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Professional and Personal Development

(529) Use Artificial Intelligence Technology for Otolaryngology Core Medical Knowledge Learning

Monday, October 2, 2023
2:45 PM – 3:45 PM East Coast USA Time
Has Audio

    Co-Author(s)

  • JS

    Jun-Ming Su, PhD

    Department of Information and Learning Technology,National University of Tainan, USA

  • Te-Yung Fang, MD

    Attending Physician, Department of Otolaryngology
    Cathay General Hospital, Taipei, Taiwan
    Taipei, Taiwan (Republic of China)

  • SH

    Su-Yi Hsu, MD

    Chief of Department of Otolaryngology
    Cathay General Hospital, Taipei, Taiwan
    Taipei, USA

    • Presenting Author(s)

  • Pa-Chun Wang, MD

    CEO
    Joint Commission Taiwan
    Taipei, Taipei, Taiwan (Republic of China)

Disclosure(s):
Te-Yung Fang, MD: No relevant relationships to disclose.
Pa-Chun Wang, MD: No relevant relationships to disclose.


Introduction: The impacts of Covid-19 has fostered the innovation of clinical medical education. We use knowledge-based approach in artificial intelligence technology to develop a multiple-expert knowledge aggregated adaptive assessment scheme (MEKAS), aiming to facilitate otolaryngology clinical core medical knowledge (CCMK-OTO) learning.

Methods: CCMK-OTO education was delivered virtually through mobile devices within a 4 weeks' period. A MEKAS was developed to automatically generate test items for the support of effective self-learning and assessment based on trainee’s CCMK-OTO competency. Longitudinal training effects were compared among experiment and control groups (EG and CG). The EGs are comprised with EG-ENT-R (8 otolaryngology residents), EG-Non-ENT (5 PGY and 10 UGY trainees); while as control group is with CG-Non-ENT (5 PGY and 10 UGY trainees). A 5-point technology acceptance model (TAM) was used to investigate user experiences.

Results: As compared to CG (scores 58.7 to 60.7, p=0.732), there are significant improvements (p < 0.000) in both EG-ENT-R (12.5) and EG-Non-ENT groups (19.7). In general, EG trainees are satisfied with MEKAS in self-learning assistance, adaptive testing, perceived satisfaction, intention to use, perceived usefulness, perceived ease of use, and perceived enjoyment (scores range from 3.3 to 4.1).

Conclusions: MEKAS proves to be useful in facilitating otolaryngology CCMK-OTO learning. The scheme in MEKAS provides efficient knowledge aggregation and level-based self-assessment for conducting adaptive CCMK-OTO learning.