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AI-Based EMG Reporting: A Randomized Controlled Trial.

Alon Gorenshtein1,2,3, Yana Weisblat4, Mohamed Khateb4,5

  • 1Department of Neurology, Rambam Health Care Campus, Haifa, Israel. alon.gorenshtein@live.biu.ac.il.

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|August 22, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) in electrodiagnostic (EDX) studies did not significantly improve report quality compared to physician-only interpretation. However, AI may assist with routine cases, reducing physician workload.

Keywords:
Artificial intelligenceElectrodiagnostic studyElectromyogramLarge language modelsMulti-AI agentsNerve conduction studyNeurologyNeuromuscularRandomized controlled trial

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Area of Science:

  • Neurology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Accurate electrodiagnostic (EDX) studies are crucial for diagnosing and managing neuromuscular disorders.
  • Artificial intelligence (AI) tools offer potential for enhancing EDX reporting consistency, quality, and efficiency.
  • A randomized controlled trial (RCT) was conducted to evaluate an AI-assisted framework for EDX interpretation.

Purpose of the Study:

  • To evaluate the performance of an AI-assisted, multi-agent framework (INSPIRE) against standard physician interpretation in EDX studies.
  • To compare the quality of EDX reports generated by physician-AI integration versus physician-only interpretation.

Main Methods:

  • A prospective randomized controlled trial enrolled 200 patients undergoing EDX.
  • Patients were assigned to either physician-only interpretation or physician-AI integrated interpretation.
  • The primary outcome measure was EDX report quality assessed by the AI-Generated EMG Report Score (AIGERS).

Main Results:

  • The physician-AI integrated approach did not significantly outperform physician-only interpretation in terms of AIGERS scores.
  • Physicians reported challenges with AI interpretability, workflow integration, and perceived lack of efficiency and workload reduction.
  • While AI preliminary reports showed moderate consistency, the integrated approach yielded similar quality scores to the control group.

Conclusions:

  • AI-assisted EDX interpretation did not show a significant advantage over conventional physician-only methods in this trial.
  • The AI framework holds potential for managing simpler EDX tests, thereby reducing physician workload and allowing focus on complex cases.