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.
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.
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.
Discover the powerful tools and features that make CoDiagnosis the premier platform for radiology education and collaboration.
Access a comprehensive library of real-world radiology cases with detailed annotations, enabling hands-on learning through interactive diagnosis exercises.
Connect with peers and mentors instantly. Share insights, discuss findings, and learn together in a collaborative environment that mirrors real clinical settings.
Seamlessly switch between student and teacher roles. Experienced radiologists can share their expertise while continuing their own professional development.
Browse through an extensive collection of curated cases covering various radiology specialties, difficulty levels, and diagnostic scenarios.
Monitor your learning journey with detailed analytics, performance metrics, and personalized recommendations to guide your educational path.
Learn from experienced radiologists who provide mentorship, feedback on your diagnoses, and valuable insights from years of clinical practice.
Our diverse team of experienced radiologists, educators, and technologists work together to create the best learning experience for you.
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.
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.
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.
Machine learning Researcher
Dr. Akash Awasthi is a Machine learning Researcher at the University of Houston. His PhD research focused on developing collaborative AI tools utilizing large multimodal models for healthcare applications.
