AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times

Advancing industrial inspection with an automated computer vision solution for orthopedic surgical tray inspection

This work aimed to automate the current manual inspection process of orthopedic joint reconstruction loaner trays, which contain multiple components securely placed in designated slots, by developing an end-to-end pipeline, including an object detection model followed by a tray layout verification algorithm. We collected video training data from 74 trays comprising 1039 unique components. We trained a single You Only Look Once (YOLOv7) model capable of detecting 1039 classes, where we customized the loss and non-max suppression (NMS) functions. For layout verification, we implemented an in-house algorithm based on a pretrained Local Feature Matching with Transformer (LoFTR) model to verify each component's presence and correct placement within a tray. We evaluated our end-to-end pipeline on 139 testing inspection images and achieved an overall mean average precision (mAP@0.5) and false-positive rate of 0.94 ± 0.10 and 0.05 ± 0.08, respectively, across 12 test scenarios. Our enhanced YOLOv7-X architecture outperformed the Region-based Convolutional Neural Network (Faster-RCNN-ResNet101) baseline model by 24%. Remarkably, in 7 out of 12 scenarios, we achieved an mAP exceeding 0.99. Our proposed solution substantially reduces the inspection time by 47.3% and is highly scalable, allowing straightforward integration into broad industrial applications.

Evaluating GenAI systems to combat mental health issues in healthcare workers: An integrative literature review

BACKGROUND

Mental health issues among healthcare workers remain a serious problem globally. Recent surveys continue to report high levels of depression, anxiety, burnout and other conditions amongst various occupational groups. Novel approaches are needed to support clinician well-being.

OBJECTIVE

This integrative literature review aims to explore the current state of research examining the use of generative artificial intelligence (GenAI) and machine learning (ML) systems to predict mental health issues and identify associated risk factors amongst healthcare professionals.

METHODS

A literature search of databases was conducted in Medline then adapted as necessary to Scopus, Web of Science, Google Scholar, PubMed and CINAHL with Full Text. Eleven studies met the inclusion criteria for the review.

RESULTS

Nine studies employed various machine learning techniques to predict different mental health outcomes among healthcare workers. Models showed good predictive performance, with AUCs ranging from 0.82 to 0.904 for outcomes such as depression, anxiety and safety perceptions. Key risk factors identified included fatigue, stress, burnout, workload, sleep issues and lack of support. Two studies explored the potential of sensor-based technologies and GenAI analysis of physiological data. None of the included studies focused on the use of GenAI systems specifically for providing mental health support to healthcare workers.

CONCLUSION

Preliminary research demonstrates that AI/ML models can effectively predict mental health issues. However, more work is needed to evaluate the real-world integration and impact of these tools, including GenAI systems, in identifying clinician distress and supporting well-being over time. Further research should aim to explore how GenAI may be developed and applied to provide mental health support for healthcare workers.

The Effect of Job Skill Demands Under Artificial Intelligence Embeddedness on Employees' Job Performance: A Moderated Double-Edged Sword Model

With the widespread application of AI technology, the skills and abilities required by employees in their work are undergoing fundamental changes, redefining the roles of employees. This research aims to explore the effect of job skill demands under AI embeddedness on well-being in organizations and job performance. Based on conservation of resources theory, this research randomly selected 479 employees from 8 companies in China using a time-lag method as samples, and conducted statistical analysis with ordinary least squares (OLS). This research found that, job skill demands under AI embeddedness will both increase employees' competency needs, promoting their well-being in organizations and job performance and decrease employees' job embeddedness, inhibiting their well-being in organizations and job performance. Meanwhile, technological anxiety moderated the impact of job skill demands under AI embeddedness on job embeddedness.

Prediction and optimization of employee turnover intentions in enterprises based on unbalanced data

The sudden resignation of core employees often brings losses to companies in various aspects. Traditional employee turnover theory cannot analyze the unbalanced data of employees comprehensively, which leads the company to make wrong decisions. In the face the classification of unbalanced data, the traditional Support Vector Machine (SVM) suffers from insufficient decision plane offset and unbalanced support vector distribution, for which the Synthetic Minority Oversampling Technique (SMOTE) is introduced to improve the balance of generated data. Further, the Fuzzy C-mean (FCM) clustering is improved and combined with the SMOTE (IFCM-SMOTE-SVM) to new synthesized samples with higher accuracy, solving the drawback that the separation data synthesized by SMOTE is too random and easy to generate noisy data. The kernel function is combined with IFCM-SMOTE-SVM and transformed to a high-dimensional space for clustering sampling and classification, and the kernel space-based classification algorithm (KS-IFCM-SMOTE-SVM) is proposed, which improves the effectiveness of the generated data on SVM classification results. Finally, the generalization ability of KS-IFCM-SMOTE-SVM for different types of enterprise data is experimentally demonstrated, and it is verified that the proposed algorithm has stable and accurate performance. This study introduces the SMOTE and FCM clustering, and improves the SVM by combining the data transformation in the kernel space to achieve accurate classification of unbalanced data of employees, which helps enterprises to predict whether employees have the tendency to leave in advance.