Singapore

Wearable health devices provide continuous, real-time insights into physiological patterns that may reflect underlying systemic diseases. However, validating these signals often requires invasive and costly clinical tests. As the only externally visible microvascular structure, the retina offers a unique noninvasive window into vascular health. Retinal biomarkers including vessel calibers (CRAE, CRVE), arteriovenous ratio (AVR), vessel density, fractal dimension, and tortuosity have been associated with systemic conditions such as diabetes, hypertension, and cardiovascular disease. 
In this study, we leveraged the AIREADI dataset to investigate how retinal vascular features vary across stratified levels of physiological dysregulation, as measured by wearable-derived metrics (e.g., heart rate, $SpO_2$, sleep, activity, and stress scores) collected over 10 days. Participants were categorized into low, moderate, and severe physiological severity groups and further stratified by six chronic disease conditions. 
Our analysis revealed significant associations between higher physiological severity and venular dilation (increased CRVE), reduced arteriovenous ratio (AVR), and elevated venous fractal dimension. These trends were consistent across several disease-specific subgroups, particularly in diabetes, hypertension, and kidney disease. However, parameters such as CRAE and tortuosity showed no consistent or statistically significant changes. 
These findings support the complementary role of retinal imaging and wearable sensors in passive, scalable monitoring of systemic vascular health, offering promise for early risk stratification and precision health interventions.

AAAI 2026

Integration of Wearable Health Device Data and Retinal Imaging for Multimodal Systemic Disease Stratification

systemic health monitoring

early disease diagnosis

fitness trackers

wearable health data

ai for digital health

oculomics

multimodal analysis

2025 National Inter-University Health Data & AI Inquiry Program - this program is a unique project-based inquiry and science communication program that brings together students from diverse academic backgrounds and research interests. Presented research projects explore how generative AI is being applied to medication management, mental health screening, and chronic disease monitoring to align with the future of pharmacy practice.

Aging Powerfully: AI Blueprint for Chronic Disease Management and Social Determinants of Longevity

2025 National Inter-University Health Data & AI Inquiry Program is a unique project-based inquiry and science communication program that brings together students from diverse academic backgrounds and research interests. 

workshop paper

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Teaching machine learning (ML) workflows to non-programmers
remains a challenge in introductory AI courses.
Traditionally, educators have turned to no-code tools such
as KNIME to lower barriers. With the rise of generative AI
(GenAI), students can now construct ML pipelines through
natural language prompts, potentially offering a new
“no-code” pathway. In a polytechnic-wide elective in
Singapore, students were given the choice of using either
KNIME or a GenAI chatbot for practical exercises and their
semester project. Survey responses, informal interviews,
and classroom observations revealed that both tools
supported conceptual learning, but students’ experiences
diverged: KNIME provided predictability and structured
guidance, while GenAI offered speed and flexibility yet
posed setup challenges and required coding familiarity.
Students valued having a choice, though this complicated
teaching logistics. Our experience suggests that GenAI can
complement—but not yet replace—traditional no-code
platforms, and that the design of introductory activities
is critical for adoption. We share lessons learned for
educators considering GenAI as an alternative in
workflow-based ML education.

Understanding the Effects of GenAI as No-Code Alternative for Teaching Machine Learning Workflows

Research on how the popularization of generative Artificial
Intelligence (AI) tools is impacting learning environments
has led to hesitancy among educators to teach these tools
in classrooms, creating two observed disconnects.
Generative AI competency is increasingly valued in industry
but not in higher education, and students are experimenting
with generative AI without formal guidance. Grounded in
learning sciences literature, the authors believe students
across fields must be taught to responsibly and expertly
harness the potential of AI tools to ensure job market
readiness and positive outcomes. Computer Science
trajectories are particularly impacted, and while
consistently top ranked U.S. Computer Science departments
teach the mechanisms and frameworks underlying AI, few
appear to offer courses on applications for existing
generative AI tools. A course was developed at a private
research university to teach undergraduate and graduate
Computer Science students applications for generative AI
tools in software development. Two mixed method surveys
indicated students overwhelmingly found the course valuable
and effective. Co-authored by the professor and one of the
graduate students, this paper explores the context,
implementation, and impact of the course through data
analysis and reflections from both perspectives. It
additionally offers recommendations for replication in and
beyond Computer Science departments.

