AI Simplification of Dermatopathology Reports for Patients: Basic Versus Prompt‐Engineered Approaches

Background Patients struggle to comprehend dermatopathology reports. As artificial intelligence (AI) tools become more accessible, patients may use them to interpret reports; however, optimal approaches remain unexplored. Objective Evaluate whether prompt‐engineered AI simplification of dermatopathology reports improves factualness, completeness, and reduces potential harm compared to basic AI usage. Methods Survey‐based study (January–April 2025) of 52 US dermatology and dermatopathology professionals (70.3% response rate). Six fictitious dermatopathology reports were simplified using: (1) Basic ChatGPT‐4.0 with simple prompt and (2) Custom “DermDecoder” GPT with structured 489‐word prompt. Participants rated reports on 3‐point Likert scales for factualness, completeness, and potential harm, with free‐text responses analyzed thematically. Results Mean ratings ranged from 1.27 to 1.63 (factualness/completeness) and 1.31–1.83 (harmfulness), indicating “Agree” to “Mostly Agree” or “Completely Harmless” to “Mostly Harmless.” DermDecoder performed significantly worse for completeness in psoriasis ( t = −2.79, p = 0.007) and harmfulness in molluscum contagiosum ( p = 0.049) and melanoma in situ ( p = 0.048). Free‐text analysis revealed Basic Prompt preserved details but lacked clinical context, while DermDecoder provided generic education disconnected from pathological findings. Limitations Fictitious reports, small sample, evolving AI capabilities, and absence of patient perspectives. Conclusion Prompt engineering offered no advantage over basic AI usage in balancing professional accuracy with patient accessibility, necessitating human‐in‐the‐loop oversight for AI‐generated explanations.