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arxiv: 2605.11224 · v1 · submitted 2026-05-11 · 💻 cs.CV · cs.AI

Recognition: 3 theorem links

· Lean Theorem

ABRA: Agent Benchmark for Radiology Applications

Bulat Maksudov , Vladislav Kurenkov , Kathleen M. Curran , Alessandra Mileo
Authors on Pith no claims yet

Pith reviewed 2026-05-13 06:47 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords radiology agentsAI benchmarksmedical imagingDICOM viewerperception bottlenecktool callingagent evaluation
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The pith

Current AI agents reach 89% execution on radiology annotation but only 0-25% outcome success, with oracle perception inputs lifting outcomes to 69-100%.

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

ABRA is a benchmark that places agents inside a real DICOM viewer and server, requiring them to use 21 tools for slice navigation, windowing, annotation, and structured reporting instead of receiving pre-selected images. Ten models were tested across 655 tasks drawn from LIDC-IDRI, Duke Breast Cancer MRI, and NLST datasets. The models handled tool execution reliably yet produced correct medical outcomes in only a small fraction of cases. Supplying a simulated perfect detector as input raised outcome scores sharply, isolating the failure to visual perception rather than planning or tool use.

Core claim

In the ABRA benchmark, agents operate an OHIF viewer and Orthanc DICOM server through twenty-one function-calling tools spanning navigation, metadata queries, pixel annotation, and BI-RADS reporting. Ten evaluated models achieve at least 89% execution on real annotation tasks but only 0-25% outcome success; the paired oracle variant, which supplies findings from a simulated detector, raises outcome success to 69-100% across the same models and tasks.

What carries the argument

The ABRA environment and its twenty-one tools for viewer control, annotation, and reporting, evaluated through automatic scorers on Planning, Execution, and Outcome dimensions.

If this is right

  • Tool orchestration itself is not the primary obstacle for current radiology agents.
  • Perception accuracy directly determines whether an agent can complete diagnostic or reporting goals.
  • Interactive viewer benchmarks expose limitations hidden by static-image evaluation setups.
  • Medical agents will require tighter coupling between vision modules and action tools to reach usable performance.

Where Pith is reading between the lines

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

  • Advances in medical vision models could translate quickly into functional agent systems once perception improves.
  • Similar bottlenecks may appear in other domains where agents must interpret complex visual data before acting.
  • Future benchmarks could add live human-in-the-loop scoring to validate the automatic metrics.

Load-bearing premise

The automatic scorers correctly measure whether an agent has solved the medical task, and the programmatically generated tasks faithfully capture the difficulties of actual radiology work.

What would settle it

Running the identical ABRA annotation and reporting tasks with expert radiologists in the live viewer and comparing their outcome scores to the models, or testing newer vision models to check whether outcome scores rise without oracle inputs.

Figures

Figures reproduced from arXiv: 2605.11224 by Alessandra Mileo, Bulat Maksudov, Kathleen M. Curran, Vladislav Kurenkov.

Figure 1
Figure 1. Figure 1: Overview of the ABRA architecture. The benchmark controller draws tasks from the suite, [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Task distribution by difficulty and type for ABRA. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Agent execution path inside OHIF. A user prompt (D) submitted via the embedded [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Agent system prompt template. {task_description} is the per-task string from [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗
read the original abstract

Existing medical-agent benchmarks deliver imaging as pre-selected samples, never as an environment the agent must navigate. We introduce ABRA, a radiology-agent benchmark in which the agent operates an OHIF viewer and an Orthanc DICOM server through twenty-one function-calling tools that span slice navigation, windowing, series selection, pixel-coordinate annotation, and structured reporting. ABRA contains 655 programmatically generated tasks across three difficulty tiers and eight types (viewer control, metadata QA, vision probe, annotation, longitudinal comparison, BI-RADS reporting, and oracle variants of annotation and BI-RADS reporting), drawn from LIDC-IDRI, Duke Breast Cancer MRI, and NLST New-Lesion LongCT. Each episode is scored along Planning, Execution, and Outcome (Bluethgen et al., 2025) by task-type-specific automatic scorers. Ten current models, five closed-weight and five open-weight, reach at least 89% Execution on real annotation but only 0-25% Outcome; on the paired oracle variant where a simulated detector supplies the finding, Outcome on the same task reaches 69-100% across the models evaluated, localising the bottleneck to perception rather than tool orchestration. Code, task generators, and scorers are released at https://github.com/Luab/ABRA

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.

Referee Report

2 major / 3 minor

Summary. The manuscript introduces ABRA, a benchmark for radiology agents operating an OHIF viewer and Orthanc DICOM server through 21 function-calling tools for slice navigation, windowing, annotation, and structured reporting. It evaluates 10 models (5 closed, 5 open) on 655 programmatically generated tasks across 8 types and 3 difficulty tiers drawn from LIDC-IDRI, Duke Breast Cancer MRI, and NLST. Results show ≥89% Execution but only 0-25% Outcome on real annotation/BI-RADS tasks, rising to 69-100% on oracle variants with simulated detectors, localizing the performance bottleneck to perception rather than tool use. Code, generators, and scorers are released.

