Sub track:-
Enhanced Image Quality Quantitative Analysis, Faster Turnaround Times,...
Sub track:-
Integration of Imaging Modalities, Advanced Image...
Track Overview:
Artificial Intelligence (AI) is
revolutionizing digital pathology by automating and enhancing diagnostic
processes. This track will explore the latest advancements in AI algorithms for
histopathology, tumor detection, image analysis, and predictive modeling,
alongside their clinical integration and regulatory challenges.
Key
Topics:
AI-based Image Analysis: Leveraging deep learning
for histopathological image classification, segmentation, and feature
extraction.
Automated Tumor Detection: Implementing AI to
identify, classify, and predict cancer types and stages.
Predictive Modeling: Utilizing machine learning
algorithms to predict patient outcomes based on pathology images.
Regulatory & Ethical Considerations:
Navigating the challenges of AI in healthcare, including FDA/EMA compliance and
ethical concerns related to privacy and bias.
Clinical Integration: Best practices for
implementing AI tools in pathology workflows, from initial diagnosis to
real-time decision support systems.
Learning
Objectives:
Understand the principles of AI and
deep learning in pathology.
Explore real-world applications of AI
in histopathology and diagnostic workflows.
Discuss the regulatory, ethical, and
technical hurdles in the clinical adoption of AI tools.
Learn about the integration of AI in
decision support systems and the future of personalized medicine.
Target
Audience:
Pathologists, digital pathology
specialists, AI developers, researchers, and healthcare professionals looking
to understand AI's role in pathology.
Speakers/Presenters:
AI researchers
Clinical pathologists with AI
expertise
Healthcare regulators
Conclusion: