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For better collaboration among radiologists, the interpretation workload should be evaluated, considering the difference in difficulty for interpreting each case. However, objective evaluation of difficulty is challenging. This study proposes a multimodal classifier of structural and textual data to predict difficulty based on order information and patient data without using images. The classifier showed performance with a specificity of 0.9 and an accuracy of 0.7.

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Patient perception involves a patient's thoughts and beliefs regarding their health status. It is also associated with medical compliance and outcomes. However, discrepancies often arise between patient perception and physicians' documentation within the medical records, resulting in misunderstanding and suboptimal doctor-patient communication. In this study, we assessed the efficacy of generative artificial intelligence (AI) in comparing the content of patient perception as recorded in patient questionnaires and physicians' records of the Department of Breast Surgery. We evaluated the precision and recall of the generative AI by comparison with human-created ground truth. Our results demonstrated the high performance of the generative AI in comprehending and contrasting symptoms and the entire content recorded differently by patients and physicians, with F1 scores ranging from 0.77 to 0.97. These results highlight the potential contribution of a generative AI to deeper mutual comprehension in healthcare scenarios.

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Background: The damage that may be caused to the operating table and patients under general anaesthesia when a large earthquake occurs is unclear. We aimed to evaluate the movement and damage to operating tables and patients under general anaesthesia during an earthquake. Methods: An operating table with a manikin resembling a patient on it was placed on a shaking table, and seismic waves were input into the shaking table. The effects of seismic waves were evaluated by altering surgical positions (supine and head-down positions), operating tables, flooring material, seismic waves, and output. We observed the movement of the operating table and measured the acceleration of the operating table and manikin head. Results: Under 90% output of long-period seismic waves, the operating table with the supine manikin was overturned. Under experimental conditions that did not cause rocking, shaking such as tilting of the operating table caused stronger acceleration in the manikin's head than in the operating table. There was no clear relationship between operating table rocking and maximum acceleration as a result of programmed seismic waves. In long-period earthquakes, rocking and overturning occurred >60 s after the onset of shaking, whereas in direct earthquakes, rocking occurred within 10 s. Conclusions: An earthquake could cause strong acceleration of the patient's head under general anaesthesia, and operating tables may overturn or shake violently. Regarding patient safety, further measures to prevent overturning should be considered.

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Surveillance videos of operating rooms have potential to benefit post-operative analysis and study. However, there is currently no effective method to extract useful information from the long and massive videos. As a step towards tackling this issue, we propose a novel method to recognize and evaluate individual activities using an anomaly estimation model based on time-sequential prediction. We verified the effectiveness of our method by comparing two time-sequential features: individual bounding boxes and body key points. Experiment results using actual surgery videos show that the bounding boxes are suitable for predicting and detecting regional movements, while the anomaly scores using key points can hardly be used to detect activities. As future work, we will be proceeding with extending our activity prediction for detecting unexpected and urgent events.

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