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  9. Knowledge Representation And Reasoning
  11. Explanatory Argument Extraction Of Correct Answers In Resident Medical Exams.

Explanatory argument extraction of correct answers in resident medical exams.

Iakes Goenaga1, Aitziber Atutxa1, Koldo Gojenola1

  • 1HiTZ Center - Ixa, University of the Basque Country UPV/EHU, Spain.

Artificial Intelligence in Medicine
|October 9, 2024

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new AI approach and dataset for medical question answering, helping doctors find evidence-based explanations in patient notes. It improves understanding of clinical decisions and supports medical expertise.

Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing (NLP)
  • Evidence-Based Medicine (EBM)

Background:

  • AI is crucial for assisting medical experts in decision-making within Evidence-Based Medicine.
  • Natural Language Processing (NLP) can aid in extracting relevant information from clinical notes for better human-AI interaction.
  • Identifying arguments in patient journeys is vital when multiple physicians are involved in care.

Purpose of the Study:

  • To develop and evaluate an AI-based Question Answering (QA) system for identifying explanatory arguments in medical decision-making.
  • To introduce a novel Spanish-language dataset (Antidote CasiMedicos) for evaluating AI models on medical reasoning.
  • To enable an extractive task for pinpointing evidence supporting correct medical hypotheses.

Main Methods:

Keywords:
ArgumentationExplainable artificial intelligenceNatural language processingQuestion answering

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  • Framing explanatory argument identification as a QA task for AI models.
  • Creating a new dataset with arguments for both correct and incorrect hypotheses, originally in Spanish.
  • Utilizing automatic metrics to evaluate the extractive performance of AI language models.

Main Results:

  • The proposed approach achieved a 74.47 F1 score on the Antidote CasiMedicos dataset.
  • The novel benchmark facilitates the evaluation of AI's ability to identify supporting evidence in medical texts.
  • Experimentation confirmed the effectiveness of the dataset and approach in aiding practitioners.

Conclusions:

  • The developed AI technique and dataset effectively assist medical practitioners in identifying evidence-based explanations.
  • This work enhances the understanding of clinical decision-making processes through advanced NLP.
  • The QA-based approach offers a promising direction for human-AI collaboration in healthcare.
Resident medical exams