FengGao Lab · Closed-Loop Medical Intelligence
We connect patient data, disease biology and intelligent systems to advance precision care.
FengGao Lab at SYSU
Clinical observations capture different aspects of a patient at different moments. Connecting them helps us understand the individual, explain how disease unfolds, and determine what to do next.
Based at the Sixth Affiliated Hospital of Sun Yat-sen University, we bring together clinical research, bioinformatics, disease biology and artificial intelligence, with colorectal cancer as our flagship setting. We build on advances in general-purpose AI, focusing our research on patient representations, disease knowledge and models, and agents that turn these capabilities into professional work.
Three connected questions, investigated through computational, experimental and clinical studies.
Multimodal and longitudinal patient representationWe connect imaging, pathology and molecular measurements, and study how changes during treatment inform patient assessment.
CRCFound · Brim · TMO-Net · Longitudinal MRI
Explore this researchMolecular subtypes and disease biologyWe investigate differences in tumour biology, immune environments and outcomes through molecular classification, pathway analysis and experimental studies.
DeepCC · MotifCC · PIANOS
Explore this researchClinical AI and human–AI collaborationWe study diagnostic assistance and surgical visual perception, evaluating clinician accuracy, reading time and recognition of anatomical landmarks.
INTELCAPE · FSA-Net
Explore this researchUpdated 2026-11-05
Shared model access for literature research, code, data analysis, scientific writing, and agent workflows.
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FengGao Lab · Sun Yat-sen University
Clinicians, computational researchers, and trainees working across cancer biology, medical imaging, and AI-native healthcare.
Postdoc
Obstructive Sleep Apnea-Hypopnea SyndromeChronic RhinosinusitisAllergic Rhinitis
Recent work from the lab and our collaborators.
Research advances, AI infrastructure, and life in the lab.
Our team published a new study in Research that integrates CT foundation model features with radiomic features to characterize clinically and biologically distinct colorectal cancer subtypes. The work extends imaging based colore…
Read updateFor collaborations, student inquiries, and lab visits.
