Renal pathology education often begins with diagnosis. This project begins one step earlier: perception.
The goal is to help learners develop visual attention, pattern recognition, and clinicopathologic reasoning through structured renal pathology image review, drawing-based observation, art-informed visual literacy, and reflective learning.
This project explores whether AI-assisted visual feedback, structured heuristics, drawing/coloring-based learning, and art-based observation can improve renal pathology pattern recognition among nephrology trainees.
The platform is designed for educational use within nephrology training. It does not replace renal pathology expertise and is not intended to train nephrology fellows to function as nephropathologists. Instead, it supports visual literacy, communication with pathology colleagues, and deeper clinicopathologic reasoning.
As artificial intelligence becomes increasingly integrated into medical image interpretation, this project asks how human visual expertise can be preserved, strengthened, and taught intentionally.
How Experts See
Novice pathway
- Searches for isolated features
- May focus on one striking detail
- May jump to diagnosis before describing the image
- May miss distribution or architecture
- May confuse pattern with diagnosis
Expert pathway
- Recognizes the global pattern
- Identifies the involved compartment
- Assesses distribution and severity
- Uses specific features to verify or revise
- Links morphology to clinicopathologic reasoning
- Communicates findings using precise visual language
For educational use only. Not intended for clinical diagnosis or patient care decisions.