Revolutionizing Healthcare with GenAI
The 2027 International Conference on Trends in Generative AI is the premier forum for researchers, practitioners, and healthcare professionals. This year's theme, Generative AI for Healthcare, explores the profound impact of synthesis models, LLMs, and synthetic data on the future of medical science, patient care, and drug discovery.
Important Deadlines
Keynote Speakers
Visionaries leading the intersection of artificial intelligence and medical science.
Dr. Elena Rodriguez
Director of AI, Global Health Institute
Pioneering researcher in multimodal generative models for holistic disease detection and early diagnosis prediction.
Prof. David Chen
Head of Synthetic Biology, TechMed
Leading expert in drug discovery and molecular generation using diffusion models and LLMs.
Tracks & Scopes
We invite submissions across 10 specialized tracks focusing on the application of Generative AI in healthcare.
Generative Models for Medical Imaging
Synthesis, super-resolution, and cross-modal translation of MRI, CT, X-ray, and pathology images.
LLMs & Clinical Documentation
Automated summarization of patient records, clinical note generation, and decision support.
Drug Discovery & Molecular Generation
De novo design of small molecules, protein structure prediction, and peptide generation.
Synthetic Data for Privacy-Preserving
Generation of realistic but anonymized patient data for research and model training.
GenAI for Personalized Treatment
Patient-specific therapy recommendations, treatment response simulation, and digital twins.
Multimodal Gen Models in Diagnosis
Integrating text, images, genomics, and sensor data for holistic disease detection.
Ethics, Fairness & Regulation
Bias mitigation, explainability, regulatory compliance, and clinical validation of GenAI in medicine.
Generative AI for Mental Health
Conversational agents, therapeutic dialogue generation, and mood/anxiety state simulation.
Time-Series & Signal Generation
ECG, EEG, wearable sensor data synthesis for rare event simulation and model training.
Rare Disease & Low-Resource Settings
Data augmentation, synthetic cohorts, and knowledge graph generation for understudied conditions.