± HEALTH Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and gener…+ 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…
TruaceTracing the truth around AIMonday, September 14, 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.

1,404 results
Show filters and sorting

AI gains · 779

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

AI adoption in organizations can improve work practices by increasing efficiency, productivity, and innovation.

A systematic literature review published November 28, 2024 examined how artificial intelligence is transforming organizational landscapes. It found AI reshapes work practices through automation and changes to decision making and employee roles, while driving cultural shifts toward innovation, agility, and continuous learning.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%89

Updated Jul 19, 2026 · TRV-2026-0273

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

Deep learning drives advancements in biology, chemistry, and physics by facilitating analysis of protein folding, molecular discovery, and particle interactions.

As of November 27, 2024, this peer-reviewed review paper surveys the state of deep learning, describing its role as a core component of modern AI and summarizing evolution from CNNs and RNNs to transformers, GANs, capsule networks, and GNNs, plus training methods like self-supervised, federated, and deep reinforcement learning.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%89

Updated Jul 19, 2026 · TRV-2026-0272

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

Machine learning models that incorporate compressed respiratory and oxygenation timeseries improved early prediction of BPD at 7 days after birth compared to clinical-data-only models.

In a retrospective study of 513 preterm infants in a neonatal intensive care unit between 2009-2015, investigators developed machine learning models to predict bronchopulmonary dysplasia within one week after birth using clinical data and respiratory and oxygenation timeseries. The best model combined clinical data with advanced timeseries features processed by a long short-term memory network, achieving AUC 0.83.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%89

Updated Jul 19, 2026 · TRV-2026-0270

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

Deep learning models, especially U-Net variants and newer transformer ensembles, improved ischemic stroke lesion segmentation on MRI, achieving Dice scores above 0.80 and approaching 0.90 in multisite DWI reports, to support faster treatment selection and quantification.

By July 2026, a narrative review of 40 studies from 2020-2025 found deep learning, led by U-Net variants with residual and attention mechanisms and standardized pipelines like nnU-Net, increasingly achieved high Dice scores on MRI DWI/ADC, with many reports above 0.80 and recent transformer and ensemble multisite models approaching 0.90, while CT performance was lower and more variable.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%89

Updated Jul 19, 2026 · TRV-2026-0268

AI problems · 625

55
ProblemClimate· Stable· Evidence: Moderate (1 source)

Generative AI systems expend large amounts of electricity and associated planet-heating emissions to produce nonsensical or misleading slop content

By 17 Nov 2025, reporting from Cop30 in Belém, Brazil noted that artificial intelligence was widely associated with heavy electricity consumption to produce low-value slop content, creating planet-heating emissions. At the same summit, some advocates advanced a counter-narrative that AI could be applied to climate solutions.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0103

55
ProblemEducation· Stable· Evidence: Moderate (1 source)

Young people entering the workforce are projected to face job displacement as AI transforms work.

On a US podcast before 13 September 2025, OpenAI CEO Sam Altman said that if he was graduating today he would feel like the luckiest kid in history, arguing that ChatGPT, released in November 2022, shows AI's transformative power. He acknowledged that job displacement will occur but said this always happens.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0098

55
ProblemEducation· Stable· Evidence: Moderate (1 source)

Lecturers using artificial intelligence to create teaching materials risks degrading teaching quality and prompting student backlash.

A Guardian letters page publishes a response to a 20 November story about students fighting back over a course taught by AI. The writer, a recent doctoral graduate on fixed or zero-hours contracts, says each taught hour may take days of unpaid preparation and describes creating work plans, assessments, reading lists and tutorial tasks for three modules, while disagreeing with lecturers' decision to use artificial intelligence to create teaching materials but understanding the incentives.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0097

55
ProblemBusiness· Stable· Evidence: Moderate (1 source)

Americans risk having pension plans and investment portfolios tied to a frenzied, risky multibillion-dollar race to build AI that automates cognitive tasks

The article reports that despite broad public worry about AI, upcoming large IPOs including SpaceX will push AI exposure into ordinary Americans' pension plans and investment portfolios, linking household financial futures to tech firms whose current revenue comes from internet access but whose capital needs are driven by AI ambitions like orbital datacenters.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0094

Recomputed live from the record · Sep 14, 2026, 5:03 PM