The development of wearable medical devices has progressed rapidly in recent years, especially in the domain of vital sign monitoring using optical and electrical sensors. However, the use of ultrasound—long regarded as a powerful tool for real-time internal imaging—has remained limited to stationary, hand-held applications. In this paper, we introduce a novel ultrasound array probe that is thin, flexible, and lightweight, enabling continuous and hands-free monitoring of internal body structures. By combining sol-gel spray deposition of piezoelectric composites with novel integration techniques, the proposed probe can be applied to curved and mobile regions of the body. We also discuss potential clinical applications. This technology holds great promise for democratizing ultrasound imaging beyond hospitals, bringing diagnostic capabilities to new environments and expanding the frontier of human-centered sensing.

Career development is no longer a linear accumulation of achievements but a dynamic process shaped by multiple transitions across academia, industry, and international settings. This review reflects on insights gained through systematic career training at Harvard Medical School and their application to the Japanese context, where structured programs for training future leaders remain limited. Key practices emphasized include the strategic use of CV updates to visualize and plan skill acquisition, and time management based on “The 7 Habits of Highly Effective People,” particularly prioritizing important but non-urgent tasks. These frameworks enable researchers and professionals to design sustainable career trajectories, avoiding the trap of being consumed by short-term busyness. Furthermore, the review highlights the responsibility of senior leaders―including professors, PIs, and mentors in both academia and industry―to create opportunities that are officially recorded and contribute to the CVs of younger generations. Such tangible experiences, ranging from conference presentations to committee work, serve as milestones that strengthen career resilience. Ultimately, career transitions should not be feared but embraced as opportunities for growth and renewal. By embedding these principles into daily practice, individuals and organizations alike can foster a culture of proactive career design and adaptive leadership.

There have been many reports on gaze movements when interpretating mammograms using eye tracking devices. If we could build an automatic gaze movement pattern recognition system that warn radiologists when their gaze movements do not match their interpretation of the image, we believe that this would be able to assist radiologists. Therefore, as an initial step in this study, we aimed to verify whether it is possible to correctly learn to heat map videos indicating gaze movement and interpretation of the image using R(2+1)D, Two stream I3D, and SlowFast. We obtained heat map videos for two mammography export radiologists and 19 mammography technologists on 8 abnormal and normal MLO and CC mammograms. The accuracy on heat map videos on MLO and CC were calculated using a 5-fold cross validation method. Among the three deep learning models for video classification, the highest accuracy was 0.69±0.03 for frame: 32 SlowFast. The sensitivity of the MLO video for TP, TN, and FP+FN was 70.0%, 72.2%, and 59.3%, respectively. The sensitivity of the CC video was 70.6%, 84.2%, and 54.4%, respectively. In the future, it is necessary to increase the number of eye movement images to improve the accuracy.

Cytology imposes a significant burden on practitioners, and image classification techniques have been developed to alleviate this burden. One approach to improving the performance of image classification models is data augmentation using generative AI. However, conventional generative techniques often introduce model-specific artifacts, making it difficult to generate realistic images. In this study, we propose a novel conditional diffusion model to generate high-quality benign and malignant lung cytology images for use in image classification models. We conducted a subjective evaluation by cytology experts and a quantitative evaluation based on image similarity, comparing the images generated by our proposed method with those generated by conventional techniques. The results demonstrated that our proposed method outperformed conventional approaches. In conclusion, the proposed method may be effective in generating realistic cytology images.