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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 sourced gains and 771 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

935 gains
771 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

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

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AI gains · 935

313
HealthNewly addedModerate evidence · 1 source

Fully automated AI-based lesion segmentation achieved excellent overlap and high quantitative agreement with expert manual delineation for lymphoma on whole-body FDG PET/CT.

By September 2026, researchers retrospectively evaluated a fully automated AI-based lesion segmentation tool using a hybrid 2D-3D nnUNet with ResNet18 backbone on whole-body FDG PET/CT scans from 72 lymphoma patients. Compared with manual delineations by experienced physicians, the model segmented 711 lesions versus 848 manual lesions and achieved DSC 89.2%, sensitivity 82.9, PPV 96.5, with strong correlations for PET metrics.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1222

68Index score
314
HealthNewly addedModerate evidence · 1 source

An interpretable machine learning nomogram using eGFR, anhepatic phase time, postoperative log D-dimer and ALT available within 1 hour after liver transplantation predicted AKI with AUC 0.861 in training and 0.761 in validation, with SVM reaching 0.846, to enable early identification for intensified renal monitoring.

Researchers retrospectively developed an early postoperative interpretable nomogram to predict acute kidney injury after liver transplantation using four variables available within about one hour of surgery. In 127 patients split into training and validation cohorts, the model achieved AUCs of 0.861 and 0.761, with a support vector machine variant reaching 0.846 on validation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1221

68Index score
315
HealthNewly addedModerate evidence · 1 source

Interpretable ML models classified prevalent CVD among middle-aged and elderly CKD patients with test AUCs of 0.753 in the US NHANES cohort using XGBoost and 0.769 in the China CHARLS cohort using logistic regression.

Researchers developed interpretable machine learning models to classify cardiovascular disease among middle-aged and elderly CKD patients using US NHANES (n=1057) and China CHARLS (n=1109) cohorts. Seven algorithms were trained and tested internally, with XGBoost achieving test AUC 0.753 in NHANES and logistic regression achieving 0.769 in CHARLS, followed by bidirectional cross-population external validation and SHAP analysis.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1220

68Index score
316
HealthNewly addedModerate evidence · 1 source

Deep learning models, especially U-Net variants, achieved Dice scores up to 0.93 for automated segmentation of cervical tumors and related structures on MRI, suggesting improved efficiency and reproducibility over manual segmentation.

This PRISMA-guided systematic review of 30 studies up to December 2025 evaluated machine learning and deep learning algorithms for automated cervical cancer segmentation on MRI, a task central to staging and treatment planning that is currently manual and variable.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1219

68Index score

AI problems · 771

313
LifestyleStableModerate evidence · 1 source

Perceived risk around AIGC reduced fashion designers' feelings of autonomy, competence and relatedness, undermining psychological conditions for adoption.

Researchers examined why fashion designers adopt Artificial Intelligence Generated Content, which is described as increasingly used in creative design. Using the Stimulus-Organism-Response framework combined with Self-Determination Theory, they surveyed 318 Chinese fashion-design practitioners and analyzed 21 items with PLS-SEM to link perceived risk, social influence and facilitating conditions to autonomy, competence, relatedness and behavioral intention.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 18, 2026 · TRV-2026-0818

67Index score
314
HealthStableModerate evidence · 1 source

AI systems applied to healthcare decision-making, medical diagnosis, and other domains can lead to unfair outcomes that perpetuate existing inequalities and reinforce harmful stereotypes, including generative biases in synthetic media.

A peer-reviewed survey published December 26, 2023 reviewed literature on fairness and bias in AI, focusing on sources such as data, algorithm, and human decision biases and the emerging issue of generative AI bias in synthetic media across healthcare, employment, criminal justice, and credit scoring.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0813

67Index score
315
ClimateStableModerate evidence · 1 source

Rapid growth of deep learning workloads increases energy consumption and carbon emissions, while current carbon tracking and static scheduling fail to deliver carbon awareness in real ML operations.

On 2026-08-16, a peer-reviewed paper in Discover Artificial Intelligence described rising energy use and emissions from growing deep learning workloads in contemporary data centres and presented EcoSchedAI, a carbon-aware job scheduling framework intended to bring carbon awareness into actual machine learning operational processes.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0812

67Index score

Recomputed live from the record · Oct 12, 2026, 4:45 PM