AI Research
AI could detect heart risk from breast screening images, study suggests
Artificial intelligence (AI) trained on images from routine breast screening appointments could help predict heart problems in women, a study suggests.
The technology could offer a “cost-effective, ‘two for one’” opportunity to screen women for both breast cancer and heart risks, experts said.
In the UK, anyone registered with a GP as female will be automatically invited for breast screening – known as a mammogram – every three years between the ages of 50 and 71.
At the appointment, two X-rays are taken of each breast to look for signs of cancer.
Researchers in Australia developed an AI algorithm based on mammogram images from 49,196 women enrolled on the Lifepool cohort registry, an Australian breast cancer research initiative.
The average age of the group was 59, with a third taking medication for high cholesterol and 27% for high blood pressure.
The aim of the technology was to predict the risk of major cardiovascular disease, such as heart attacks and strokes, over 10 years.
Researchers said: “Many women undergo screening mammography in midlife when the risk of cardiovascular disease rises.
“Mammographic features such as breast arterial calcification and tissue density are associated with cardiovascular risk.
“We developed and tested a deep learning algorithm for cardiovascular risk prediction based on routine mammography images.”
During an average tracking period of almost nine years, 2,383 women had a heart attack, 731 had heart failure and 656 had a stroke.
The study, published in the journal Heart, found the algorithm performed just as well as other traditional calculators that use age and clinical variables to assess heart risks.
Researchers added: “A deep learning algorithm utilising routine mammograms and age shows promise as a cardiovascular risk prediction tool.
“Mammography may offer a cost-effective ‘two for one’ opportunity to screen women for both breast cancer and cardiovascular risk, enabling broader cardiovascular risk screening for women than is currently achieved.”
AI Research
(Policy Address 2025) HK earmarks HK$3B for AI research and talent recruitment – The Standard (HK)
AI Research
[2506.08171] Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models
View a PDF of the paper titled Worst-Case Symbolic Constraints Analysis and Generalisation with Large Language Models, by Daniel Koh and 4 other authors
Abstract:Large language models (LLMs) have demonstrated strong performance on coding tasks such as generation, completion and repair, but their ability to handle complex symbolic reasoning over code still remains underexplored. We introduce the task of worst-case symbolic constraints analysis, which requires inferring the symbolic constraints that characterise worst-case program executions; these constraints can be solved to obtain inputs that expose performance bottlenecks or denial-of-service vulnerabilities in software systems. We show that even state-of-the-art LLMs (e.g., GPT-5) struggle when applied directly on this task. To address this challenge, we propose WARP, an innovative neurosymbolic approach that computes worst-case constraints on smaller concrete input sizes using existing program analysis tools, and then leverages LLMs to generalise these constraints to larger input sizes. Concretely, WARP comprises: (1) an incremental strategy for LLM-based worst-case reasoning, (2) a solver-aligned neurosymbolic framework that integrates reinforcement learning with SMT (Satisfiability Modulo Theories) solving, and (3) a curated dataset of symbolic constraints. Experimental results show that WARP consistently improves performance on worst-case constraint reasoning. Leveraging the curated constraint dataset, we use reinforcement learning to fine-tune a model, WARP-1.0-3B, which significantly outperforms size-matched and even larger baselines. These results demonstrate that incremental constraint reasoning enhances LLMs’ ability to handle symbolic reasoning and highlight the potential for deeper integration between neural learning and formal methods in rigorous program analysis.
Submission history
From: Daniel Koh [view email]
[v1]
Mon, 9 Jun 2025 19:33:30 UTC (1,462 KB)
[v2]
Tue, 16 Sep 2025 10:35:33 UTC (1,871 KB)
When artificial intelligence (AI) can help you retrieve literature, support your research, and even act as a “super assistant”, university education is undergoing a profound transformation.
On 9 September, XJTLU’s Centre for Knowledge and Information (CKI) hosted its third AI Learning Day, themed “AI-Empowered, Ecosystem-Co-created”. The event showcased the latest milestones of the University’s “Education + AI” strategy and offered in-depth discussions on the role of AI in higher education.
In her opening remarks, Professor Qiuling Chao, Vice President of XJTLU, said: “AI offers us an opportunity to rethink education, helping us create a learning environment that is fairer, more efficient and more personalised. I hope today’s event will inspire everyone to explore how AI technologies can be applied in your own practice.”
Professor Qiuling Chao
In his keynote speech, Professor Youmin Xi, Executive President of XJTLU, elaborated on the University’s vision for future universities. He stressed that future universities would evolve into human-AI symbiotic ecosystems, where learning would be centred on project-based co-creation and human-AI collaboration. The role of educators, he noted, would shift from transmitters of knowledge to mentors for both learning and life.
