# Effect of Reporting Mode and Clinical Experience on Radiologists' Gaze and Image Analysis Behavior in Chest Radiography

arXiv: https://arxiv.org/abs/2510.16070 · HTML: https://mlsanity.com/paper/2510.16070/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 22 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging](https://mlsanity.com/paper/2302.01622/) (2023)
- [Collaborative Training of Medical Artificial Intelligence Models with non-uniform Labels](https://mlsanity.com/paper/2211.13606/) (2022)

## Built on by (most cited first, 1 of 1)

- [From Clinical Intent to Clinical Model: Autonomous Coding-Agents for Clinician-driven AI Development](https://mlsanity.com/paper/2604.17110/) (2026)

## Abstract (arXiv)

Structured reporting (SR) and artificial intelligence (AI) may transform how radiologists interact with imaging studies. This prospective study (July to December 2024) evaluated the impact of three reporting modes: free-text (FT), structured reporting (SR), and AI-assisted structured reporting (AI-SR), on image analysis behavior, diagnostic accuracy, efficiency, and user experience. Four novice and four non-novice readers (radiologists and medical students) each analyzed 35 bedside chest radiographs per session using a customized viewer and an eye-tracking system. Outcomes included diagnostic accuracy (compared with expert consensus using Cohen's kappa), reporting time per radiograph, eye-tracking metrics, and questionnaire-based user experience. Statistical analysis used generalized linear mixed models with Bonferroni post-hoc tests with a significance level of (P ≤ .01). Diagnostic accuracy was similar in FT (kappa = 0.58) and SR (kappa = 0.60) but higher in AI-SR (kappa = 0.71, P < .001). Reporting times decreased from 88 ± 38 s (FT) to 37 ± 18 s (SR) and 25 ± 9 s (AI-SR) (P < .001). Saccade counts for the radiograph field (205 ± 135 (FT), 123 ± 88 (SR), 97 ± 58 (AI-SR)) and total fixation duration for the report field (11 ± 5 s (FT), 5 ± 3 s (SR), 4 ± 1 s (AI-SR)) were lower with SR and AI-SR (P < .001 each). Novice readers shifted gaze towards the radiograph in SR, while non-novice readers maintained their focus on the radiograph. AI-SR was the preferred mode. In conclusion, SR improves efficiency by guiding visual attention toward the image, and AI-prefilled SR further enhances diagnostic accuracy and user satisfaction.