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Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation

Aiding Large Language Models Using Clinical Scoresheets for Neurobehavioral Diagnostic Classification From Text: Algorithm Development and Validation

In the direct diagnosis approach without assessment scales, we directly input data into the conversational artificial intelligence (AI) model, which then generated classification results. This process involved providing the chatbot with the processed dataset as input data and defining its primary task as providing neurobehavioral classification results for the condition of interest.

Kaiying Lin, Abdur Rasool, Saimourya Surabhi, Cezmi Mutlu, Haopeng Zhang, Dennis P Wall, Peter Washington

JMIR AI 2025;4:e75030

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YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study

YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study

Deep learning and radiomics have achieved impressive results for the automated detection and classification of musculoskeletal fractures—including those of the ankle [20], femoral neck [21], hip [22], knee [23], spine [24], ribs [25], and scapula [26]—by using various deep convolutional neural network (DCNN) models, such as You Only Look Once (YOLO) and faster region-based convolutional neural network (R-CNN).

Jiayi Liu, Peng Sun, Yousheng Yuan, Zihan Chen, Ke Tian, Qian Gao, Xiangsheng Li, Liang Xia, Jun Zhang, Nan Xu

JMIR Med Inform 2025;13:e79064

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Diabetic Foot Ulcer Classification Models Using Artificial Intelligence and Machine Learning Techniques: Systematic Review

Diabetic Foot Ulcer Classification Models Using Artificial Intelligence and Machine Learning Techniques: Systematic Review

For that reason, classification and scoring systems were developed to create groups of patients with similar characteristics for whom similar levels of care would apply. Furthermore, they can be used to communicate wound and person-related characteristics between professionals, estimate an individual’s prognosis, help in clinical practice decision-making, and audit and comparison of populations.

Manuel Alberto Silva, Emma J Hamilton, David A Russell, Fran Game, Sheila C Wang, Sofia Baptista, Matilde Monteiro-Soares

J Med Internet Res 2025;27:e69408

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Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation

Deep Learning Radiomics Model Based on Computed Tomography Image for Predicting the Classification of Osteoporotic Vertebral Fractures: Algorithm Development and Validation

Accurate classification of OVFs is widely recognized as critical for early diagnosis, treatment planning, and prognosis evaluation [3]. Current classification systems, including the Genant semi-quantitative method [4], Heini classification [5], osteoporotic fractures classification [6], and the Assessment System of Thoracolumbar Osteoporotic Fractures (ASTLOF) [7], differ in methodology but lack global agreement [8].

Jiayi Liu, Lincen Zhang, Yousheng Yuan, Jun Tang, Yongkang Liu, Liang Xia, Jun Zhang

JMIR Med Inform 2025;13:e75665

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Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study

Prediction of Mini-Mental State Examination Scores for Cognitive Impairment and Machine Learning Analysis of Oral Health and Demographic Data Among Individuals Older Than 60 Years: Cross-Sectional Study

The main contributions of this study are as follows: The evaluation of the potential of oral health parameters for binary classification of MMSE scores of 30 and ≤26. The evaluation of the most deterministic oral health parameters that influence the outcome of the best-performing ML classifier. The assessment of RF, SVM, and Cat Boost (CB) ML classifiers as indicators of MMSE scores of 30 and ≤26.

Alper Idrisoglu, Johan Flyborg, Sarah Nauman Ghazi, Elina Mikaelsson Midlöv, Helén Dellkvist, Anna Axén, Ana Luiza Dallora

JMIR Med Inform 2025;13:e75069

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Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach

Framework for Race-Specific Prostate Cancer Detection Using Machine Learning Through Gene Expression Data: Feature Selection Optimization Approach

However, the gene expression dataset poses an additional challenge due to their high dimensionality, where the ratio of features to samples is high, hindering the performance of classification models. To address this, researchers have used feature selection methods to filter out irrelevant or redundant genes [20,21].

David Agustriawan, Adithama Mulia, Marlinda Vasty Overbeek, Vincent Kurniawan, Jheno Syechlo, Moeljono Widjaja, Muhammad Imran Ahmad

JMIR Bioinform Biotech 2025;6:e72423

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Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study

Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study

The effectiveness of this ensemble was evaluated using a range of performance metrics meticulously selected to comprehensively assess the classification performance of our model ensemble, ensuring a comprehensive understanding of its ability to distinguish between cases of with and those without CMV complications (Figure 1).

Jeong Heon Kim, A Reum Choe, Ju Ran Byeon, Yehyun Park, Eun Mi Song, Seong-Eun Kim, Eui Sun Jeong, Rena Lee, Jin Sung Kim, So Hyun Ahn, Sung Ae Jung

JMIR Med Inform 2025;13:e64987

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Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

Convolutional Neural Network Models for Visual Classification of Pressure Ulcer Stages: Cross-Sectional Study

Alex Net, a pioneering CNN in image classification, consists of 8 layers: 5 convolutional layers with varying filter numbers and 3 fully connected layers. It employs Re LU activations, max pooling, and dropout for regularization. The introduction of Re LU and dropout layers in Alex Net reduced training times and prevented overfitting, whereas its deep architecture allowed for the learning of complex features, enhancing classification accuracy [18].

Changbin Lei, Yan Jiang, Ke Xu, Shanshan Liu, Hua Cao, Cong Wang

JMIR Med Inform 2025;13:e62774

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Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study

Machine Learning Analysis of Engagement Behaviors in Older Adults With Dementia Playing Mobile Games: Exploratory Study

Reference 21: Classification of maize genotype using logistic regression Reference 25: An approach for sentiment analysis using gini index with random forest classificationclassification

Melika Torabgar, Mathieu Figeys, Shaniff Esmail, Eleni Stroulia, Adriana M Ríos Rincón

JMIR Serious Games 2025;13:e54797

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