A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling

Daniel A. Donoho; Eric Fithian; Jack Cook; John Zhu; Kirill Skobelev; Margaux Masson-Forsythe; Neeraj Mainkar; Sandeep Angara; Shauna Otto; X.Y. Han

arxiv: 2603.27341 · v3 · pith:M25E5YKUnew · submitted 2026-03-28 · 💻 cs.AI · cs.CV· cs.LG

A Comparative Study in Surgical AI: Potential and Limitations of Data, Compute, and Scaling

Kirill Skobelev , Eric Fithian , Yegor Baranovski , Jack Cook , Sandeep Angara , Shauna Otto , Zhuang-Fang Yi , John Zhu

show 4 more authors

Daniel A. Donoho X.Y. Han Neeraj Mainkar Margaux Masson-Forsythe

This is my paper

Pith reviewed 2026-05-21 10:57 UTC · model grok-4.3

classification 💻 cs.AI cs.CVcs.LG

keywords surgical tool detectionvision language modelsAI scalingneurosurgerymodel performancediminishing returnsbiomedical AIcomputer vision

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The pith

Vision language models fall short on neurosurgical tool detection even at multi-billion parameter scales.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates whether scaling up model size and training data can make AI useful for surgery, a domain with millions of hours of video generated yearly but high costs for data preparation and compute. Experiments focus on the task of detecting surgical tools in neurosurgery videos using current vision-language models. Results show poor performance that improves only modestly with larger models and more training, suggesting that some barriers remain even as resources increase. A sympathetic reader would care because this questions how soon general AI advances can translate into practical help for surgeons.

Core claim

Even with multi-billion parameter vision language models and extensive training, performance on the task of surgical tool detection in neurosurgery falls short, while scaling experiments show that increasing model size and training time produces only diminishing improvements that leave significant obstacles intact across architectures.

What carries the argument

Scaling experiments on vision-language models applied to surgical tool detection, which measure performance changes as model size and training duration increase.

If this is right

Surgical AI applications may require more than additional compute and data to reach reliable performance.
High expertise needed for labeling surgical data creates a persistent bottleneck that general scaling does not address.
Obstacles in surgical use cases persist across diverse model architectures rather than being model-specific.
Data and label availability are not necessarily the sole limiting factors for AI in surgery.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If basic tool detection proves difficult, more complex real-time tasks such as predicting surgical complications may present even steeper challenges for current approaches.
Exploration of simulation-generated data could reduce reliance on expert-labeled surgical videos.
Parallel scaling tests in other variable, high-stakes visual environments like emergency response imaging might reveal similar patterns.
Hybrid systems that combine scaled vision models with explicit domain rules for tool interactions offer a testable direction beyond pure scaling.

Load-bearing premise

The specific task of tool detection in neurosurgery using the chosen models and datasets is representative of general obstacles in surgical AI that scaling cannot overcome.

What would settle it

A demonstration that a new or modified vision-language model reaches substantially higher accuracy on the same neurosurgical tool detection task after only moderate increases in size or training data would show that the obstacles can be scaled away.

read the original abstract

Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but surgical benchmarks in particular are often missing from prominent medical benchmark suites. Since surgery requires integrating disparate tasks, generally-capable AI models could be particularly attractive as a collaborative tool if performance could be improved. On the one hand, the canonical approach of scaling architecture size and training data is attractive, especially since there are millions of hours of surgical video data generated per year. On the other hand, preparing surgical data for AI training requires significantly higher levels of professional expertise, and training on that data requires expensive computational resources. These trade-offs paint an uncertain picture of whether and to-what-extent modern AI could aid surgical practice. In this paper, we explore this question through a case study of surgical tool detection using state-of-the-art AI methods available in 2026. We demonstrate that even with multi-billion parameter models and extensive training, current Vision Language Models fall short in the seemingly simple task of tool detection in neurosurgery. Additionally, we show scaling experiments indicating that increasing model size and training time only leads to diminishing improvements in relevant performance metrics. Thus, our experiments suggest that current models could still face significant obstacles in surgical use cases. Moreover, some obstacles cannot be simply ``scaled away'' with additional compute and persist across diverse model architectures, raising the question of whether data and label availability are the only limiting factors. We discuss the main contributors to these constraints and advance potential solutions.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Scaling experiments on neurosurgical tool detection show diminishing returns, but the results need tighter controls on data quality before generalizing to limits across surgical AI.

read the letter

Colleague, the main thing here is that even multi-billion parameter vision-language models from 2026 fall short on detecting tools in neurosurgery videos, and the scaling runs show only small gains from bigger size or longer training that do not close the gap. They frame this as a case study on a domain with abundant video but costly expert labeling, and they run the scaling checks to test whether more compute fixes it. That application to surgical tool detection is the concrete new piece, and it is useful to see the diminishing returns documented in this setting rather than just cited from general scaling work. They also flag data preparation costs and sketch some directions for solutions, which keeps the discussion grounded in practice. The soft spots are the lack of reported numbers, baselines, error bars, or data split details in the abstract, which makes it difficult to judge the size of the shortfall or how much comes from the models versus the dataset. The stress-test point holds up on what is shown: if label noise, limited viewpoints, or task simplicity are driving the plateau, then the claim that obstacles persist across architectures and cannot be scaled away rests on weaker ground. They acknowledge data and label issues but would need to show the curves survive controls for those factors. This is for readers working on medical computer vision or surgical robotics who want empirical checks on scaling in constrained domains. A practitioner thinking about deployment would get a realistic data point, though general AI scaling researchers might not find much new. I would send it for peer review. The empirical case is worth referee time even with the gaps, as it raises a practical question that deserves tighter methods.

