+ HEALTH Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (…± HEALTH Rationale and objectives The international application of artificial intelligence (AI) for lesion detection b…± HEALTH Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis…± HEALTH Objectives To conduct a nationwide survey among professionals working in oncology departments in China to inv…+ HEALTH The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area…± HEALTH BACKGROUND . Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) me…+ SCIENCE Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent de…+ HEALTH Background Intracardiac echocardiography (ICE) facilitates left atrial (LA) reconstruction during atrial fibr…
TruaceTracing the truth around AIThursday, August 27, 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.

1,168 results
Show filters and sorting

AI gains · 649

68
GainScience· Stable· Evidence: Moderate (1 source)

Deep learning models composed of multiple processing layers improved state-of-the-art performance in speech recognition, visual object recognition, object detection, drug discovery and genomics by 2015

Published in May 2015, this peer-reviewed overview describes deep learning as models with multiple processing layers that learn multi-level representations of data, trained via backpropagation to adjust parameters between layers

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0209

68
GainScience· Stable· Evidence: Moderate (1 source)

A deep neural network trained on DFT data learned a transferable potential that predicts total energies for organic molecules with chemical accuracy versus DFT, generalizing to systems up to 54 atoms despite training on smaller molecules.

On Feb 8 2017, researchers described ANI, a deep neural network architecture that uses atomic environment vectors derived from modified symmetry functions to learn molecular energies from quantum mechanical DFT calculations, and introduced Normal Mode Sampling to efficiently sample potential surfaces.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0206

68
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%91

Updated Jul 13, 2026 · TRV-2026-0205

68
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%91

Updated Jul 13, 2026 · TRV-2026-0197

AI problems · 519

51
ProblemLifestyle· Stable· Evidence: Moderate (1 source)

Almost 50 years after he first got his hands on a computer, the Oxford professor still believes in the power of technology. Can his beloved game theory explain why Silicon Valley’s entrepreneurs consistently misuse it?

Machine-ingested summary: the claims above reflect a single primary source and have not been weighed against contradicting evidence by a Truvace editor yet.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%34
Recency 10%91

Updated Jul 11, 2026 · TRV-2026-0034

Recomputed live from the record · Aug 27, 2026, 7:23 AM