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New Tech Allows Faster Breast Cancer Detection In Middle East

Breast cancer is the most common form of the disease for women in the Arab world. But now, AI screening solutions, precision medicine and molecular imaging are fighting back.

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An estimated 2.26 million women worldwide are diagnosed with breast cancer each year. In the Arab world, nearly 20% of all new cases turn out to be breast cancer, making this form of the disease the most common for women in the region.

To tackle this problem, several medical tech companies have developed screening tests to avoid misdiagnosis and late diagnosis. From AI to 3D imaging, each solution strives to improve the screening process, making it more accessible and affordable to Middle Eastern patients.

Early Detection Improvements

Mammogram diagnostics have evolved dramatically over the years. 3D scanning allows oncologists to detect small masses in dense breast tissue much earlier than traditional imaging tools allow.

“Using 3D mammograms, we can see lumps hidden within breasts accurately. By limiting the effect of covering the breast tissue, 3D mammography can make tumor detection easier. Looking into various pictures has helped specialists discover a larger number of cancer growths which was not possible with 2D scans,” says Dr. Timor Al-Shee, Surgical Consultant of Breast Oncology, King Faisal Specialist Hospital and Research Center, Saudi Arabia.

Despite their advancements, 3D mammograms are costly and still risk the possibility of false-positive results. To minimize unnecessary biopsies and increase the accuracy of decisions, researchers from New York University and NYU Abu Dhabi have devised a method to identify cancers using AI.

Devised by a team led by Farah Shamout, Yiqiu Shen and Jamie Oliver, the AI tool offers “radiologist-level accuracy” and promises to improve the consistency and efficiency of ultrasound diagnosis.

So far, the findings have been promising, with AI able to play a complementary role as a decision-making tool during the early stages of screening, aiding clinicians when forming a diagnosis.

Genetic Testing And Molecular Imaging

As well as 3D imaging and AI, genetic testing can also achieve reliable and accurate results. Although most breast cancers are not thought to be caused by inherited mutations, the tests can be helpful for women with a family history of breast cancer.

“The UAE uses the latest technologies to drive innovation in healthcare. We are part of the DoH-led Personalized Precision Medicine Programme for oncology in the region that specifically targets breast cancer. The treatment is based on a patient’s genetic makeup and genetic changes in cancer cells,” says Dr. Fahed Al Marzooqi, COO of G42 Healthcare.

Molecular breast imaging, on the other hand, can be used alongside a mammogram and involves a radioactive tracer with a nuclear medicine scanner. The tracer is injected into a vein, and if cancer cells are detected, the tracer will light up.

As well as helping to diagnose cancers earlier, these new technologies could also be used to tailor precision medicines for treatment. Scientists already know that breast cancer is treatable if spotted early, so it seems that the future of cancer medicines is all about evaluation — from genes and environment to lifestyle factors. Meanwhile, technological advances are beginning to allow oncologists to tailor highly individual treatment plans for patients.

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ESET Bets On Specialized Security AI At 0.5% The Training Cost

ESET says NeoCortex can estimate an attacker’s next move, with a kill switch and human oversight built in to keep it from going rogue.

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eset bets on specialized security ai at 0.5% the training cost

A sign-in anomaly, a suspicious process launch and an unusual connection may look harmless alone. Together, they can describe an intrusion that is hard for security to teams to piece together.

To solve that problem, ESET announced ESET NeoCortex on November 4, 2026: a specialized foundational AI model for cybersecurity. It powers ESET LiveCortex, available within the ESET PROTECT Platform, which connects fragmented security signals and turns them into actionable incidents.

Filtering is the main selling point, with LiveCortex processing billions of raw telemetry events daily, and just 0.00111% of them contributing to incidents presented to customers. The aim is less alert fatigue and more analyst time spent on threats that matter.

NeoCortex is built on world-model architecture, learning how digital environments behave and how events relate. ESET says that lets it reconstruct missing incident context, recognize similar past cases and estimate an attacker’s likely next step – what the company calls “minus-one-day defense,” a chance to intervene before the next stage unfolds. ESET points to Black October, the week leading up to October 25, 2026, when critical security issues affecting PHP, Linux, GitLab and other key systems demanded rapid, prioritized responses, as the kind of moment where that context matters.

ESET’s internal research also suggests a specialized model like NeoCortex can be trained at up to 200 times lower cost than a large general reasoning model, while delivering more accurate, defense-focused results. The company credits optimized training, a focus on cybersecurity vocabulary, lower computing requirements and privately owned hardware.

Also Read: Dubai Says Its Deepfake Detector Needs Just 7 Seconds Of Video

“AI is accelerating both cyberattacks and cyberdefenses. With ESET NeoCortex, we are applying our AI investment to develop specialized foundational cybersecurity intelligence that remains under our control and strengthens, rather than replaces, human expertise,” said Juraj Malcho, Chief Technology Officer at ESET.

As part of the design, NeoCortex forms part of a layered architecture combining specialized models, deterministic controls, correlation logic, validation, auditability and expert human oversight. Safeguards include restricted sandboxes, read-only tools, continuous monitoring, defined usage and token limits, a kill switch and human oversight of critical response decisions. The model is also developed and self-hosted on ESET’s own infrastructure, which the company says reduces exposure to shifts among third-party AI providers, including commercial and geopolitical ones.

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