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REVIEW 4 major objections 4 minor 46 references

Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Radiologists' gaze gives away AI-generated chest X-rays

desk verdict A genuinely first look at how radiologists' gaze differs between real and AI-generated chest X-rays, but the headline claim of 'significant differences' rests on descriptive statistics only and, with n=16, the observed effects could easily be sampling noise. read the letter →

arxiv 2504.15007 v2 pith:WK6W7UES submitted 2025-04-21 cs.CV cs.HC

David C Wong , Bin Wang , Gorkem Durak , Marouane Tliba , Mohamed Amine Kerkouri , Aladine Chetouani , Ahmet Enis Cetin , Cagdas Topel
show 16 more authors
Nicolo Gennaro Camila Vendrami Tugce Agirlar Trabzonlu Amir Ali Rahsepar Laetitia Perronne Matthew Antalek Onural Ozturk Gokcan Okur Andrew C. Gordon Ayis Pyrros Frank H Miller Amir A Borhani Hatice Savas Eric M. Hart Elizabeth A Krupinski Ulas Bagci
This is my paper · ORCID
classification cs.CVcs.HC
keywords eyetrackingmedicalimaginggazebehaviorAI-generatedimageschestX-rayfixationbiasmapssaccadesradiologistvisualsearch
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper asks whether expert radiologists look at real and AI-generated chest X-rays differently, and reports that they do: gaze statistics diverge most at the extremes of viewing time, in longest and shortest fixations, while first and last fixations behave nearly identically. The authors build a paired set of real and synthetic chest X-rays generated from the same report text, record eye movements of sixteen radiologists, and compare saccade amplitudes, directions, fixation durations, and spatial bias maps. If the finding holds, gaze behavior is a measurable signal of how synthetic images alter visual search and cognitive load, with consequences for AI model evaluation, radiology training, and safety guidelines. The study is a new application of established eye-tracking methodology rather than a new theoretical claim.

What carries the argument

The mechanism is scanpath analysis of eye-tracking data, organized into fixation bias maps and saccade distributions. Fixations are defined by velocity and acceleration thresholds from a head-mounted eye tracker, and the analysis isolates four temporal landmarks, first, last, longest, and shortest fixations, plus saccadic amplitude, direction, and their joint distribution. The synthetic stimuli are produced by a latent-diffusion model conditioned on the same radiology report text as the real images, creating matched real-fake pairs. Spatial comparison uses saliency metrics, correlation coefficient, KL divergence, and similarity, between real and fake bias maps. This machinery turns a hard-to-verbalize perceptual difference into quantitative distributions that can be compared.

What would settle it

Recomputing every metric for each radiologist individually would settle it: if only a few participants show the longest and shortest fixation shift or the saccade-amplitude change, or if the differences vanish after matching images for low-level difficulty, the claim of a general gaze shift on AI-generated X-rays fails.

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Extended reading notes

Core claim

The paper's central claim is that radiologists' visual search patterns shift measurably when they view AI-generated chest X-rays, and the shift is concentrated in the temporal extremes of attention rather than in initial engagement. Mean and median saccadic amplitudes are smaller for fake images while the maximum amplitude is larger, which the authors read as more cautious, uncertain scanning; longest fixations are slightly longer on fake images and shortest fixations slightly longer on real images, and the spatial bias maps for these two conditions correlate only about 0.19 to 0.20, versus 0.48 to 0.52 for first and last fixations, with higher KL divergence and lower similarity. First and last fixations are similar across image types, suggesting that entry and exit attention are captured in the same way, while prolonged and brief attention differ. The authors conclude that fake images may demand more scrutiny or present distinct visual features that require different viewing strategies.

Load-bearing premise

The pooled analysis assumes that sixteen radiologists with different experience levels and subspecialties share one consistent gaze response to synthetic images, and that the group-level maps do not hide a minority driving the differences.

