+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
TruaceTracing the truth around AIMonday, September 14, 2026
Subscribe
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.

1,402 results
Show filters and sorting

AI gains · 778

67
GainScience· Stable· Evidence: Moderate (1 source)

Decision-tree ensemble approach called deep forest achieves excellent performance across different domains using the same default setting while requiring fewer hyper-parameters and automatically determining model complexity without gradient-based adjustment or backpropagation.

As of the 2018-10-08 publication, researchers proposed deep forest, a decision-tree ensemble that stacks non-differentiable modules in layers to replicate deep learning characteristics without using backpropagation for training.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0205

67
GainCrime· Stable· Evidence: Moderate (1 source)

Trust and Safety defenders can use generative AI to detect and mitigate harmful content at scale and support investigations and moderator wellbeing.

On April 13, 2026, a peer-reviewed CHI paper reported a qualitative study of 43 Trust & Safety experts across child safety, election integrity, hate and harassment, scams, and violent extremism. It found generative AI both expands attacker capabilities and offers new defensive tools for detection and mitigation.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0197

67
GainScience· Stable· Evidence: Moderate (1 source)

ApexGO generated optimized peptide derivatives that showed enhanced antimicrobial activity in vitro and potent anti-infective efficacy in mouse models of Acinetobacter baumannii infection.

Researchers developed ApexGO, a generative AI approach that modifies existing peptide scaffolds using a transformer variational autoencoder and Bayesian optimization. Using ten template peptides, the system proposed optimized derivatives, 100 of which were synthesized and tested in vitro and in two mouse models of Acinetobacter baumannii infection.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0193

67
GainEducation· Stable· Evidence: Moderate (1 source)

Art students classified as enthusiastic explorers and human-centered directors report using generative AI as a controllable tool that enables novel expression and co-agency in creative work.

Published February 28 2026, this peer-reviewed study examined how art students negotiate creative identity when working with generative AI. Forty students from visual arts, design, music, and animation sorted 42 statements about collaboration, authorship, ethics, and control, and factor analysis revealed five distinct perspectives ranging from human-centered direction to enthusiastic exploration.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0188

67
GainHealth· Stable· Evidence: Moderate (1 source)

University students in Qatar reported willingness to use AI-based mental health tools for stress management as a complement to human care, valuing lower cost and round-the-clock access.

On 2026-05-06 a peer-reviewed survey study reported results from 220 university students in Qatar about AI in mental health support. Students reported low-to-moderate awareness and trust, said they were prepared to use AI for stress management but did not want it to replace face-to-face therapy, and cited benefits of cost reduction and 24/7 accessibility alongside concerns about human interaction, overreliance and diagnostic accuracy.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0184

67
GainHealth· Stable· Evidence: Moderate (1 source)

HAIRA maturity model provides tiered benchmarks that let healthcare organizations assess current governance and advance based on available resources.

On 2026-02-11, authors reported a systematic review of 35 healthcare AI implementation frameworks from 2019-2024, identifying seven critical governance domains. They used those findings to develop HAIRA, a five-level maturity model from Level 1 Initial/Ad Hoc to Level 5 Leading with benchmarks across the domains.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0182

67
GainEducation· Stable· Evidence: Moderate (1 source)

AI-enhanced pedagogical practices were associated with higher mathematical language proficiency, communication and reasoning among senior high school students in Ghana, with digital literacy and learning engagement acting as mediators.

Published May 26, 2026, this peer-reviewed study investigated AI-enhanced pedagogical practices and mathematical language proficiency among 360 senior high school students in Ghana. Using structural equation modeling, it found AEPP, digital literacy, and learning engagement were significant positive predictors of proficiency, with the latter two mediating the relationship.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0180

67
GainPolicy· Stable· Evidence: Moderate (1 source)

Among active social media users in Kazakhstan, perceiving algorithmic personalization was under certain conditions associated with a higher sense of control over information choices.

By May 2026, researchers reported a mixed-methods study of 1,795 active social media users in Kazakhstan examining how perceptions of algorithmic influence and AI manipulation relate to democratic indicators. They found perceived personalization was modestly negatively linked to trust and discussion quality but sometimes linked to greater control, while perceived AI manipulation was strongly negatively linked to all indicators, especially for Telegram and YouTube users and those aged 18-29.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0179

AI problems · 624

No problems on this page.

Recomputed live from the record · Sep 14, 2026, 1:57 PM