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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

45
HealthStableModerate evidence · 1 source

AI research is moving ADHD diagnosis away from subjective interviews toward objective, data-driven tools, with EEG emerging as preferred modality for recent models.

A bibliometric review of 722 Scopus-indexed papers from 2011 to 2024 tracked how artificial intelligence has been applied to ADHD prediction. Using Python and VOSviewer, the authors found exponential growth peaking in 2023, a concentration of output in the United States and China, and a technological shift from support vector machines to deep learning with EEG becoming the favored data modality.

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

Updated Aug 3, 2026 · TRV-2026-0630

77Index score
46
Media & ArtsStableModerate evidence · 1 source

Adoption of text-to-image generative AI increased human creative productivity and the likelihood of peer favorites per view among artists in a large online art community.

By February 2024, researchers analyzing over 4 million artworks from more than 50,000 users found that text-to-image generative AI adoption was linked to a 25% rise in creative productivity and a 50% rise in favorites per view, alongside shifts in novelty metrics.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
86

Updated Jul 31, 2026 · TRV-2026-0604

77Index score
47
HealthStableModerate evidence · 1 source

Structural modeling tools such as AlphaFold and RoseTTAFold enable high-accuracy 3D prediction of proteins encoded by multidrug-resistant bacterial genomes, facilitating functional annotation and accelerating design of new antimicrobial compounds.

By July 30 2026, a narrative review in Journal of Computer-Aided Molecular Design described the use of structural modeling and artificial intelligence for functional prediction of proteins encoded by multidrug-resistant bacterial genomes. It reported that tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating annotation of hypothetical proteins and identification of conserved domains and catalytic sites.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
86

Updated Jul 31, 2026 · TRV-2026-0599

77Index score
48
HealthStableModerate evidence · 1 source

AI with multi-omics and systems-level frameworks was used to support target identification, microbial network reconstruction, biomarker discovery, and therapeutic prioritization for anti-virulence strategies against Salmonella Typhi.

This critical review from July 30 2026 examines quorum-sensing, particularly LuxS-mediated AI-2 signaling, in Salmonella Typhi as a regulator of virulence, biofilm formation, and persistence amid rising multidrug-resistant and extensively drug-resistant typhoid. It surveys microbiome interactions and a range of anti-QS strategies and evaluates the role of AI, multi-omics integration, and systems-level frameworks in target identification and therapeutic prioritization.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
86

Updated Jul 31, 2026 · TRV-2026-0598

77Index score

AI problems · 770

45
BusinessStableModerate evidence · 1 source

Smart sensor deployment in precision farming is limited by calibration issues, data privacy concerns, interoperability gaps and adoption barriers.

A January 2026 review in Sensors examined smart sensor technologies in precision farming, describing how integration with IoT and AI has changed how agricultural data is collected, analyzed and utilized to optimize yield and conserve resources.

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

Updated Jul 20, 2026 · TRV-2026-0399

77Index score
46
PolicyStableModerate evidence · 1 source

AI-powered consumer contracts introduce risks of bias, opacity, privacy violations, and power asymmetries due to limited human intervention.

The source describes how artificial intelligence is used to automate the drafting, personalization, and enforcement of consumer contracts at scale with limited human intervention, and presents a comparative legal analysis of how jurisdictions including the EU, US, Canada, Brazil, and Asia-Pacific are responding.

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

Updated Jul 20, 2026 · TRV-2026-0395

77Index score
47
LaborStableModerate evidence · 1 source

Applying GnAI to literary fiction editing raises unresolved ethical and industrial concerns about intellectual property, trust, the author-editor relationship, and whether core professional editing principles translate to AI.

In a July 2024 peer-reviewed paper, researchers examined generative AI in book publishing by using a published story as a test case to compare edits made by GnAI with edits made by professional editors over multiple drafts and at different stages of editorial development. The work focuses on literary fiction editing within trade publishing.

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

Updated Jul 20, 2026 · TRV-2026-0389

77Index score
48
CrimeStableModerate evidence · 1 source

Neural network fraud detection systems in production face concept drift, adversarial evasion attacks, class imbalance, GDPR and LGPD explainability requirements, and sub-100-millisecond latency budgets that constrain deployment.

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

Recomputed live from the record · Oct 11, 2026, 5:14 AM