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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 929 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.

929 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.

1,699 results
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AI gains · 929

5
HealthStableModerate evidence · 1 source

Multi-Scale Feature Fusion model combining U-Net segmentation, EfficientNet and attention autoencoder features fused via CCA and YOLO classification achieved 99.95% accuracy on apple leaf disease datasets, enabling early detection for sustainable agriculture.

Researchers described a Multi-Scale Feature Fusion system for apple leaf disease identification that segments diseased tissue with U-Net, cleans background with Rank Order Fuzzy filtering, extracts features with EfficientNet and an Attention-based Autoencoder, fuses them with Canonical Correlation Analysis, and classifies with YOLO. Tested on five apple leaf datasets, it reported 99.95% classification accuracy with cross-validation and statistical testing.

Impact 30%
69
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 19, 2026 · TRV-2026-0262

83Index score
6
HealthStableModerate evidence · 1 source

UBNet-Seg, a 2.3M-parameter U-Net variant using lung fields as geometric proxy, achieved 95.85% lung Dice at 0.05s inference and 90.31% automated cardiomegaly accuracy on NIH, rising to 93.63% on NIH and 91.21% on OpenI after expert-guided refinement.

A peer-reviewed study published July 16, 2026 describes UBNet-Seg, a lightweight 2.3-million-parameter U-Net variant that infers cardiomegaly from lung field geometry rather than explicit heart segmentation. Trained on 11,748 images, it was evaluated on external NIH and OpenI chest X-ray datasets, reporting 95.85% lung Dice and 0.05-second inference.

Impact 30%
69
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 18, 2026 · TRV-2026-0257

83Index score
8
ScienceStableHigh evidence · 5 sources

Generative AI offers faster and scaled-up foresight that can serve as anticipatory infrastructure for imagining possible futures and foresight solutions.

On 2025-10-14, a peer-reviewed article in Qualitative Research in Psychology proposed a social science futures research agenda centered on artificial intelligence. The author describes a contemporary market for futures knowledge driven by promises of faster, scaled-up AI foresight, and examines Generative AI as an anticipatory infrastructure through which international organizations imagine futures, participants engage with dominant visions, and researchers imagine future methods.

Impact 30%
49
Evidence 25%
100
Scale 20%
85
Confidence 15%
100
Recency 10%
92

Updated Aug 30, 2026 · TRV-2026-0928

81Index score

AI problems · 770

5
HealthStableModerate evidence · 1 source

In the same tertiary GDD cohort, the Youden-optimal model missed 33.9% of children who progressed to ID and had NPV 31.7%, limiting safe rule-out and indicating poor transportability beyond high-prevalence referral settings.

Researchers retrospectively analyzed 2453 children diagnosed with GDD between January 2014 and December 2023 at a provincial tertiary children's rehabilitation centre, followed to at least 60 months. Using 28 predictors across perinatal, developmental, neuroimaging, electrophysiological, genetic and comorbidity domains, they trained L2- and L1-regularized logistic regression, random forest, XGBoost and LightGBM, with Platt scaling for the L2 model, and evaluated on a held-out 30% test set.

Impact 30%
63
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0878

82Index score
6
HealthStableModerate evidence · 1 source

Even the domain-specific M4CXR model was inconsistent with reference findings in 25.2% of chest radiograph cases and did not significantly change RADPEER discrepancy rates versus original interpretation.

In a retrospective study published July 11 2026, investigators tested 500 chest radiographs from one tertiary center with two AI systems, M4CXR and ChatGPT-4o, having four radiologists score AI-generated reports for finding detection and RADPEER discrepancies. M4CXR reached 55.8% complete concordance versus 19.8% for GPT-4o and reduced mean reporting time to 16.3 seconds from 179.2 seconds unaided.

Impact 30%
63
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0312

81Index score
7
HealthStableModerate evidence · 1 source

Fully automated cardiomegaly screening accuracy dropped to 76.07% on the external OpenI dataset, showing domain-shift vulnerability, while manual CTR measurement remains a clinical bottleneck and existing deep models suffer from algorithmic bloating.

A peer-reviewed study published July 16, 2026 describes UBNet-Seg, a lightweight 2.3-million-parameter U-Net variant that infers cardiomegaly from lung field geometry rather than explicit heart segmentation. Trained on 11,748 images, it was evaluated on external NIH and OpenI chest X-ray datasets, reporting 95.85% lung Dice and 0.05-second inference.

Impact 30%
63
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 18, 2026 · TRV-2026-0257

81Index score
8
CrimeStableModerate evidence · 1 source

7.2% of evaluated MCP servers contained general vulnerabilities and 5.5% exhibited MCP-specific tool poisoning, part of eight distinct vulnerabilities largely distinct from traditional software flaws.

In a first large-scale empirical study published May 2026, researchers examined 1,899 open-source Model Context Protocol servers, the standard introduced by Anthropic in late 2024 to unify tool calling for Foundation Models. Using health metrics and a combined general and MCP-specific scanner, they measured adoption signals and code quality across the ecosystem.

Impact 30%
63
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0137

81Index score

Recomputed live from the record · Oct 11, 2026, 1:36 AM