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Introduction <p>The Fundamentals of Laparoscopic Surgery (FLS) curriculum is a validated simulation program for surgical training, yet time constraints in clinical settings limit faculty-guided skill acquisition. This study aimed to implement an asynchronous tele-mentoring model using smartphones and deliberate practice to train residents to FLS proficiency. </p> Methods and procedures <p>Surgical residents (PGY1-5) engaged in self-directed practice of FLS psychomotor tasks without restrictions on session frequency or duration. Smartphones mounted inside FLS boxes recorded performances, mimicking the onboard camera angle. Residents submitted videos post-practice to an expert instructor, who provided written feedback within 24–48&#xa0;h. Feedback themes were analyzed qualitatively using artificial intelligence (ChatGPT).</p> Results <p>Forty-one residents (2017–2024) participated. Thematic analysis revealed early feedback emphasized technical skill development (62% of comments, e.g., instrument handling), shifting to efficiency optimization (22%) and error reduction (10%) as proficiency increased. All residents surpassed the FLS passing score (356), achieving a mean score of 598 (± 72.9 SD), with a range of 421–721.</p> Conclusion <p>This asynchronous tele-mentoring model demonstrates feasibility in proficiency-based FLS training, combining self-directed practice with timely expert feedback. Smartphone integration and AI-aided feedback analysis enhance accessibility and objectivity, addressing barriers to faculty availability. The evolution of feedback themes aligns with skill acquisition theory, underscoring the value of structured, phase-specific guidance. AI’s role in parsing feedback patterns offers scalability without compromising expert-led mentorship. This approach provides a framework for flexible, technology-enhanced surgical education.</p> Graphical abstract <p></p>

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Asynchronous tele-mentoring in proficiency-based training for the fundamentals of laparoscopic surgery (FLS): a use of smartphones and deliberate practice

  • 期刊论文

  • 发表日期：

    2025年12月23日

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  • Mazin Abdalgadir,
  • Diaa Soliman,
  • Yousef Shaher,
  • Adam Nelson,
  • Stephanie Worrell,
  • Michael Ditillo,
  • Iman Ghaderi

摘要

Introduction

The Fundamentals of Laparoscopic Surgery (FLS) curriculum is a validated simulation program for surgical training, yet time constraints in clinical settings limit faculty-guided skill acquisition. This study aimed to implement an asynchronous tele-mentoring model using smartphones and deliberate practice to train residents to FLS proficiency.

Methods and procedures

Surgical residents (PGY1-5) engaged in self-directed practice of FLS psychomotor tasks without restrictions on session frequency or duration. Smartphones mounted inside FLS boxes recorded performances, mimicking the onboard camera angle. Residents submitted videos post-practice to an expert instructor, who provided written feedback within 24–48 h. Feedback themes were analyzed qualitatively using artificial intelligence (ChatGPT).

Results

Forty-one residents (2017–2024) participated. Thematic analysis revealed early feedback emphasized technical skill development (62% of comments, e.g., instrument handling), shifting to efficiency optimization (22%) and error reduction (10%) as proficiency increased. All residents surpassed the FLS passing score (356), achieving a mean score of 598 (± 72.9 SD), with a range of 421–721.

Conclusion

This asynchronous tele-mentoring model demonstrates feasibility in proficiency-based FLS training, combining self-directed practice with timely expert feedback. Smartphone integration and AI-aided feedback analysis enhance accessibility and objectivity, addressing barriers to faculty availability. The evolution of feedback themes aligns with skill acquisition theory, underscoring the value of structured, phase-specific guidance. AI’s role in parsing feedback patterns offers scalability without compromising expert-led mentorship. This approach provides a framework for flexible, technology-enhanced surgical education.

Graphical abstract