# Deep Learning Enhances Radiologists' Detection of Potential Spinal Alignancies in CT Scans
## 4 Discussion
While these previous studies have primarily focused on DLAs improving performance in explicit detection tasks, our study provides a novel perspective by highlighting the potential benefits of a tool for reducing incidental findings during routine reporting. This observation helps to explain the relatively lower baseline performance of participants in our study. Since a complete radiological report includes information regarding all body parts that can be seen on the scan, we expected insights on all findings that the six radiologists encountered while analyzing the images. Our findings support the already existing issue of missed spinal findings in clinical practice, which might rely on the aforementioned reasons for perceptual errors in radiology. It is worth noting that the sensitivities and specificities are calculated only for potentially malignant spinal lesion findings and not findings for all other organs (lungs, liver, kidneys). Hence, the lowered sensitivities and specificities due to many spinal lesions being missed during the initial diagnostic process without the assistance of the DLA. Indeed, the DLA reported false-positive findings. It is important to mention that the radiologists' assessments were not solely based on the AI's predictions for false-positive lesions. They considered various factors, including other findings identified as true positives by the AI. Furthermore, based on the AI's indication of true-positive spinal conditions, four participants requested further follow-up or diagnostic tests. These requests were made to investigate potential abnormalities or conditions related to the spine, as suggested by the AI. Several studies have already investigated the potential of deep-learning systems for pathology assessment of the spine [13; 28; 32; 33; 34; 35]. Although deep-learning algorithms (DLAs) can demonstrate accuracy in lesion detection on par with radiologists, it is crucial to consider their real-world implementation in datasets that differ significantly from the training data. DLAs may face challenges in such scenarios and are more prone to producing incorrect results. However, by addressing these challenges through ongoing research and fine-tuning, we can further optimize the effectiveness of DLAs in practical settings. It is, therefore, crucial to focus on the effects and performance of the human reader when in conjunction with this emerging technology. While our DLA's performance may not have demonstrated superiority over expert human radiologists, there is potential for radiologists of all experience levels to benefit from its second reader function. It is essential to conduct future studies with larger cohorts and greater sample sizes to validate any hypothetical benefits and potential risks associated with AI. These studies will ensure the safe and effective integration of machine learning software into the clinical setting. This study has several limitations. Since the allocated time between the two reading sessions was set for only ten days, one could argue that the first reading could have biased the participants' performance in the second session. We conducted the McNemar test to assess significance. The test (\(p=0.25\)) confirmed that the study did not yield statistically significant results (\(p>0.05\)). This outcome is linked to the small number of radiologist participants used for this study and probably could not prove generalizable effects. However, it is important to note that this finding presents an opportunity for improvement in future studies. The number of patient studies investigated by the participants was also small and should be increased for further studies. Moreover, the training that radiologists receive, their reporting style, and thus their attention to detail differ between countries and subspecialties.