Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.
This peer-reviewed review published July 24, 2026 synthesized recent progress on machine learning models that integrate multimodal big data such as electronic health records, genomic and proteomic data to guide transfusion support for acute myeloid leukaemia, a highly heterogeneous malignancy where transfusion is essential.
Using a national trauma databank of over 1.2 million ambulance-transported adults from 2017-2020, researchers built an XGBoost model from routinely collected prehospital vitals, demographics, and field triage criteria to predict serious injury and compared it to a database-derived surrogate of the 2021 National Field Triage Guidelines.
Researchers developed Retina4IRD, an AI-based clinician decision support system that predicts genotype categories from fundus photographs and OCT scans, and tested it in internal and external validation and in a 300-participant randomized controlled trial comparing AI-assisted specialists to specialists alone for suspected inherited retinal diseases.
Researchers developed and validated Random Forest and Gradient Boosting models to predict Hoehn and Yahr scores 5 years after 123I-ioflupane SPECT imaging, using harmonized data from 343 real-world patients and 134 PPMI patients with 83 overlapping features. Models using 2 years of clinical follow-up achieved the highest accuracy, driven by early H&Y scores, gait severity, and select imaging features.
A 2026 narrative review and conceptual analysis in Diagnosis synthesized literature on noise in medical decision-making, AI applications in healthcare, and clinical reasoning, reviewing case studies in radiology and pathology and empirical data on AI performance.
On March 14, 2017, authors in PNAS described a method to overcome catastrophic forgetting in deep neural networks. They noted that while deep networks were the most successful technique for translation, image classification and generation, they could not learn multiple tasks sequentially. Their solution protects weights important for previous tasks, inspired by synaptic consolidation.
Published 12 February 2026 in Societies, this peer-reviewed article analyzes how algorithmic systems in employment screening, welfare administration, and digital platforms function as social and institutional actors. Using regulatory materials, platform governance documents, technical disclosures, and composite vignettes synthesized from public evidence, it examines how automated classification and delegated authority reshape how individuals are evaluated and legitimised.
Researchers studied whether legally mandated disclosure labels help users avoid deception from AI-generated images. After five focus groups, they surveyed 1,354 participants on how labels changed their judgments of true and false claims illustrated with different image types.
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