ReviewJMIR AI2026
Machine Learning for Human-Autonomy Teaming in Surgical Skill Assessment: Scoping Review.
Review in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHuman-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.
objectiveThis scoping review aims to map the current landscape of machine learning methods for surgical skill assessment and evaluate their technical readiness for integration into functional HAT systems.
methodsFollowing PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a systematic search across 3 major scientific databases (PubMed, IEEE Xplore, and Web of Science). We identified and analyzed 92 peer-reviewed studies published between 2019 and 2025. The review focused on data modalities (kinematics, video, and biosignals), model architectures, and validation environments. To assess translational maturity, each study was evaluated using the NASA technology readiness level (TRL) scale and mapped to the Yang et al levels of autonomy for surgical robotics, alongside 6 additional dimensions: real-time capability, interpretability, adaptivity, validation environment, external validation, and clinical integration.
resultsOur 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. The TRL analysis shows that 77.2% (71/92) of studies remain at TRL 3 (proof-of-concept), with only 2.2% (2/92) reaching TRL 5 or above. Furthermore, 96.7% (89/92) of studies operate at Yang autonomy level 0, providing no autonomous functionality beyond classification. We identified that 91.3% (84/92) of models are static, with no user-specific adaptation, 38% (35/92) lack any external validation, and validation practices remain inconsistent across the field.
conclusionsCurrent 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 stand-alone assessment tools to effective surgical teammates.
trial registrationOSF Registries 10.17605/OSF.IO/PQWS5; https://osf.io/pqws5.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.