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Related Experiment Video

Updated: Nov 27, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Deep Neural Network for Reducing the Screening Workload in Systematic Reviews for Clinical Guidelines: Algorithm

Tomohide Yamada1,2, Daisuke Yoneoka3, Yuta Hiraike4

  • 1University Institute for Population Health, King's College London, London, United Kingdom.

Journal of Medical Internet Research
|December 2, 2020
PubMed
Summary

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Machine learning significantly reduces systematic review workload, achieving a 10-fold reduction in screening tasks. This AI approach enhances efficiency in evidence acquisition for clinical guidelines.

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Evidence-Based Medicine

Background:

  • Systematic reviews are critical for evidence-based medicine but are labor-intensive.
  • Efficient methods are needed to streamline the systematic review process.

Purpose of the Study:

  • To evaluate the efficiency of a machine learning system (Concept Encoder) in performing systematic reviews.
  • To quantify the reduction in workload compared to manual screening.

Main Methods:

  • Trained a neural network-based AI engine (Concept Encoder) using extracted articles from systematic reviews.
  • Assessed performance based on work saved over sampling at 95% recall (WSS@95%).
  • Evaluated AI performance after training with 2 randomly selected correct articles.
Keywords:
clinical guidelinedeep learningevidence-based medicinemachine learningmeta-analysisneural networksystematic review

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Main Results:

  • The machine learning system reduced literature screening workload by at least 6-fold.
  • Using 2 randomly selected correct articles for training achieved a 10-fold workload reduction.
  • AI demonstrated high sensitivity and improved performance with continued learning.

Conclusions:

  • Concept Encoder can reduce systematic review screening workload by up to 10-fold.
  • AI facilitates evidence acquisition for clinical guidelines.
  • Further research is needed as few meta-analyses were included in this study.