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Editing Physicians’ Responses Using GPT-4 for Academic Research
Magdalena T. Weber, Jannik Schaaf, Holger Storf, Thomas O.F. Wagner, Alexandra Berger, Richard Noll
101 - 106
10.3233/SHTI240019
Research Article
Studies in Health Technology and Informatics
Volume 313: dHealth 2024
Abstract

The integration of Artificial Intelligence (AI) into digital healthcare, particularly in the anonymisation and processing of health information, holds considerable potential.

Objectives:

To develop a methodology using Generative Pre-trained Transformer (GPT) models to preserve the essence of medical advice in doctors’ responses, while editing them for use in scientific studies.

Methods:

German and English responses from EXABO, a rare respiratory disease platform, were processed using iterative refinement and other prompt engineering techniques, with a focus on removing identifiable and irrelevant content.

Results:

Of 40 responses tested, 31 were accurately modified according to the developed guidelines. Challenges included misclassification and incomplete removal, with incremental prompting proving more accurate than combined prompting.

Conclusion:

GPT-4 models show promise in medical response editing, but face challenges in accuracy and consistency. Precision in prompt engineering is essential in medical contexts to minimise bias and retain relevant information.

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