Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
Researchers prospectively collected 3676 smartphone images from 1016 atopic dermatitis patients at 16 Japanese institutions and trained a ConvNeXt model to score four EASI signs against dermatologist ratings. The model achieved AUCs of 0.825 to 0.860 for scores >=2 and over 0.900 for score 3, with erythema most accurately detected.
Investigators developed and externally validated a machine learning model to stratify risk of postoperative hydrocephalus after posterior fossa tumor resection using data from 1,073 patients treated at five tertiary centers from 2013 to 2024. After screening 30 variables, they built a three-variable preoperative model using Evans index, tumor-fourth ventricle relationship, and preoperative CSF diversion status, with SVM showing AUC 0.877 in the external cohort.
Researchers developed a TRIPOD+AI-compliant, survey-weighted MICE-EBM framework to predict severe tooth loss defined as six or more missing teeth using US representative data. The model was derived on BRFSS 2022 with 433,772 adults, temporally validated on BRFSS 2024 with 448,213 adults, and tested for cross-survey generalizability on NHANES 2015-2018 with 10,775 adults.
Researchers developed and validated a two-stage convolutional neural network pipeline to detect dental implants on panoramic radiographs and classify them by brand and prosthetic platform size. Using 387 radiographs with 1004 implants, the Faster R-CNN with EfficientNet-B7 backbone achieved 99.45% detection accuracy and 85.60% combined brand-and-platform accuracy across 25 partitions.
By November 2024, researchers tested prompt engineering as a creative skill for AI art in three studies with crowdsourced participants, asking them to judge prompt quality, write prompts, and refine them.
Published November 30, 2024, this peer-reviewed article reviews how combining genomics, transcriptomics, proteomics and metabolomics with machine learning and high-throughput sequencing is being used to tailor therapies to individual genetic and molecular profiles.
A systematic literature review published November 28, 2024 examined how artificial intelligence is transforming organizational landscapes. It found AI reshapes work practices through automation and changes to decision making and employee roles, while driving cultural shifts toward innovation, agility, and continuous learning.
By July 2026, a narrative review of 40 studies from 2020-2025 found deep learning, led by U-Net variants with residual and attention mechanisms and standardized pipelines like nnU-Net, increasingly achieved high Dice scores on MRI DWI/ADC, with many reports above 0.80 and recent transformer and ensemble multisite models approaching 0.90, while CT performance was lower and more variable.
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