JMIR Publications

ABSTRACT

Background:

Human–autonomy teaming (HAT) has the potential to reshape surgical practice by fostering true partnership between surgeons and intelligent systems. To achieve this, AI must move beyond static scoring to provide adaptive, real-time guidance based on reliable skill assessment.

Objective:

This scoping review aims to map the current landscape of machine learning (ML) methods for surgical skill assessment and evaluate their technical readiness for integration into functional HAT systems.

Methods:

Following PRISMA-ScR guidelines, we conducted a systematic search across three major scientific databases (PubMed, IEEE Xplore, and Scopus). We identified and analyzed 92 peer-reviewed studies published between 2019 and 2025. The review focused on data modalities (kinematics, video, biosignals), model architectures, and validation environments.

Results:

Our analysis of the 92 included studies reveals a dominant shift toward multimodal data integration and deep learning architectures. While high performance is frequently reported on benchmark datasets, significant barriers to HAT integration persist. We identified that a majority of current models lack interpretability and fail to demonstrate generalizability to real-world clinical settings. Furthermore, validation practices remain inconsistent, with limited evidence of adaptivity to individual user needs during live surgical workflows.

Conclusions:

Current AI techniques provide a robust foundation for objective skill assessment, but they are not yet ready for autonomous teaming. Future development must prioritize model robustness, interpretability, and seamless integration into clinical environments to transition from standalone assessment tools to effective surgical teammates. Clinical Trial: The protocol was registered at OSF Registries [https://doi.org/10.17605/OSF.IO/PQWS5]