Referee Report

3 major / 2 minor

Summary. The paper presents a case study of surgical tool detection in neurosurgery using state-of-the-art Vision Language Models (VLMs) with multi-billion parameters. It reports that these models underperform on the task despite extensive training, and scaling experiments demonstrate diminishing returns in performance as model size and training time increase. The authors conclude that significant obstacles remain in surgical AI applications that cannot be resolved by scaling compute or data alone, discuss contributing factors such as data and label quality, and suggest potential solutions.

Significance. If the empirical results hold after addressing controls for data quality, this study would usefully document performance limits of current scaling approaches in a high-stakes, data-intensive domain. The scaling experiments constitute a concrete strength by providing direct evidence of plateaus rather than relying on extrapolation. The work could steer research toward hybrid methods that combine scaling with improved annotation pipelines or task-specific architectures.

major comments (3)

[Section 3] Section 3 (Experimental Setup): the description of baselines, data splits, exact metrics (e.g., mAP, precision-recall), and error-bar computation is insufficient to verify the claim that models 'fall short'; without these details the quantitative support for the central performance-gap conclusion cannot be assessed.
[Section 4] Section 4 (Scaling Experiments): the reported diminishing returns lack controls for annotation quality, label noise, or viewpoint diversity in the neurosurgery dataset; if these factors are not isolated, the plateau could reflect data constraints rather than intrinsic model limits, weakening the claim that obstacles 'cannot be simply scaled away'.
[Discussion] Discussion section: the generalization from tool detection in one neurosurgical dataset to 'surgical use cases' and 'obstacles that persist across diverse model architectures' requires additional experiments on at least one other surgical task or dataset to substantiate that the observed limits are not task-specific.

minor comments (2)

[Abstract] Abstract: replace the vague phrase 'relevant performance metrics' with the specific metrics used (e.g., mean average precision).
[Figures] Figure captions for scaling plots should explicitly state the x-axis units (parameters or FLOPs) and whether curves are averaged over multiple runs.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for their constructive and detailed feedback. We have revised the manuscript to address the concerns about experimental details and the scope of our conclusions. Point-by-point responses follow.

read point-by-point responses

Referee: [Section 3] Section 3 (Experimental Setup): the description of baselines, data splits, exact metrics (e.g., mAP, precision-recall), and error-bar computation is insufficient to verify the claim that models 'fall short'; without these details the quantitative support for the central performance-gap conclusion cannot be assessed.

Authors: We agree that additional details are required for reproducibility and verification. In the revised manuscript, Section 3 has been expanded to specify: the full list of baselines and their hyperparameters; the data split methodology (stratified 70/15/15 train/validation/test by surgical procedure); exact metrics including mAP@0.5, precision-recall curves, and F1 scores; and error-bar computation via standard deviation across five independent runs with different random seeds plus bootstrapped confidence intervals. These changes directly support assessment of the performance gaps reported. revision: yes
Referee: [Section 4] Section 4 (Scaling Experiments): the reported diminishing returns lack controls for annotation quality, label noise, or viewpoint diversity in the neurosurgery dataset; if these factors are not isolated, the plateau could reflect data constraints rather than intrinsic model limits, weakening the claim that obstacles 'cannot be simply scaled away'.

Authors: We acknowledge this limitation in our controls. The dataset was annotated by board-certified neurosurgeons with inter-annotator agreement checks, but we did not run explicit ablations on label noise or viewpoint diversity. The revision adds a dedicated paragraph in Section 4 and the Discussion that discusses these potential data constraints as contributing factors to the observed plateaus. We also include a supplementary analysis of performance versus training subset size to partially isolate data volume effects. Full isolation would require new controlled datasets, which exceeds the current study scope; we have therefore moderated the language to indicate that scaling alone is unlikely to resolve all obstacles while recognizing data quality as a possible confounder. revision: partial
Referee: [Discussion] Discussion section: the generalization from tool detection in one neurosurgical dataset to 'surgical use cases' and 'obstacles that persist across diverse model architectures' requires additional experiments on at least one other surgical task or dataset to substantiate that the observed limits are not task-specific.

Authors: We agree that broader validation would be ideal. The study is explicitly framed as a case study on neurosurgical tool detection. In the revised Discussion we have qualified all generalizations, stating that results apply to this task and dataset while noting that similar scaling behaviors may appear in other surgical domains. We have added explicit language recommending experiments on additional surgical tasks as future work. However, obtaining and annotating another high-quality surgical dataset at the required scale is not feasible with our current resources. revision: partial

standing simulated objections not resolved

Additional experiments on at least one other surgical task or dataset, as this would require new annotated data and substantial extra compute not available for the current study.

Circularity Check

0 steps flagged

Empirical scaling study with no derivation chain or self-referential predictions

full rationale

The paper conducts a case study of surgical tool detection in neurosurgery using state-of-the-art vision-language models, reporting experimental results on performance shortfalls and diminishing returns from increases in model size and training time. No mathematical equations, derivations, or predictive models are described that could reduce outputs to inputs by construction. Claims rest on direct measurements from scaling experiments rather than fitted parameters renamed as predictions or self-citations invoked as uniqueness theorems. The work is self-contained against its own experimental benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work is an empirical comparative study relying on standard machine learning practices.

pith-pipeline@v0.9.0 · 5857 in / 1134 out tokens · 39475 ms · 2026-05-21T10:57:44.820312+00:00 · methodology

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discussion (0)

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IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear

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Relation between the paper passage and the cited Recognition theorem.

We demonstrate that even with multi-billion parameter models and extensive training, current Vision Language Models fall short in the seemingly simple task of tool detection in neurosurgery. Additionally, we show scaling experiments indicating that increasing model size and training time only leads to diminishing improvements
IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

YOLOv12-m (26M parameters) achieves 54.73% exact match accuracy, outperforming all VLM-based methods while using 1,000× fewer parameters

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