Editorial extensions

If this is right

  • Radiologists' initial and final attention can be expected to behave the same on real and AI-generated chest X-rays, so tools that rely on entry-point gaze may transfer between the two image types.
  • Longest and shortest fixation patterns form a behavioral marker for synthetic-image processing, which could be used as a gaze-based check of whether generated images elicit natural search behavior.
  • AI-generated images that provoke larger maximum saccades and smaller mean saccades may impose higher cognitive load, and generative models could be tuned to reduce this gap.
  • Gaze statistics can complement image-level realism scores when evaluating whether synthetic medical images are acceptable for clinical workflows.
  • The paired real-fake dataset, with matched report text, provides a reusable stimulus set for eye-tracking studies of AI-generated medical images.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the sixteen radiologists span very different experience levels, one extension not pursued here is computing the same metrics per radiologist; the pooled bias maps might hide a subgroup whose gaze differs sharply from the rest.
  • Gaze signals could be repurposed as a passive detector of AI-generated images: if fixation extremes are reliably different, a radiologist's pattern of eye movements might flag suspicious images even when the radiologist cannot consciously identify them.
  • A direct testable extension would link the gaze differences to diagnostic accuracy on the same images, asking whether longer longest fixations on fake images coincide with correct rejection or with confusion.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper reports an eye-tracking study in which 16 radiologists viewed real and AI-generated (RoentGen) chest X-rays, with the goal of detecting shifts in gaze behavior between the two image types. The authors analyze saccade amplitude and direction distributions, fixation durations for first, last, longest, and shortest fixations, and spatial bias maps quantified by saliency metrics (correlation coefficient, KL divergence, similarity). The abstract and conclusion claim that radiologists exhibit significant differences in visual behavior between real and fake images, particularly in longest and shortest fixations and in saccadic amplitude distributions.

Significance. If the central claim were properly supported, this would be a useful contribution to the growing literature on AI-generated medical images and their effect on expert visual search, with implications for radiologist training, image generation quality, and regulatory guidance. The paper's strengths are its novel research question, the construction of a paired real/fake chest X-ray dataset using matched reports, and the descriptive characterization of multiple gaze features including joint saccade distributions and temporal fixation subtypes. However, the study currently provides only descriptive statistics and aggregate saliency metrics, with no inferential statistics, so the headline claim of 'significant differences' is not established. The hierarchical structure of the data is also ignored, making the reported aggregate comparisons difficult to interpret at the population level.

major comments (4)
  1. [Abstract, §3.4, §3.5, §5] The central claim of 'significant differences' in gaze behavior is not supported by any inferential statistics. Tables 1 and 2 report only means, medians, standard deviations, and ranges; Table 3 reports saliency metric values. No p-values, confidence intervals, effect sizes, or hypothesis tests are provided. For instance, the longest-fixation mean difference in Table 2 is 575.9 ms (fake) versus 559.3 ms (real) with standard deviations of roughly 250 ms, and the saccade-amplitude difference in Table 1 is 5.71 vs 5.93 degrees with standard deviations near 4.6 degrees. With 16 participants, these differences are plausibly within sampling error, so the word 'significant' in the abstract, Section 3.4, and Section 5 is unjustified as written.
  2. [§3.3, §3.4] The analysis pools thousands of fixations and saccades across participants and stimuli without accounting for the hierarchical structure of eye-tracking data. Fixations are nested within participants and within images, so the effective sample size for the real-versus-fake comparison is at most 16 radiologists (or the number of distinct stimuli). Treating each event as an independent observation would inflate precision. The manuscript should either use a mixed-effects model with random intercepts for participants and images, or a participant-level summary analysis, before drawing conclusions about radiologists as a population.
  3. [§3.5, Table 3] The saliency metrics comparing real and fake bias maps are presented without any uncertainty quantification or significance testing. The values (e.g., CC = 0.1938 for Longest, CC = 0.5158 for First) are descriptive comparisons of group-aggregated maps, but no confidence intervals or permutation tests are provided. The conclusion in Section 5 that 'the alignment weakens significantly' is therefore not supported by the reported analyses. Additionally, the interpretation of what constitutes a meaningful difference in CC, KL, or SIM is not defined.
  4. [§3.3, §3.4] The study pools radiologists with heterogeneous experience levels (2 with 0–5 years, 6 with 6–10 years, 4 with 10–20 years, 3 with over 20 years) and different subspecialties. Because expertise is known to affect visual search behavior, the pooled aggregate statistics may mask subgroup differences or be driven by a few individuals. The paper should report whether the observed patterns are consistent across experience levels or justify pooling, for example by including experience as a covariate or performing a subgroup analysis.
minor comments (4)
  1. [§3.2.2] The text says '30 reports being randomly selected from this set' but does not specify how many unique images were shown to each participant; please clarify whether each radiologist viewed all 30 real and 30 fake images in a single trial and how image order was randomized.
  2. [Figure 2] The caption lists '(a+b)' three times; the third pair should refer to subplots (e) and (f) for the joint distributions.
  3. [§3.4.4] In the bullet on shortest fixations, the text states 'quick glances are more common in real images,' but the descriptive statistics show only that the mean and median are slightly higher; this interpretive claim goes beyond the presented data.
  4. [References] Several references are incomplete or inconsistent in formatting (e.g., [8] lacks publication venue details, [21] lacks page numbers, [33] is listed as both a journal article and an arXiv preprint). Please harmonize the bibliography to the journal style.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the study is a direct empirical measurement and comparison of eye-tracking data, with no fitted model, prediction derived from assumptions, or self-citation chain that makes outputs equal to inputs.