Bridging the Skills Gap: A Course Model for Modern Generative AI Education

Artificial Intelligence (AI), particularly in the form of
intelligent AI agents, is transforming education, industry,
and everyday life. These agents extend the capabilities of
Large Language Models (LLMs) by integrating planning,
decision-making, tool use, and multi-agent collaboration,
enabling systems that can reason, adapt, and act in dynamic
environments. As such systems become integral to modern
workplaces and everyday problem solving, early exposure
equips high school students with systems thinking,
practical problem-solving skills, and ethical awareness,
while preparing them to create applications that address
real-world needs. Yet most high school AI programs focus on
basic model usage and overlook the skills required to
design and deploy agentic systems. Existing resources are
largely aimed at university learners and assume substantial
programming expertise, creating a significant accessibility
gap. To address this need, we present a structured
hackathon-based framework for introducing high school
students to the design and application of AI agents. The
framework combines expert-led lectures on core topics such
as agent architectures, prompting strategies, reasoning
methods, and tool-use protocols with a guided hackathon in
which students collaboratively develop domain-specific
agent-based chatbots. We provide a complete suite of
instructional materials, step-by-step tutorials, and
starter code to support hands-on learning, enabling
participants to build functional agents capable of
reasoning and interacting with external tools. Our approach
bridges the gap between AI literacy and practical
deployment while fostering creativity, collaboration, and
responsible innovation, and our findings suggest that early
engagement with agent-based AI design equips students with
both technical proficiency and the mindset to shape the
AI-driven future.

Catching the First Light of Tomorrow: A Hackathon-Based Framework for Introducing High School Students to AI Agents

Aging is a major risk factor for multiple chronic diseases, yet individual rates of aging vary significantly. To address the challenge of applying biological age models across populations, this study first harmonized clinical data between the UK Biobank (UKB) and a Chinese cohort (XMUFAH) using statistical distance metrics, selecting 36 health indicators with high distributional similarity. Based on these indicators, a LightGBM biological age prediction model was developed using a UKB population (n=431,113). The model demonstrated strong predictive performance in the independent XMUFAH external validation cohort (n=86,233), confirming its robustness and generalizability for cross-population application. Results show that accelerated biological aging is significantly associated with the future incidence risk of 12 chronic diseases in the UKB cohort and 8 in the XMUFAH cohort, suggesting the model as an effective tool for cross-population assessment of aging and risk prediction for related diseases.

Cross-Cohort Prediction of Biological Age using Routine Medical Examination Data


This paper presents GRAFT (Graph-Augmented Framework with Vision Transformers), a multi-stage framework for multi-label classification of chest X-ray images. GRAFT combines self-supervised vision transformers with graph-based relational learning, designed to enhance prediction accuracy in complex multi-label settings. Our framework operates in three stages: (1) self-supervised pre-training using Masked Autoencoders (MAE) with Vision Transformers to learn robust visual representations without extensive labeled data, (2) multi-perspective graph construction incorporating statistical co-occurrence patterns, spatial relationships, and visual feature similarities to model inter-label dependencies, and (3) uncertainty-weighted fusion through a Contextual Graph Fusion Network (CGFN) that dynamically balances contributions from different graph types, while considering their biomedical contexts. The approach addresses key challenges in multi-label classification, including class imbalance, incomplete annotations, and complex label interdependencies. Given the high prevalence and co-occurrence of thoracic diseases in aging populations, our multi-label framework directly addresses a critical gap in diagnostic care, with implications for healthy aging and longevity. 
We evaluate GRAFT on popular medical imaging datasets.

GRAFT: Graph-Augmented Framework with Vision Transformers for Multi-Label Classification

As populations age globally, it is increasingly important to develop digital tools for assessing the functional ability of older adults at scale. This study explores the potential for using movement data from exergaming interventions recorded with wearable inertial measurement units (IMUs) to predict fall risk in older adults. We propose a self-supervised learning approach that uses Long Short-Term Memory (LSTM) architectures to analyze exergaming signals and gait patterns. Two models are evaluated: (i) a baseline supervised LSTM classifier and (ii) an LSTM-based autoencoder that learns latent representations of movement dynamics. These latent embeddings not only improve fall-risk prediction (F1 = 0.444 versus. 0.233 for the baseline) but also form a foundation for inferring other functional and physiological measures in the future. Using data from a 14-week exergaming intervention with 36 participants, our findings demonstrate that unsupervised representations derived from movement signatures during tasks like exergames can encode clinically relevant markers of physical ability. The use of low-cost IMU sensors to record movement data coupled with artificial intelligence-based data processing enables scalable and continuous fall risk prediction among older adults undergoing exercise-based interventions for improving functional ability.

