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Empowering
the Next Generation
of Radiologists

CoDiagnosis is a revolutionary platform that bridges the gap between expert radiologists and aspiring medical professionals. Through interactive case studies and real-time collaboration, we're transforming radiology education.

Our Mission

To make high-quality radiology education accessible to everyone by leveraging cutting-edge technology. We provide an interactive platform where students can learn from real-world cases and experienced radiologists can share their expertise, creating a collaborative learning environment that bridges theory and practice.

Our Vision

To transform medical diagnosis education through innovative teacher-student collaboration. We envision a future where every aspiring radiologist has access to comprehensive case libraries, expert guidance, and interactive learning tools that prepare them for real-world diagnostic challenges.

Platform Features

Discover the powerful tools and features that make CoDiagnosis the premier platform for radiology education and collaboration.

Interactive Case Studies

Access a comprehensive library of real-world radiology cases with detailed annotations, enabling hands-on learning through interactive diagnosis exercises.

Real-time Collaboration

Connect with peers and mentors instantly. Share insights, discuss findings, and learn together in a collaborative environment that mirrors real clinical settings.

Teacher-Student Mode

Seamlessly switch between student and teacher roles. Experienced radiologists can share their expertise while continuing their own professional development.

Comprehensive Library

Browse through an extensive collection of curated cases covering various radiology specialties, difficulty levels, and diagnostic scenarios.

Progress Tracking

Monitor your learning journey with detailed analytics, performance metrics, and personalized recommendations to guide your educational path.

Expert Guidance

Learn from experienced radiologists who provide mentorship, feedback on your diagnoses, and valuable insights from years of clinical practice.

How It Works

Getting started with CoDiagnosis is simple. Follow these four steps to begin your radiology learning journey.

1

Sign Up & Choose Your Role

Create your account and select whether you want to join as a student seeking to learn or as a teacher ready to share your expertise. You can switch roles anytime to experience both perspectives.

2

Browse the Case Library

Explore our extensive collection of radiology cases covering various specialties and difficulty levels. Filter by modality, body system, or specific conditions to find cases that match your learning goals.

3

Analyze Cases with Advanced Tools

Use our interactive diagnostic tools to examine images, make annotations, and formulate your diagnoses. Take advantage of features like zoom, measurements, and comparison views for thorough analysis.

4

Collaborate & Learn Together

Engage with teachers and fellow students through real-time discussions. Receive feedback on your diagnoses, share insights, and learn from experienced radiologists in a supportive community environment.

Meet Our Team

Our diverse team of experienced radiologists, educators, and technologists work together to create the best learning experience for you.

Carol C. Wu, M.D.

Professor

Dr. Carol C. Wu is a Professor at The University of Texas MD Anderson Cancer Center. Her clinical and research work focuses on thoracic imaging, with a particular emphasis on the diagnosis, staging, and assessment of treatment response for lung cancer.

Hien Van Nguyen, Ph.D.

Associate Professor

Dr. Hien Van Nguyen is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Houston. His research lies at the intersection of artificial intelligence, computer vision, and biomedical image analysis.

Rishi Agrawal, M.D.

Associate Professor

Dr. Rishi Agarwal is an associate professor at The University of Texas MD Anderson Cancer Center. His clinical and research interests center on thoracic imaging, with a focus on lung cancer screening, early detection, and image-based biomarkers.

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