Professor Youmin Xi
At the event, Professor Xi’s digital twin, created by the XJTLU Virtual Engineering Centre in collaboration with the team led by Qilei Sun from the Academy of Artificial Intelligence, delivered Teachers’ Day greetings to all staff.
(Teachers’ Day message from President Xi’s digital twin)
“Education + AI” in diverse scenarios
This event also highlighted four case studies from different areas of the University. Dr Ling Xia from the Global Cultures and Languages Hub suggested that in the AI era, curricula should undergo de-skilling (assigning repetitive tasks to AI), re-skilling, and up-skilling, thereby enabling students to focus on in-depth learning in critical thinking and research methodologies.
Dr Xiangyun Lu from International Business School Suzhou (IBSS) demonstrated how AI teaching assistants and the University’s Junmou AI platform can offer students a customised and highly interactive learning experience, particularly for those facing challenges such as information overload and language barriers.
Dr Juan Li from the School of Science shared the concept of the “AI amplifier” for research. She explained that the “double amplifier” effect works in two stages: AI first amplifies students’ efficiency by automating tasks like literature searches and coding. These empowered students then become the second amplifier, freeing mentors from routine work so they can focus on high-level strategy. This human-AI partnership allows a small research team to achieve the output of a much larger one.
Jing Wang, Deputy Director of the XJTLU Learning Mall, showed how AI agents are already being used to support scheduling, meeting bookings, news updates and other administrative and learning tasks. She also announced that from this semester, all students would have access to the XIPU AI Agent platform.
Students and teachers are having a discussion at one of the booths
AI education system co-created by staff and students
The event’s AI interactive zone also drew significant attention from students and staff. From the Junmou AI platform to the E
-Support chatbot, and from AI-assisted creative design to 3D printing, 10 exhibition booths demonstrated the integration of AI across campus life.
These innovative applications sparked lively discussions and thoughtful reflections among participants. In an interview, Thomas Durham from IBSS noted that, although he had rarely used AI before, the event was highly inspiring and motivated him to explore its use in both professional and personal life. He also shared his perspective on AI’s role in learning, stating: “My expectation for the future of AI in education is that it should help students think critically. My worry is that AI’s convenience and efficiency might make students’ understanding too superficial, since AI does much of the hard work for them. Hopefully, critical thinking will still be preserved.”
Year One student Zifei Xu was particularly inspired by the interdisciplinary collaboration on display at the event, remarking that it offered her a glimpse of a more holistic and future-focused education.
Dr Xin Bi, XJTLU’s Chief Officer of Data and Director of the CKI, noted that, supported by robust digital infrastructure such as the Junmou AI platform, more than 26,000 students and 2,400 staff are already using the University’s AI platforms. XJTLU’s digital transformation is advancing from informatisation and digitisation towards intelligentisation, with AI expected to empower teaching, research and administration, and to help staff and students leap from knowledge to wisdom.
Dr Xin Bi
“Looking ahead, we will continue to advance the deep integration of AI in education, research, administration and services, building a data-driven intelligent operations centre and fostering a sustainable AI learning ecosystem,” said Dr Xin Bi.
By Qinru Liu
Edited by Patricia Pieterse
Translated by Xiangyin Han
-
Business3 weeks agoThe Guardian view on Trump and the Fed: independence is no substitute for accountability | Editorial
-
Tools & Platforms1 month ago
Building Trust in Military AI Starts with Opening the Black Box – War on the Rocks
-
Ethics & Policy2 months agoSDAIA Supports Saudi Arabia’s Leadership in Shaping Global AI Ethics, Policy, and Research – وكالة الأنباء السعودية
-
Events & Conferences4 months agoJourney to 1000 models: Scaling Instagram’s recommendation system
-
Jobs & Careers3 months agoMumbai-based Perplexity Alternative Has 60k+ Users Without Funding
-
Podcasts & Talks2 months agoHappy 4th of July! 🎆 Made with Veo 3 in Gemini
-
Education2 months agoMacron says UK and France have duty to tackle illegal migration ‘with humanity, solidarity and firmness’ – UK politics live | Politics
-
Education3 months agoVEX Robotics launches AI-powered classroom robotics system
-
Podcasts & Talks2 months agoOpenAI 🤝 @teamganassi
-
Funding & Business3 months agoKayak and Expedia race to build AI travel agents that turn social posts into itineraries