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Evaluation of basic life support practice skills based on artificial intelligence technology: system construction and

Yan Jiang1,2, Yan Chen1, Yin Zhang3

  • 1Nursing Department, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai, 200025, China.

BMC Medical Education
|April 29, 2026
PubMed
Summary

A new artificial intelligence (AI) system accurately assesses basic life support (BLS) skills, matching human examiner scores. This AI tool enhances assessment efficiency and candidate experience in BLS training.

Keywords:
Artificial intelligenceAssessmentBasic life support practice skillsEquivalence verificationNew nurses

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Area of Science:

  • Medical Education Technology
  • Artificial Intelligence in Healthcare
  • Cardiopulmonary Resuscitation Training

Background:

  • Traditional basic life support (BLS) skills assessment lacks objectivity and efficiency.
  • Examiner fatigue and subjective bias affect manual BLS skill evaluations.
  • A validated AI system for BLS assessment is currently unavailable.

Purpose of the Study:

  • To develop an AI-based system for evaluating BLS practice skills.
  • To validate the AI system's equivalence against manual examiner assessments.
  • To analyze the advantages and future directions of AI in BLS skill evaluation.

Main Methods:

  • Developed an AI system using RTMPose, ST-GCN, SVM, YOLOX, and Whisper models.
  • Constructed an assessment environment with audio-video capture and CPR simulators.
  • Conducted a paired assessment of 85 nurses, comparing AI and human examiner scores.

Main Results:

  • AI system scores showed no significant difference from examiner scores (p=0.769).
  • High absolute agreement was observed between AI and examiner evaluations (ICC=0.868).
  • 64.56% of candidates felt the AI assessment reduced nervousness.

Conclusions:

  • The AI system is consistent with examiner marking for BLS skills evaluation.
  • AI application can improve the effectiveness and organization of BLS assessments.
  • Candidate acceptance is good, with potential for expansion after system optimization.