A Self-Supervised Learning-Based Approach to Predict Fall Risk in Older Adults from Exergaming Intervention Movements

Brain-Computer Interfaces (BCIs) have emerged as alternative 
means of communication for individuals with speech & motor 
impairments. These systems enable patients to express themselves without any articulation, by decoding speech from neural activity. However, most of the existing studies rely on invasive surgical procedures, with limited studies using non-invasive signals for phoneme or word level classification, thus covering a short vocabulary. To the best of our knowledge, this study presents the first demonstration of a framework for sentence level imagined speech synthesis from non-invasive electroencephalography (EEG) signals. Our model uses an Efficient-Net based masked auto-encoder approach for learning feature embeddings from EEG signals which are then used for fine-tuning BERT for next token generation. For this study, Large Spanish Speech EEG Dataset has been used with a mixed subject approach for both training & evaluation purposes, resulting into a 48.92% accuracy.

Towards Sentence Level Imagined Speech Generation from EEG signals

Loneliness is a major determinant of poor health and wellbeing in older adults, yet existing interventions remain limited in accessibility, personalisation, and long-term effectiveness. Emerging technologies, including artificial intelligence (AI) and virtual reality (VR), offer new opportunities for emotionally adaptive and engaging support. This study explores how older adults perceive the potential of VR to address loneliness through a participatory focus group and interactive VR workshop. Thematic analysis revealed that participants sought technologies fostering agency, emotional regulation, and meaningful social connection, rather than existing strategies of passive distraction. We present several AI integration approaches that can actualise participants’ visions of effective loneliness support, augmenting the therapeutic capabilities of immersive technologies. These findings highlight the promise of AI-enhanced and ethically grounded VR interventions as tools for emotional growth and healthy ageing, emphasising the importance of co-design and user-centred development in future digital therapeutics for loneliness.

From Isolation to Connection: Participatory Insights on AI and VR for Healthy Ageing

This work addresses the large inter-individual variability in physiological adaptation to exercise, which limits the effectiveness of generalized programs. We introduce mechanobioAI, a knowledge-graph framework that operationalizes mechanobiology to infer adaptive predispositions and generate N-of-1, parameterized exercise prescriptions. The framework links observable performance variables such as strength, velocity, and recovery to latent mechanobiological traits including muscle–tendon stiffness, fiber-type composition, and recovery kinetics. Estimated predispositions are integrated with a quantitative model of the titin-kinase mechanosensitive switch to prescribe mechanical dosages—load, volume, frequency, and tempo—targeting specific molecular pathways. Beyond dosage, the knowledge graph also constrains exercise selection so that chosen movements instantiate the intended mechanical stimuli, aligning program design with user goals. 

By encoding mechanobiological and genetic principles directly within a knowledge graph, mechanobioAI moves beyond proxy-based AI personalization toward biologically grounded, mechanism-informed coaching. Preliminary feasibility analyses using public datasets that couple anthropometrics and performance proxies with common polymorphisms suggest that graph-inferred traits can recapitulate known genotype–phenotype links. Practically, this approach seeks to increase the speed and magnitude of beneficial adaptation, reduce injury risk through better control of mechanical exposure, and address the lack of principled personalization in current digital fitness tools. The result is a coherent pathway from cellular mechanisms to individualized training programs, enabling scalable N-of-1 prescriptions that are both interpretable and actionable.

mechanobioAI: A Mechanobiology-Driven Knowledge Graph for N-of-1 Exercise Personalization

Chronic disease management requires regular adherence feedback to prevent avoidable hospitalizations, yet clinicians lack time to produce personalized patient communications. Manual authoring preserves clinical accuracy but does not scale; AI generation scales but can undermine trust in patient-facing contexts. We present a clinician-in-the-loop interface that constrains AI to data organization and preserves physician oversight through recognition-based review. A single-page editor pairs AI-generated section drafts with time-aligned visualizations, enabling inline editing with visual evidence for each claim. This division of labor (AI organizes, clinician decides) targets both efficiency and accountability. In a pilot with three physicians reviewing 24 cases, AI successfully generated clinically personalized drafts matching physicians' manual authoring practice (overall mean 4.86/10 vs. 5.0/10 baseline), requiring minimal physician editing (mean 8.3\% content modification) with zero safety-critical issues, demonstrating effective automation of content generation. However, review time remained comparable to manual practice, revealing an accountability paradox: in high-stakes clinical contexts, professional responsibility requires complete verification regardless of AI accuracy. We contribute three interaction patterns for clinical AI collaboration: bounded generation with recognition-based review via chart-text pairing, automated urgency flagging that analyzes vital trends and adherence patterns with fail-safe escalation for missed critical monitoring tasks, and progressive disclosure controls that reduce cognitive load while maintaining oversight. These patterns indicate that clinical AI efficiency requires not only accurate models, but also mechanisms for selective verification that preserve accountability.

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