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Digitalizing Medical Forms Through Visual Question Answering: Are We There Yet?...20th World Congress on Medical and Health Informatics, Aug 09 - 13, 2025, Taipei, Taiwan.

This study investigates the potential of multimodal neural networks to convert data from unstructured paper-based medical documents into a structured format. Utilizing advancements in Visual Question Answering, we curated a dataset from neurological documents at the University Medical Center Hamburg...

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Published in:Studies in Health Technology & Informatics Vol. 329; pp. 253 - 258
Main Authors: GUNDLER, Christopher, WIEDERHOLD, Alexander Johannes, PÖTTER-NERGER, Monika
Format: proceedings research tables/charts Journal Article
Published: Sage Publications Inc. 2025
Online Access:View this record in EBSCOhost
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      dt: 2025
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      pub: Sage Publications Inc.
      place: Thousand Oaks, California
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        atl: Digitalizing Medical Forms Through Visual Question Answering: Are We There Yet?...20th World Congress on Medical and Health Informatics, Aug 09 - 13, 2025, Taipei, Taiwan.
      aug:
        au:
          GUNDLER, Christopher
          WIEDERHOLD, Alexander Johannes
          PÖTTER-NERGER, Monika
        affil: Institute for Applied Medical Informatics, University Medical Center Hamburg-Eppendorf
      sug:
        subj:
          Electronic Health Records
          Documentation
          Neural Networks (Computer)
          Patient Record Systems
          Medical Records
          Congresses and Conferences Taiwan
          Taiwan
          Human
          Clinical Documentation Improvement
          Parkinson Disease
          Natural Language Processing
          Comparative Studies
          Quality Improvement
          Implementation Science
          Information Retrieval
      ab: This study investigates the potential of multimodal neural networks to convert data from unstructured paper-based medical documents into a structured format. Utilizing advancements in Visual Question Answering, we curated a dataset from neurological documents at the University Medical Center Hamburg-Eppendorf. Different models were assessed for their effectiveness. While models from 2024 showed improved performance, accuracy remained below clinical standards, revealing significant challenges in adapting such technology to complex and heterogeneous medical records. The findings emphasize the need for larger, diverse datasets and ongoing refinement to bridge the gap between current model capabilities and human-level performance, underscoring the complexity of automating data extraction in clinical settings.
      pubtype: Academic Journal
      doctype:
        proceedings
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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