Conference Tracks

AI is transforming biomedical science across an enormous range of fields, from analyzing medical images and discovering new treatments to exploring how AI should be developed and used responsibly in healthcare. We organize our conference in four tracks to help with organization of submissions for review, not to limit what your research topics are. Please select one track that most fits your topic. Or you can describe your project below, and we’ll help you find the best fit - note that this is an experimental feature.

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Describe your idea in a sentence or two and we'll point you at the closest track. It runs entirely in your browser, so nothing you type is sent or stored anywhere.

Or browse all four tracks in detail below.

TRACK T1

AI for Healthcare, Diagnostics, and Clinical Decision Support

Artificial intelligence is increasingly supporting clinicians and healthcare systems through improved diagnostics, predictive analytics, personalized medicine, and intelligent clinical decision-making. Research in this track focuses on the development or evaluation of AI technologies that improve patient care, healthcare accessibility, and medical decision support.

Topics this track covers

Medical image analysis
Computer vision for healthcare
AI-assisted diagnosis
Clinical decision support systems
Disease prediction and risk assessment
Precision and personalized medicine
Electronic health record analytics
Wearable health technologies
Remote patient monitoring
Telemedicine and digital healthcare
Healthcare workflow optimization

TRACK T2

AI in Biomedical Research, Genomics, and Drug Discovery

Artificial intelligence has become an essential tool for accelerating biomedical discovery. This track welcomes research applying AI to biological sciences, pharmaceutical research, computational biology, and laboratory science. Students are encouraged to explore how AI can improve our understanding of biological systems, identify therapeutic targets, discover new medicines, and analyze complex biomedical datasets.

Topics this track covers

Drug discovery
Drug repurposing
Protein structure prediction
Computational biology
Bioinformatics
Genomics
Transcriptomics
Systems biology
Biomarker discovery
Molecular modeling
AI-assisted laboratory automation
Biomedical data analysis

TRACK T3

Responsible AI, Ethics, and Policy in Biomedicine

As AI becomes increasingly integrated into healthcare and biomedical research, ethical, legal, and societal considerations are becoming equally important. This track focuses on developing trustworthy, transparent, fair, and responsible AI systems while considering their broader impacts on individuals and society. Research addressing governance, explainability, privacy, bias, and regulatory frameworks is especially encouraged.

Topics this track covers

Ethical AI in medicine
Algorithmic bias and fairness
Explainable and interpretable AI
Responsible AI development
Biomedical data privacy
Cybersecurity for medical AI
Regulatory frameworks
AI governance
Health equity
Patient trust and transparency
Societal implications of AI in healthcare

TRACK T4

Emerging AI Technologies and Future Biomedical Innovation

The future of biomedical sciences will be driven by interdisciplinary innovation. This track highlights emerging technologies that combine AI with engineering, robotics, neuroscience, biotechnology, and digital health to address future biomedical challenges. Submissions may present novel research, conceptual frameworks, prototype systems, or forward-looking analyses that demonstrate innovative applications of AI.

Topics this track covers

Medical robotics
AI-assisted surgery
Brain-computer interfaces
Biomedical engineering
Smart prosthetics
Digital twins in healthcare
Internet of Medical Things (IoMT)
Multi-modal AI
Digital health ecosystems
AI for biotechnology
Future biomedical technologies