AI Research
Taking medical imaging embeddings 3D
Over recent years, developers and researchers have made progress in efficiently building AI applications. Google Research has contributed to this effort by providing easy-to-use embedding APIs for radiology, digital pathology and dermatology to help AI developers train models in these domains with less data and compute. However, these applications have been restricted to 2D imaging, while physicians often use 3D imaging for complex diagnostic decision-making. For example, computed tomography (CT) scans are the most common 3D medical imaging modality, with over 70 million CT exams conducted each year in the USA alone. CT scans are often essential for a variety of critical patient imaging evaluations, such as lung cancer screening, evaluation for acute neurological conditions, cardiac and trauma imaging, and follow-up on abnormal X-ray findings. Because they are volumetric, CT scans are more involved and time-consuming for radiologists to interpret compared to 2D X-rays. Similarly, given their size and structure, CT scans also require more storage and compute resources for AI model development.
CT scans are commonly stored as a series of 2D images in the standard DICOM format for medical images. These images are then recomposed into a 3D volume for either viewing or further processing. In 2018, we developed a state-of-the-art chest lung cancer detection research model trained on low dose chest CT images. We’ve subsequently improved the model, tested it in clinically realistic workflows and extended this model to classify incidental pulmonary nodules. We’ve partnered with both Aidence in Europe and Apollo Radiology International in India to productionize and deploy this model. Building on this work, our team explored multimodal interpretation of head CT scans through automated report generation, which we described in our Med-Gemini publication earlier this year.
Based on our direct experience with the difficulties of training AI models for 3D medical modalities, coupled with CT’s importance in diagnostic medicine, we designed a tool that allows researchers and developers to more easily build models for CT studies across different body parts. Today we announce the release of CT Foundation, a new research medical imaging embedding tool that accepts a CT volume as input and returns a small, information-rich numerical embedding that can be used for rapidly training models with little data. We developed this model for research purposes only and as such it may not be used in patient care, and is not intended to be used to diagnose, cure, mitigate, treat, or prevent a disease. For example, the model and any embeddings may not be used as a medical device. Interested developers and researchers can request access to the CT Foundation API, and use it for research purposes at no cost. We have included a demo notebook on training a model for lung cancer detection using the publicly available NLST data from The Cancer Imaging Archive.
AI Research
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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
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