Significance. If the automatic scorers are shown to be reliable, this work supplies a valuable interactive benchmark that moves beyond static-image medical-agent evaluations and supplies concrete evidence of a perception bottleneck in current VLMs. The public release of task generators and scorers is a clear strength that supports reproducibility and extension by the community.

major comments (2)
  1. [§4 and §4.3] §4 (Evaluation Protocol) and §4.3 (Automatic Scorers): The headline localization of the bottleneck to perception rests on the large Outcome gap (0-25% real vs. 69-100% oracle). The paper relies on task-type-specific automatic scorers (citing Bluethgen et al. 2025) but reports no inter-rater agreement study, human calibration set, or validation against expert radiologists. Because Execution is already ≥89%, any systematic bias in the Outcome scorer (e.g., penalizing semantically correct but stylistically variant reports) directly affects the central claim.
  2. [§3.2] §3.2 (Task Generation): The 655 tasks are programmatically derived from the cited public datasets, yet the manuscript provides no quantitative assessment of how faithfully the generated tasks capture the distribution of real radiology challenges (ambiguous findings, multi-lesion cases, or clinical workflow variability). This limits the strength of the generalization that the observed perception bottleneck applies to clinical practice.
minor comments (3)
  1. [Table 2] Table 2 (Model Performance): The per-model Outcome scores would benefit from confidence intervals or statistical tests to support the claim that the oracle improvement is consistent across all ten models.
  2. [Figure 1] Figure 1 (Tool Taxonomy): The diagram of the 21 tools would be clearer if it explicitly grouped tools by category (navigation, annotation, reporting) and indicated which are used in the oracle variants.
  3. [References] The reference list should include the full bibliographic details for Bluethgen et al. 2025 if it is not already present.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on our manuscript introducing ABRA. We appreciate the emphasis on the reliability of the automatic scorers and the fidelity of the task generation process. Below, we provide point-by-point responses to the major comments and indicate the revisions we will incorporate in the updated version.

read point-by-point responses

  1. Referee: [§4 and §4.3] §4 (Evaluation Protocol) and §4.3 (Automatic Scorers): The headline localization of the bottleneck to perception rests on the large Outcome gap (0-25% real vs. 69-100% oracle). The paper relies on task-type-specific automatic scorers (citing Bluethgen et al. 2025) but reports no inter-rater agreement study, human calibration set, or validation against expert radiologists. Because Execution is already ≥89%, any systematic bias in the Outcome scorer (e.g., penalizing semantically correct but stylistically variant reports) directly affects the central claim.

    Authors: We agree that additional validation of the automatic scorers would strengthen the central claim regarding the perception bottleneck. While the scorers follow the methodology validated in Bluethgen et al. (2025), the current manuscript does not present a dedicated inter-rater study. In the revised manuscript, we will add a new subsection under §4.3 that includes a human calibration study. Specifically, we will have two expert radiologists independently score a random sample of 100 tasks (50 annotation and 50 BI-RADS) and report Cohen's kappa agreement between human and automatic scores, as well as between the two radiologists. This will allow us to quantify any potential bias in the Outcome metric. revision: yes

  2. Referee: [§3.2] §3.2 (Task Generation): The 655 tasks are programmatically derived from the cited public datasets, yet the manuscript provides no quantitative assessment of how faithfully the generated tasks capture the distribution of real radiology challenges (ambiguous findings, multi-lesion cases, or clinical workflow variability). This limits the strength of the generalization that the observed perception bottleneck applies to clinical practice.

    Authors: We acknowledge the value of demonstrating how well the programmatically generated tasks reflect real-world radiology distributions. The tasks are derived directly from the annotations and metadata in LIDC-IDRI, Duke Breast Cancer MRI, and NLST, which are established clinical datasets. To address this, we will include in the revised §3.2 a quantitative comparison: for example, histograms of lesion diameters and counts per case in ABRA tasks versus the source datasets' reported statistics. We will also add a limitations paragraph noting that ABRA prioritizes tasks amenable to automatic scoring and does not fully capture highly ambiguous or workflow-variable cases, suggesting this as an avenue for future extensions. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical benchmark with direct evaluations

full rationale

The paper introduces a radiology agent benchmark and reports model performance via direct execution on programmatically generated tasks drawn from public datasets. Scoring relies on an external citation (Bluethgen et al. 2025) rather than self-citation or internal fitting. The real-vs-oracle comparison is a controlled experimental contrast, not a derivation that reduces to its own inputs by construction. No equations, parameter fitting, uniqueness theorems, or ansatzes appear; results are released artifacts evaluated on the defined tasks. This is a standard empirical benchmark study whose central claims rest on observable performance gaps rather than any self-referential reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper builds upon existing medical imaging datasets and adopts a scoring framework from prior work (Bluethgen et al., 2025). No free parameters or new entities are introduced; the novelty lies in the tool interface and task suite.

axioms (1)
  • domain assumption Scoring along Planning, Execution, and Outcome as per Bluethgen et al., 2025
    The paper adopts this prior scoring method for evaluating agent episodes.

pith-pipeline@v0.9.0 · 5539 in / 1386 out tokens · 132671 ms · 2026-05-13T06:47:40.905220+00:00 · methodology

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Reference graph

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