Objectives <p>To evaluate whether collaborative assistance from an artificial intelligence-based tool that proposes partial radiology report content can improve reporting efficiency and radiologist satisfaction in chest X-ray interpretation, without compromising report quality.</p> Materials and methods <p>In a retrospective study, three radiologists reported 50 MIMIC-CXR chest X-rays twice, once with artificial intelligence (AI) assistance and once without. A specialized large vision-language model (LVLM) provided real-time suggestions, which could be accepted, modified or rejected. The study evaluated writing time, suggestion acceptance, report length and quality and assessed usability and suggestion quality on a 5-point Likert-scale questionnaire. Statistical analysis used paired <i>t</i>-tests or Wilcoxon signed-rank tests based on normality.</p> Results <p>AI assistance reduced mean writing time by 7.80% (<i>p</i> = 0.08), with significant gains for complex reports (18.34%, <i>p</i> &lt; 0.001). Efficiency improvements correlated with suggestion acceptance and were user-dependent, with benefits up to 27.24% (CI: [17.34, 37.14], <i>p</i> &lt; 0.001) for radiologists with high acceptance. Report quality and length remained stable, indicating preserved diagnostic accuracy without degradation. Radiologists rated the tool highly for ease of use (mean: 4.33) and desired regular use (mean: 4), noting minimal errors (mean: 1.67).</p> Conclusion <p>Collaborative AI assistance with an LVLM can improve reporting efficiency if well adopted, particularly for complex cases, without compromising quality, and is well-received by radiologists. These exploratory findings suggest potential to optimize radiology workflows through collaborative reporting and warrant prospective validation in clinical settings.</p> Critical relevance statement <p>This study critically evaluates a collaborative AI-assisted reporting tool for chest X-rays, demonstrating its potential to enhance radiologist efficiency without compromising automatically measured report quality, thereby demonstrating a potential path for practical integration of AI into clinical radiology workflows.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>A collaborative vision-language model supported radiology workflow is proposed, and its effectiveness is studied in a user study.</p> </ItemContent> <ItemContent> <p>Mean writing time for a radiology report decreases with AI support without affecting report quality.</p> </ItemContent> <ItemContent> <p>The AI-assisted tool was rated highly for usability and integration into clinical workflow, supporting its practical adoption in radiology reporting.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>