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This study takes stock of business and economics research on Artificial Intelligence (AI) and provides a dynamic panorama of the overall knowledge structure of this ever-growing body of work ever since its inception in 1966. Our bibliometric analysis based on the full archive of 1024 studies identifies the main trends of and the major intellectual contributors to the extant knowledge of AI in business and economics research. Specifically, our results show that (1) AI-focused business and economics research wintnessed growth over three stages, particularly with a sharp increase after 2017. (2) While this body of research has gained tremendous momentum across the globe, the United States is by far the center of knowledge generation. (3) Research collaborations are still limited in this area. (4) Research topics flourished, ranging from early decision support systems, neural networks, and scheduling methods to more recent machine learning, automation, and big data. This study also identifies fruitful avenues for further business and economics research with an AI focus. [ABSTRACT FROM AUTHOR]

Trajectory-based fishing vessel behavior recognition is currently a vibrant and dynamic area of investigation within transportation and maritime research. It can provide crucial technical support for combating illegal, unreported, and unregulated (IUU) fishing. Nevertheless, existing studies typically rely on extensive datasets of pre-labeled data to train machine learning models. The acquisition of these labeled datasets is frequently a time-consuming and complicated endeavor, which presents challenges in the practical application. Hence, this paper develops a semi-supervised approach for fishing vessel behavior identification using a small amount of manually labeled trajectory samples. Besides, given that the common vessel behavioral feature parameters provide limited insight into fishing activities, this paper innovatively proposes a set of descriptive trajectory feature parameters. Based on the morphological characteristics of fishing activities and domain knowledge, the proposed trajectory features excel in discerning the intricate pattern of fishing vessel trajectories. Combined with a set of Long Short-Term Memory (LSTM)-based encoding trajectory features, a composed feature engineering scheme that leverages both human intelligence and machine intelligence is proposed. The clustering experiment result confirms that our trajectory feature scheme can successfully recognize different fishing vessel behaviors while the conventional trajectory features can't. In the consequently classification tests, the 90% accuracy achieved by the composed feature scheme exhibits a 10% increase compared to solely using deep learning-based encoding features. This novel trajectory feature engineering method together with the semi-supervised machine learning structure, not reliant on extensive labeled datasets, can be tailored and applied to mobility pattern identification across various domains. • Develop a semi-supervised approach for fishing vessel behavior identification. • Propose a set of descriptive trajectory feature parameters based on domain knowledge. • Combine both human and machine intelligence to compose the feature engineering. • The developed approach identifies fishing vessel behavior with over 90% accuracy. [ABSTRACT FROM AUTHOR]

Background and Aim: Reliable bowel preparation assessment is important in colonoscopy. However, current scoring systems are limited by laborious and time‐consuming tasks and interobserver variability. We aimed to develop an artificial intelligence (AI) model to assess bowel cleanliness and evaluate its clinical applicability. Methods: A still image‐driven AI model to assess the Boston Bowel Preparation Scale (BBPS) was developed and validated using 2361 colonoscopy images. For evaluating real‐world applicability, the model was validated using 113 10‐s colonoscopy video clips and 30 full colonoscopy videos to identify "adequate (BBPS 2–3)" or "inadequate (BBPS 0–1)" preparation. The model was tested with an external dataset of 29 colonoscopy videos. The clinical applicability of the model was evaluated using 225 consecutive colonoscopies. Inter‐rater variability was analyzed between the AI model and endoscopists. Results: The AI model achieved an accuracy of 94.0% and an area under the receiver operating characteristic curve of 0.939 with the still images. Model testing with an external dataset showed an accuracy of 95.3%, an area under the receiver operating characteristic curve of 0.976, and a sensitivity of 100% for the detection of inadequate preparations. The clinical applicability study showed an overall agreement rate of 85.3% between endoscopists and the AI model, with Fleiss' kappa of 0.686. The agreement rate was lower for the right colon compared with the transverse and left colon, with Fleiss' kappa of 0.563, 0.575, and 0.789, respectively. Conclusions: The AI model demonstrated accurate bowel preparation assessment and substantial agreement with endoscopists. Further refinement of the AI model is warranted for effective monitoring of qualified colonoscopy in large‐scale screening programs. [ABSTRACT FROM AUTHOR]

Objective: Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters. Materials and methods: A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model. Results: 683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05–329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707–0.971), 0.873 (0–0.997), and 0.894 (0.025–0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001). Conclusion: Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models. [ABSTRACT FROM AUTHOR]