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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 932 sourced gains and 770 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.

932 gains
770 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 · 932

117
CrimeStableModerate evidence · 1 source

Supervised logistic regression distinguished dominant versus non-dominant handwriting with 92.98% test accuracy using execution-quality features, providing quantitative benchmarks to support forensic evaluation of suspected off-hand disguise.

Researchers collected paired samples from 94 right-handed participants who each wrote the same standardized text with dominant and non-dominant hands, then scored 13 general and 19 individual characteristics. They found statistically significant differences in 61.5% of general and 57.9% of individual characteristics and trained a supervised logistic regression model to distinguish hand use.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 26, 2026 · TRV-2026-0889

74Index score
118
HealthStableModerate evidence · 1 source

An ITO/CuBi2O4/LaNiO3 heterojunction sensor with machine learning analysis identified 9 biocide types and 5 ethanol concentration gradients with 98.7% overall accuracy.

Researchers built an optical sensor based on an ITO/CuBi2O4/LaNiO3 heterojunction with a hydrophobic surface that turns the curvature changes of moving biocide droplets into photocurrent signals. Machine learning was used to extract and analyze feature peaks from those signals to classify liquids.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0876

74Index score
119
HealthStableModerate evidence · 1 source

Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. Results Intraclass correlation between Fick- and DLM-derived Qp:Qs was 0.782 (P 1.5: 90% specificity, 98% sensitivity, area under the receiver operating characteristic curve [AUROC] 98%; predicting Qp:Qs Conclusion Deep learning-based analysis of CXRs enables accurate evaluation of pulmonary vascularity and outperforms structured assessment by clinicians.

Background Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 22, 2026 · TRV-2026-0848

74Index score
120
HealthNewly addedModerate evidence · 1 source

Systematic review of 11 studies (36,863 patients) found ultrasound-based ML models achieved high pooled sensitivity and specificity with external validation, and AI assistance improved junior readers' specificity and cut examination time by 61% without loss of accuracy.

This PROSPERO-registered systematic review and meta-analysis evaluated 11 studies comprising 37 model variants and 36,863 patients assessing machine learning for pediatric intussusception. Internally validated ultrasound models achieved pooled sensitivity 0.914 and specificity 0.980, with externally validated models showing sensitivity 0.946 and specificity 0.958, while abdominal radiograph triage models performed lower externally.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
99

Updated Oct 6, 2026 · TRV-2026-1297

73Index score

AI problems · 770

117
BusinessStableModerate evidence · 1 source

Those TFP gains are likely exaggerated and even more modest, predicted to be less than 0.53% over 10 years, because future AI effects will involve hard-to-learn tasks with many context-dependent factors and no objective outcome measures.

This peer-reviewed paper models AI's macroeconomic impact as task-level automation and complementarity, using Hulten's theorem to translate the fraction of tasks impacted and average cost savings into GDP and TFP effects. Using existing exposure estimates, it calculates no more than a 0.66% TFP increase over 10 years, then revises down to less than 0.53% after accounting for the shift from easy-to-learn to hard-to-learn tasks.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0378

71Index score
118
HealthStableModerate evidence · 1 source

Among the same respondents, 70.8% cited technical reliability and 68.2% cited data privacy as top concerns about AI application for cervical screening, while 41.2% reported anxiety during result waiting and 58.1% struggled with medical terminology.

On July 10 2026, a peer-reviewed cross-sectional study reported results from 308 online questionnaire responses about cervical HPV screening experiences. Most respondents were urban women aged 25-35, 76.30% reported a history of HPV infection, and 91.56% had undergone TCT. The study measured current distress points and attitudes toward AI-assisted diagnosis.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0329

71Index score
119
HealthStableModerate evidence · 1 source

First-year medical students changed answers to match ChatGPT in 22.3% of cases, with greater reliance on foundational than clinical questions, indicating context-dependent overreliance risk.

In a July 2026 peer-reviewed study, 57 first-year medical students completed 24 paired clinical and foundational questions during a pediatric nephrology and urology case-based session, answering individually, then viewing a ChatGPT-generated answer that was deliberately correct or incorrect, and re-answering.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 17, 2026 · TRV-2026-0235

71Index score
120
HealthStableModerate evidence · 1 source

In a 960-response vignette test, ChatGPT Health undertriaged 52% of gold-standard emergencies, directing diabetic ketoacidosis and impending respiratory failure to 24-48 h evaluation instead of the emergency department, with failures concentrated at clinical extremes and triage shifting toward less urgent care when by-

In a structured stress test published February 23, 2026, researchers evaluated ChatGPT Health, OpenAI's consumer health tool launched in January 2026, using 60 clinician-authored vignettes across 21 clinical domains under 16 factorial conditions to generate 960 responses, assessing triage recommendations and contextual sensitivity.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0181

71Index score

Recomputed live from the record · Oct 11, 2026, 1:40 PM