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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.

1,702 results
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AI gains · 932

61
CrimeStableModerate evidence · 1 source

Hybrid and ensemble neural network frameworks that combine temporal, relational, and anomaly-detection capabilities consistently achieve superior real-time fraud detection performance in credit card networks and instant payment systems.

As of its March 5 2026 publication, this narrative literature review surveyed how neural network architectures are used for real-time financial fraud detection, covering MLPs, LSTMs, CNNs, Autoencoders, GNNs and Transformers, and the production requirement to operate within sub-100-millisecond payment authorization pipelines, with examples from credit card networks and Brazil's PIX system.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0339

77Index score
62
HealthStableModerate evidence · 1 source

M4CXR achieved higher report consistency than ChatGPT-4o and cut reporting time from 179.2 seconds unaided to 16.3 seconds assisted when interpreting chest radiographs.

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%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0312

77Index score
63
ClimateStableModerate evidence · 1 source

AI and ML are being applied to improve energy efficiency and support climate mitigation through energy optimization, renewable integration, and carbon reduction.

By late 2024, a review of 237 publications from 2010 to 2024 found AI and ML increasingly studied as tools for energy efficiency and climate mitigation, with over 60% of papers appearing in the last two years and focus areas including sustainable construction and climate forecasting.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 19, 2026 · TRV-2026-0279

77Index score
64
HealthStableModerate evidence · 1 source

AI models predicted incident hypertension and cardiovascular risk from EHRs, wearables and multimodal data with AUCs around 0.75 to 0.90, supporting personalized therapy and remote monitoring.

A structured narrative review of literature from January 2015 to December 2025 examined AI for hypertension screening, diagnosis, risk stratification, treatment optimization and remote monitoring. It found ML models often outperformed conventional risk scores with AUCs of 0.75 to 0.90 and showed promise for personalized therapy and continuous monitoring.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 19, 2026 · TRV-2026-0269

77Index score

AI problems · 770

62
HealthNewly addedModerate evidence · 1 source

The AI model missed 37 scans initially classified as urgent, yielding 85.4% sensitivity, with 13 remaining small or subtle urgent findings after post hoc re-adjudication.

Researchers retrospectively tested an AI model that classifies non-contrast CT brain scans as urgent or non-urgent on 3424 consecutive adult scans from a quaternary emergency department in 2024. Against consultant radiologist reports as reference, the model achieved 85.4% sensitivity, 69.1% specificity and 98.3% NPV, with 64% of encounters identified as true negatives potentially eligible for expedited disposition.

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

Updated Oct 1, 2026 · TRV-2026-1238

74Index score
63
HealthNewly addedModerate evidence · 1 source

Automated tool detected fewer lesions than manual expert delineation (711 vs 848) with sensitivity of 82.9%, indicating missed lesions in lymphoma 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%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1222

74Index score
64
HealthStableModerate evidence · 1 source

From Answers to Agents: What Must Change Before Generative and Agentic AI Become Clinical Infrastructure in Hematology: In 2026, a hematology agent achieved roughly 83% concordance with tumor-board decisions in a prospective silent trial, with hallucinations in 0.3%, suggesting that for well-structured tasks the binding constraint is shifting from accuracy to governability.

Generative artificial intelligence in hematology is entering a new phase. The dominant question, whether large language models are accurate enough for clinical decision support, is being overtaken by a harder one, as systems shift from answering questions to acting: extracting structured cases, routing them, classifying variants, and grounding recommendations in guidelines and case memory.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1168

74Index score

Recomputed live from the record · Oct 11, 2026, 7:05 AM