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Interdisciplinary data annotation: Best practices for enhancing accuracy in medical large language models

Year: 2025

Type: conference-paper

Abstract: Objectives and questions: Data-viewing is an essential but time-consuming part of the clinical workflow. The application of artificial intelligence for data filtering can result in a significant time reduction. However, untrained models lack the accuracy required for medical data evaluation, necessitating [for full text, please go to the a.m. URL]

Source: German Medical Science (German Research Foundation)

DOI: 10.3205/25dkou007

Authors: Seidl, Paulina, Hinterwimmer, Florian, Breden, Sebastian, von Eisenhart-Rothe, Rüdiger, Rueckert, Daniel, Mogler, Carolin, Schüffler, Peter, Lazic, Igor, Szép, Márton

Institutions: TUM Klinikum, Technical University of Munich

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7103179843

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