Truace
Subscribe
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
Show filters and sorting

Download every matching row, not just this page:Export CSVExport JSON

AI gains · 932

77
HealthStableHigh evidence · 3 sources

In routine practice between May 2023 and May 2025, Brainomix e-CTA achieved 84% sensitivity and 95% specificity for LVO detection and reduced time to diagnostic conclusion for readers of different experience levels, with 94% sensitivity for ICA/proximal M1 occlusions.

Between May 2023 and May 2025, researchers retrospectively evaluated 531 multiphase CTA examinations from consecutive patients with suspected acute ischemic stroke at a single center, comparing Brainomix e-CTA automated LVO detection to expert neuroradiologist interpretation.

Impact 30%
69
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0307

76Index score
78
HealthStableModerate evidence · 1 source

EAGLE selectively analyzes informative regions of whole-slide pathology images, improving classification accuracy by up to 23% and reducing per-slide processing to 2.27 seconds.

On 2026-07-01, Nature Communications published a peer-reviewed study introducing EAGLE, a deep learning framework for digital pathology. The system was tested across 43 tasks from nine cancer types and was reported to outperform patch aggregation methods by up to 23% while processing one slide in 2.27 seconds.

Impact 30%
63
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0252

76Index score
79
HealthStableHigh evidence · 5 sources

Smartphone apps, virtual reality, and generative AI including large language models show utility across well-being and clinical conditions and can positively impact mental health care when deployed as tools to augment and extend care.

As of May 2025, this review in World Psychiatry examined how smartphone apps, virtual reality, and generative AI including large language models are being applied to mental health, evaluating evidence across well-being, depression, anxiety, schizophrenia, eating disorders and substance use, and outlining advances in digital phenotyping and generative outputs.

Impact 30%
49
Evidence 25%
100
Scale 20%
60
Confidence 15%
100
Recency 10%
85

Updated Jul 24, 2026 · TRV-2026-0523

75Index score
80
EducationStableHigh evidence · 5 sources

Sustainable AI-Metaverse adoption in universities substantially fosters digital pedagogical innovation and enhanced perceived student learning outcomes

On March 4, 2026, a peer-reviewed study in Frontiers in Artificial Intelligence reported results from 280 university students on an ESG-informed framework for Sustainable AI-Metaverse Adoption. Using SEM-PLS, the authors found environmental and social factors were stronger predictors of adoption than governance factors, and that sustainable adoption was linked to higher digital pedagogical innovation and enhanced student learning outcomes.

Impact 30%
49
Evidence 25%
100
Scale 20%
60
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0343

75Index score

AI problems · 770

77
EducationNewly addedModerate evidence · 1 source

Communication-related university curricula emphasize conceptual-critical AI ethics, power and governance, while employer ads prioritize operational SEO, multichannel analytics and campaign performance, leaving environmental and social externalities of AI absent from hiring discourse.

Between July 2024 and June 2025, researchers compared 66 course descriptions from six leading UK universities with 107 graduate-to-mid-level ads in communications, digital media, advertising and public relations. Using an AI-keyword index, TF-IDF and LDA topic modeling, they found curricula devoted more vocabulary to AI, datafication and platform governance than job ads, but with a different focus.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
97

Updated Sep 27, 2026 · TRV-2026-1203

73Index score
78
HealthStableModerate evidence · 1 source

When applied to a regional hospital 150 km outside Melbourne, the same AI system's ability to distinguish self-harm cases declined to PR AUC 0.78, with instability linked to linguistic domain shift and different self-harm presentations.

Researchers validated a previously developed AI system that detects self-harm in emergency department triage notes using extensive text normalisation and 1931 features. They tested it prospectively on 329,655 notes from the original major metropolitan hospital in Melbourne and externally on 316,877 notes from a regional hospital 150 km away covering 2012-2021.

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

Updated Sep 13, 2026 · TRV-2026-1072

73Index score
79
HealthStableModerate evidence · 1 source

There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models.

There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models. However, this literature has focused on acute-care hospitals, rather than post-acute facilities, where the risk of deterioration remains high.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0982

73Index score
80
CrimeStableModerate evidence · 1 source

Despite increased modal yields for rice and wheat, regional yield gaps continue to widen and the share of high-vulnerability districts has risen, particularly in resource-stressed regions such as the Indo-Gangetic Plains, driven by socioeconomic inequality and climate variability.

Researchers developed a Yield Gap Vulnerability framework for India that integrates agricultural, hydrological, meteorological and socioeconomic indicators with observed yield gaps for rice, wheat, maize and millet at district level, using an integrated Machine Learning approach with XGBoost, Random Forest and Artificial Neural Network models.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0939

73Index score

Recomputed live from the record · Oct 11, 2026, 8:56 AM