Enhancing <scp>AI</scp> 's ability to interpret histological images with prompt engineering: An evaluation of <scp>GPT</scp> ‐4o performance

Abstract This study examines the effect of various prompting strategies on ChatGPT's ability to interpret histological images across different tissue types and varying question complexities. GPT‐4o's performance was assessed using three distinct prompting techniques: P1 (zero‐shot), P2 (few‐shot with examples), and P3 (chain‐of‐thought with reasoning explanations) across 120 histological images of four tissue types (epithelial, connective, muscular, and neural). Three standardized questions assessed tissue recognition, structural identification, and functional assessment. GPT‐4o demonstrated a noticeable variation in performance across tissue types ( p &lt; 0.001, η 2 = 0.042). Question complexity significantly affected performance ( p &lt; 0.001, η 2 = 0.022), revealing a hierarchical pattern in which structural identification proved most challenging across all conditions. Error dependency analysis revealed that 98% of functional assessment errors co‐occurred with structural identification errors, indicating strong cascading effects. Inter‐rater reliability remained consistently high across all conditions (ICC = 0.95–0.96). Across prompting approaches, answer accuracy ranged from 58.9% (P1) to 63.6% (P3), with modest or nonsignificant effects of prompt type ( p = 0.116, η 2 = 0.004). This study demonstrates that GPT‐4o's performance in histological image interpretation varies significantly across tissue types and question complexity. Tissue‐specific approaches and a focus on structural identification accuracy are essential for effective educational integration, whereas prompt engineering alone yields limited performance gains.