full rationale

This paper reports an eye-tracking experiment in which radiologists viewed real and AI-generated chest X-rays, and the analysis consists of descriptive statistics of saccades and fixations plus saliency-map comparisons between the real and fake conditions. There is no derivation chain in the sense of a model making predictions from fitted parameters: the gaze data are measured directly, aggregated into tables and bias maps, and compared using standard saliency metrics (CC, KL divergence, similarity). The claim that there are 'significant differences' is not supported by inferential statistics in the paper, but that is a statistical-evidence problem, not circularity. The paper does not define its outcome in terms of its input, does not fit a parameter and then rename it a prediction, and does not rely on a load-bearing self-citation to justify its central comparison. The RoentGen generation model, MIMIC-CXR data, and eye-tracking apparatus are external tools and datasets, not outputs of the present analysis. Therefore no circular step can be exhibited, and the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The study introduces no fitted parameters or new theoretical entities. Its load-bearing assumptions are domain assumptions about fixation detection, the representativeness of the generative model, and the reliability of group-level maps.

assumptions (3)
  • domain assumption Fixation detection parameters (saccade velocity less than 30 degrees per second and acceleration less than 8000 degrees per second squared) correctly identify fixations in all participants.
    The EyeLink 1000 Plus is configured with these thresholds (Section 3.1), but they may not be optimal for all participants or image conditions, and no validation is provided.
  • domain assumption RoentGen-generated images are representative of AI-generated chest X-rays as a category.
    The conclusions about AI-generated images rely on a single generative model (RoentGen, Section 3.2.1), so the findings may not transfer to other generative architectures.
  • domain assumption Aggregating fixations from all participants yields stable group-level attention maps for each condition.
    With only 16 observers, the bias maps in Figure 3 may be noisy, and no inter-observer reliability is reported.

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Cite this review

Pith. "Pith review of Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images." pith.science (2026). https://pith.science/paper/WK6W7UES

@misc{pith2026250415007,
  author       = {Pith},
  title        = {Pith review of: Shifts in Doctors' Eye Movements Between Real and AI-Generated Medical Images},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WK6W7UES}},
  note         = {Machine review of arXiv:2504.15007}
}
read the original abstract

Eye-tracking analysis plays a vital role in medical imaging, providing key insights into how radiologists visually interpret and diagnose clinical cases. In this work, we first analyze radiologists' attention and agreement by measuring the distribution of various eye-movement patterns, including saccades direction, amplitude, and their joint distribution. These metrics help uncover patterns in attention allocation and diagnostic strategies. Furthermore, we investigate whether and how doctors' gaze behavior shifts when viewing authentic (Real) versus deep-learning-generated (Fake) images. To achieve this, we examine fixation bias maps, focusing on first, last, short, and longest fixations independently, along with detailed saccades patterns, to quantify differences in gaze distribution and visual saliency between authentic and synthetic images.

Figures

Figures reproduced from arXiv: 2504.15007 by the authors.

Figure 1
Figure 1. The first row displays real X-ray images, while the second row shows generated images. The [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. (a+b) Comparision of saccadic length distribution, (c+d) Comparision of saccadic direction [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Visualization of Bias Maps between real and fake in the order of: First, Last, Longest, Shortest. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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    Matthias K¨ ummerer, Thomas S. A. Wallis, and Matthias Bethge, “Saliency benchmarking made easy: Separating models, maps and metrics,” in Computer Vision – ECCV 2018 , Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss, Eds. Lecture Notes in Computer Scien...

    2018

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.