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.