TruaceTracing the truth around AITuesday, September 15, 2026
The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,419 results
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AI gains · 788

74
GainHealth· Newly added· Evidence: Moderate (1 source)

YOLOv11 Nano achieved multiclass detection and Gartland I-III classification of pediatric supracondylar fractures with ~91-93% accuracy across validation strategies, improving further with bone segmentation.

Researchers developed a YOLOv11 Nano model to detect pediatric supracondylar fractures and classify Gartland subtypes I-III on 1082 elbow radiographs from 2004-2018, testing three patient-level validation schemes and adding bone segmentation and explainable AI visualizations. They also conducted a PRISMA-DTA meta-analysis of four studies totaling 2232 images comparing CNN and radiomics approaches.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Sep 7, 2026 · TRV-2026-1008

74
GainEducation· Newly added· Evidence: Moderate (1 source)

An 8-hour hands-on AI rotation delivered to 27 radiology residents over three years was completed by all participants with consistent structure, yielding approximate 80-90% post-training quiz performance and favorable ratings for overall value.

Between 2023 and 2025, educators at a single academic institution integrated an 8-hour interactive AI rotation into diagnostic radiology residency, combining didactics with lab modules on convolution, radiomics, machine learning, deep learning, evaluation, bias, and clinical cases for 27 residents across three cohorts.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Sep 7, 2026 · TRV-2026-1007

74
GainHealth· Newly added· Evidence: Moderate (1 source)

Mistral-NeMo verified orthopaedic lower-extremity billing by correctly identifying 90% of true CPT codes and rejecting 99.8% of incorrect codes when provided with billing descriptions.

A peer-reviewed study tested the Mistral-NeMo language model on 1000 operative notes from 177 providers to verify Current Procedural Terminology codes for lower-extremity orthopaedic surgery. When CPT billing descriptions were included in the prompt, the model correctly identified 90% of true codes and rejected 99.80% of incorrect codes.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Sep 7, 2026 · TRV-2026-1003

74
GainHealth· Newly added· Evidence: Moderate (1 source)

Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients: Based on the above evaluation indicators, the BPNN model had the best performance, with an F1 score of 0.95, an accuracy rate of 94.87%, and an AUC of 0.9738.

Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (pITP) from connective tissue disease-related thrombocytopenia (CTD-TP) in children presenting with thrombocytopenia. Method A retrospective study was conducted on 387 newly diagnosed children with thrombocytopenia.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Sep 4, 2026 · TRV-2026-0975

AI problems · 631

72
ProblemHealth· Stable· Evidence: Moderate (1 source)

Deployment of AI in healthcare raises ethical challenges and risks related to data privacy and algorithmic bias that must be mitigated.

This peer-reviewed review from March 2024 surveys how artificial intelligence is being integrated across hospitals and clinics, covering clinical decision support, operational management, medical image analysis, and patient monitoring with AI-powered wearables, drawing on case studies of domain-specific transformation.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0454

72
ProblemLabor· Stable· Evidence: Moderate (1 source)

Low-skilled workers are subjected to stronger technological control, and large language models disproportionately influence women, younger demographics, professional skilled laborers, and higher-income groups in the tertiary industry.

A peer-reviewed study published November 17, 2025 analyzed skill heterogeneity as technology moves from physical automation to cognitive automation. It assessed both substitution and control, finding limited substitution for high- and low-skilled workers, but stronger control for low-skilled workers, and compared sectoral effects of automation versus large language models.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0451

72
ProblemLabor· Stable· Evidence: Moderate (1 source)

Industrial AI deployment creates ongoing ethical threats linked to entrusting machines with autonomy and decision-making responsibility.

Published December 2025 in Production Engineering Archives, this peer-reviewed theoretical paper reviews how artificial intelligence is being used in industry, including cobots, algorithmic management, employee monitoring, sustainability efforts, and generative AI, and summarizes existing international legal frameworks for safe and ethical AI.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0411

72
ProblemMedia & Arts· Stable· Evidence: Moderate (1 source)

Generative AI may amplify existing dysfunctions of streaming platforms, threaten livelihoods of music professionals, and raise governance and transparency concerns.

Published December 5, 2025, this peer-reviewed study investigated how AI and Generative AI affect music streaming. Using two focus groups with users and with artists/performers, it explored perceptions of AI-generated music for listening and for artists' position and opportunities within the streaming model.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0410

Recomputed live from the record · Sep 15, 2026, 3:12 PM