Textual descriptions of radiologic image findings play a critical role in GPT-4 with vision–based differential diagnosis, underlining the importance of radiologist experts even in multimodal large language models.

Summary This feasibility study investigated the spatial heterogeneity of the lumbar vertebral bone marrow using chemical shift encoding–based water-fat MRI. Acquired texture features like contrast and dissimilarity allowed for differentiation of pre- and postmenopausal women and may serve as imaging biomarkers in the future. Introduction While the vertebral bone marrow fat using chemical shift encoding water-fat magnetic resonance imaging (MRI) has been extensively studied, its spatial heterogeneity has not been analyzed yet. Therefore, this feasibility study investigated the spatial heterogeneity of the lumbar vertebral bone marrow by using texture analysis in proton density fat fraction (PDFF) maps. Methods Forty-one healthy pre- and postmenopausal women were recruited for this study (premenopausal (n = 15) 30 ± 7 years, postmenopausal (n = 26) 65 ± 7 years). An eight-echo 3D spoiled gradient echo sequence was used for chemical shift encoding–based water-fat separation at the lumbar spine. Vertebral bodies L1 to L5 were manually segmented. Mean PDFF values and texture features were extracted at each vertebral level, namely variance, skewness, and kurtosis, using statistical moments and second-order features (energy, contrast, correlation, homogeneity, dissimilarity, entropy, variance, and sum average). Parameters were compared between pre- and postmenopausal women and vertebral levels. Results PDFF was significantly higher in post- than in premenopausal women (49.37 ± 8.14% versus 27.76 ± 7.30%, p < 0.05). Furthermore, PDFF increased from L1 to L5 (L1 37.93 ± 12.85%, L2 38.81 ± 12.77%, L3 40.23 ± 12.72%, L4 42.80 ± 13.27%, L5 45.21 ± 14.55%, p < 0.05). Bone marrow heterogeneity based on texture analysis was significantly (p < 0.05) increased in postmenopausal women. Contrast and dissimilarity performed best in differentiating pre- and postmenopausal women (AUC = 0.97 and 0.96, respectively), not significantly different compared with PDFF (AUC = 0.97). Conclusion Conclusively, an increased bone marrow heterogeneity could be observed in postmenopausal women. In the future, texture parameters might provide additional information to detect and monitor vertebral bone marrow alterations due to aging or hormonal changes beyond conventional anatomic imaging.

Successful adoption of artificial intelligence (AI) in medical imaging requires medical professionals to understand underlying principles and techniques. However, educational offerings tailored to the need of medical professionals are scarce. To fill this gap, we created the course “AI for Doctors: Medical Imaging”. An analysis of participants’ opinions on AI and self-perceived skills rated on a five-point Likert scale was conducted before and after the course. The participants’ attitude towards AI in medical imaging was very optimistic before and after the course. However, deeper knowledge of AI and the process for validating and deploying it resulted in significantly less overoptimism with respect to perceivable patient benefits through AI (p = 0.020). Self-assessed skill ratings significantly improved after the course, and the appreciation of the course content was very positive. However, we observed a substantial drop-out rate, mostly attributed to the lack of time of medical professionals. There is a high demand for educational offerings regarding AI in medical imaging among medical professionals, and better education may lead to a more realistic appreciation of clinical adoption. However, time constraints imposed by a busy clinical schedule need to be taken into account for successful education of medical professionals.

Commercial software based on artificial intelligence (AI) is entering clinical practice in neuroradiology. Consequently, medico-legal aspects of using Software as a Medical Device (SaMD) become increasingly important. These medico-legal issues warrant an interdisciplinary approach and may affect the way we work in daily practice. In this article, we seek to address three major topics: medical malpractice liability, regulation of AI-based medical devices, and privacy protection in shared medical imaging data, thereby focusing on the legal frameworks of the European Union and the USA. As many of the presented concepts are very complex and, in part, remain yet unsolved, this article is not meant to be comprehensive but rather thought-provoking. The goal is to engage clinical neuroradiologists in the debate and equip them to actively shape these topics in the future.