+ SCIENCE This study investigates the impact of a novel application of generative artificial intelligence (AI) in physi…− HEALTH The rapid integration of artificial intelligence (AI) into everyday life has created new intersections among…+ HEALTH Introduction Stereotactic Radiotherapy plays a main role in Brain Metastases treatment. Radiomics and Dosiomi…+ HEALTH Background Internet Gaming Disorder (IGD) poses significant psychological and social risks for adolescents. I…± HEALTH Background Major depressive disorder (MDD) is associated with high relapse rates, with around 20% of inpatien…± HEALTH Despite a decade in, immunotherapy (IO) treatment selection in non-small cell lung cancer (NSCLC) remains lar…+ HEALTH Rib fractures are a common type of chest injury, and the estimation of the fracture age mainly relies on clin…± HEALTH Abstract The integration of artificial intelligence (AI) into laboratory medicine has undergone a remarkable…
TruaceTracing the truth around AITuesday, September 15, 2026
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The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,418 results
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AI gains · 787

78
GainMedia & Arts· Stable· Evidence: Moderate (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%91

Updated Jul 31, 2026 · TRV-2026-0604

78
GainHealth· Stable· Evidence: Moderate (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%91

Updated Jul 31, 2026 · TRV-2026-0599

78
GainHealth· Stable· Evidence: Moderate (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%91

Updated Jul 31, 2026 · TRV-2026-0598

78
GainClimate· Stable· Evidence: Moderate (1 source)

Gradient-boosted machine learning predicted carbapenem MICs from resistance gene profiles with strong performance for imipenem and ertapenem, supporting AMR surveillance.

A One-Health study analyzed 30,554 E. coli whole-genome sequences from human, animal and environmental sources across 126 countries from 2000 to 2025, using AMRFinderPlus and MLST to map resistance genes and clones, and applied gradient-boosted machine learning to predict MICs from gene profiles and chromosomal features.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%91

Updated Jul 29, 2026 · TRV-2026-0584

AI problems · 631

77
ProblemPolicy· Stable· Evidence: Moderate (1 source)

Organizations attempting to embed generative AI into strategic decision-making struggle to move beyond pilots due to hallucination risks, prompt-engineering deficiencies, data readiness gaps, and privacy or IP concerns.

Researchers conducted a qualitative single-case study of a global multi-brand group to examine how generative AI moves from pilots to routinised inputs in strategic decision-making. Based on 27 executive interviews and document triangulation, they identified enablers and barriers and proposed a four-stage pathway from Awareness and Exploration to Institutionalisation and Transformation with quality, provenance, explainability and accountability gates.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0400

77
ProblemBusiness· Stable· Evidence: Moderate (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%89

Updated Jul 20, 2026 · TRV-2026-0399

77
ProblemPolicy· Stable· Evidence: Moderate (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%89

Updated Jul 20, 2026 · TRV-2026-0395

77
ProblemLabor· Stable· Evidence: Moderate (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%89

Updated Jul 20, 2026 · TRV-2026-0389

Recomputed live from the record · Sep 15, 2026, 10:36 AM