and patients. The studies assessed AMIE’s capabilities in comparison with trained primary care physicians, using standardized, text-based medical consultations across healthcare systems in India, the UK, and Canada. Dr. B. Suresh, Pro-Chancellor of JSS AHER, expressed pride in the institution’s role in shaping the future of healthcare, stating, “We are at the cutting edge of digital health and AI innovation. In line with these advances, we have also revised our pharmacy curriculum to include AI, ensuring our graduates are equipped for the evolving landscape of medicine.” Echoing this vision, Vice-Chancellor Dr. H. Basavanagowdappa highlighted the institution’s commitment to fostering an innovation-centric academic environment. “This international collaboration enhances our academic standing and gives our students and faculty the opportunity to tackle real-world healthcare issues. Our mission is to develop forward-looking solutions for global health,” he said. With this partnership, JSS AHER cements its position as a leading hub for digital health research, merging academic excellence with cutting-edge technological collaboration. Source: Economic Times  

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AI Early Detection System Prevents Strokes by Identifying Atrial Fibrillation

News on Health

Researchers have harnessed the power of artificial intelligence (AI) to identify irregular cardiac rhythms in individuals who have not yet exhibited symptoms. This AI system, which detected hidden signals in routine medical diagnostic tests, offers a promising avenue for doctors to proactively prevent strokes and other cardiovascular problems in individuals with atrial fibrillation, the most common type of heart rhythm disorder. While previous algorithms were predominantly tested on a narrow demographic, this new AI algorithm demonstrates effectiveness across various situations and patient populations, including veterans and underserved communities in the United States. The groundbreaking findings have been published in JAMA Cardiology, a reputable peer-reviewed journal. Dr. David Ouyang, a cardiologist at Cedars-Sinai’s Smidt Heart Institute and a researcher in the Division of Artificial Intelligence in Medicine, is the senior author of this study. He emphasizes the significance of this research in identifying concealed heart conditions and promoting the development of equitable and universally applicable algorithms for all patients. According to experts, approximately one in three people with atrial fibrillation remains undiagnosed. In this condition, the heart’s electrical signals, responsible for orchestrating the flow of blood from the upper chambers to the lower chambers, become chaotic. This can result in blood pooling in the upper chambers, leading to the formation of blood clots that may travel to the brain and trigger an ischemic stroke. The core of this groundbreaking discovery lies in an AI algorithm that was trained to recognize patterns within electrocardiogram (ECG) readings. ECG is a diagnostic test that monitors the heart’s electrical activity, typically involving the placement of electrodes on a patient’s body to capture these signals. By analyzing nearly one million ECGs, the AI model was not only able to accurately predict the onset of atrial fibrillation within 31 days but also demonstrated its effectiveness when applied to medical records of patients at Cedars-Sinai. Dr. Sumeet Chugh, the director of the Division of Artificial Intelligence in Medicine and the medical director of the Heart Rhythm Center at the Department of Cardiology, highlights the study’s geographic and ethnic diversity in its sample of veterans. This diversity underscores the potential of this algorithm to benefit a broad spectrum of the U.S. population. The research is a testament to the innovative ways in which AI is being harnessed at the Smidt Heart Institute and the Division of Artificial Intelligence in Medicine to proactively manage complex and challenging cardiac conditions.

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