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Mahjong, a complex game with hidden information and sparse rewards, poses significant challenges. Existing Mahjong AIs require substantial hardware resources and extensive datasets to enhance AI capabilities. The authors propose a transformer‐based Mahjong AI (Tjong) via hierarchical decision‐making. By utilising self‐attention mechanisms, Tjong effectively captures tile patterns and game dynamics, and it decouples the decision process into two distinct stages: action decision and tile decision. This design reduces decision complexity considerably. Additionally, a fan backward technique is proposed to address the sparse rewards by allocating reversed rewards for actions based on winning hands. Tjong consists of 15M parameters and is trained using approximately 0.5 M data over 7 days of supervised learning on a single server with 2 GPUs. The action decision achieved an accuracy of 94.63%, while the claim decision attained 98.55% and the discard decision reached 81.51%. In a tournament format, Tjong outperformed AIs (CNN, MLP, RNN, ResNet, VIT), achieving scores up to 230% higher than its opponents. Furthermore, after 3 days of reinforcement learning training, it ranked within the top 1% on the leaderboard on the Botzone platform. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to develop a diagnostic support tool using pretrained models for classifying panoramic images of the temporomandibular joint (TMJ) into normal and osteoarthritis (OA) cases. Subjects and Methods: A total of 858 panoramic images of the TMJ (395 normal and 463 TMJ‐OA) were obtained from 518 individuals from January 2015 to December 2018. The data were randomly divided into training, validation, and testing sets (6:2:2). We used pretrained Resnet152 and EfficientNet‐B7 as transfer learning models. The accuracy, specificity, sensitivity, area under the curve, and gradient‐weighted class activation mapping (grad‐CAM) of both trained models were evaluated. The performances of the trained models were compared to that of dentists (both TMD specialists and general dentists). Results: The classification accuracies of ResNet‐152 and EfficientNet‐B7 were 0.87 and 0.88, respectively. The trained models exhibited the highest accuracy in OA classification. In the grad‐CAM analysis, the trained models focused on specific areas in osteoarthritis images where erosion or osteophyte were observed. Conclusions: The artificial intelligence model improved the diagnostic power of TMJ‐OA when trained with two‐dimensional panoramic condyle images and can be effectively applied by dentists as a screening diagnostic tool for TMJ‐OA. [ABSTRACT FROM AUTHOR]

Context Clinical supervisors make judgements about how much to trust learners with critical activities in patient care. Such decisions mediate trainees' opportunities for learning and competency development and thus are a critical component of education. As educators apply entrustment frameworks to assessment, it is important to determine how narrative feedback reflecting entrustment may also address learners' educational needs. Methods: In this study, we used artificial intelligence (AI) and natural language processing (NLP) to identify characteristics of feedback tied to supervisors' entrustment decisions during direct observation encounters of clerkship medical students (3328 unique observations). Supervisors conducted observations of students and collaborated with them to complete an entrustment‐based assessment in which they documented narrative feedback and assigned an entrustment rating. We trained a deep neural network (DNN) to predict entrustment levels from the narrative data and developed an explainable AI protocol to uncover the latent thematic features the DNN used to make its prediction. Results: We found that entrustment levels were associated with level of detail (specific steps for performing clinical tasks), feedback type (constructive versus reinforcing) and task type (procedural versus cognitive). In justifying both high and low levels of entrustment, supervisors detailed concrete steps that trainees performed (or did not yet perform) competently. Conclusions: Framing our results in the factors previously identified as influencing entrustment, we find a focus on performance details related to trainees' clinical competency as opposed to nonspecific feedback on trainee qualities. The entrustment framework reflected in feedback appeared to guide specific goal‐setting, combined with details necessary to reach those goals. Our NLP methodology can also serve as a starting point for future work on entrustment and feedback as similar assessment datasets accumulate. Using natural language processing, Gin et al demonstrate how narrative feedback and entrustment decisions are intertwined, providing empirical evidence regarding how formative processes can help trainees achieve competencies. [ABSTRACT FROM AUTHOR]