− MEDIA & ARTS The article is devoted to a comprehensive analysis of modern problems of protecting musical works in the cont…+ 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…
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.

1,169 results
Show filters and sorting

AI gains · 649

71
GainScience· Rising· Evidence: High (2 sources)

Artificial neural networks have achieved practical deployment for pattern recognition tasks within manufacturing industries.

The source is a review of artificial neural network applications to pattern recognition. It describes an early era of simplified ANN use that expanded into multiple domains and reports progress surveyed in the literature, while highlighting persistent technical obstacles that prompted a call for state-of-the-art updates.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%99
Recency 10%91

Updated Jul 12, 2026 · TRV-2026-0066

71
GainScience· Rising· Evidence: High (5 sources)

The development and release of ethics guidelines provides normative principles intended to help harness disruptive potentials of AI systems in research, development and application.

This paper conducts a semi-systematic evaluation that analyzes and compares 22 ethics guidelines released in recent years following advances in research, development and application of AI systems. The guidelines are described as comprising normative principles and recommendations.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%91

Updated Jul 12, 2026 · TRV-2026-0056

71
GainPolicy· Rising· Evidence: High (5 sources)

Breakthroughs in algorithmic machine learning and autonomous decision-making are engendering new opportunities for continued innovation and augmentation of human tasks.

The source text presents AI as a transformative force comparable to the industrial revolution, highlighting a staggering pace of change driven by breakthroughs in algorithmic machine learning and autonomous decision-making. It states this enables augmentation and potential replacement of human tasks across industrial, intellectual and social applications, with potential disruption to finance, healthcare, manufacturing, retail, supply chain, logistics and utilities.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%91

Updated Jul 12, 2026 · TRV-2026-0049

70
GainHealth· Stable· Evidence: Moderate (1 source)

In a randomized double-blind crossover study of text consultations, AMIE showed greater diagnostic accuracy than primary care physicians.

Researchers introduced AMIE, an LLM-based system for diagnostic dialogue, and tested it against 20 primary care physicians in 159 text-based scenarios with patient-actors from Canada, the UK and India. Specialist and patient-actor raters scored performance across 32 and 26 axes including history-taking and management.

Impact 30%55
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 24, 2026 · TRV-2026-0530

AI problems · 520

68
ProblemMedia & Arts· Stable· Evidence: Moderate (1 source)

Participating writers worried that AI assistance could weaken creative control, interrupt their established process or reproduce a fixed version of their style rather than support continued experimentation and growth.

A mixed-methods study examined how 19 professional writers and 30 avid readers understood authenticity in writing produced with AI assistance. Writers completed short writing tasks using both personalized and non-personalized GPT-4 suggestions, while readers evaluated passages written independently or with either form of AI support.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 8, 2026 · TRV-2026-0692

68
ProblemMedia & Arts· Stable· Evidence: Moderate (1 source)

Fully automated AI generation of news risks bias, manipulation, lack of accountability, inaccuracy, and erosion of accuracy, trust, and copyright principles.

By June 2026, a peer-reviewed paper analyzed how AI and AIGC are being integrated into newsrooms, from data mining to co-creation of news products, increasing efficiency and output volume while prompting questions about human professionalism and editorial control.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 8, 2026 · TRV-2026-0697

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

Personal-data-driven algorithmic targeting and other manipulative design features like infinite scrolling directly cause primary harms of manipulation, including hijacking attention, overriding user autonomy, and engineering addiction.

Published July 6 2026, this law review article argues that Section 230 and the First Amendment do not categorically immunize digital platforms for harms caused by their own design choices. It proposes a typology separating direct primary harms from design decisions from secondary harms from user content and tertiary harms, focusing on personal-data-driven algorithmic targeting and dark patterns like infinite scrolling.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 8, 2026 · TRV-2026-0695

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

ChatGPT-5.0 did not achieve the highest overall classification accuracy and had lower specificity than XGBoost, indicating it missed the top performance for correctly identifying non-extraction cases.

A comparative study published August 7, 2026 evaluated ChatGPT-5.0 against five supervised machine learning algorithms for orthodontic extraction decisions. Using 520 cases (42.88% extraction, 57.12% non-extraction) and 23 clinical, cephalometric and photographic variables, with expert consensus as reference, ChatGPT-5.0 achieved 75.77% accuracy and 76.68% sensitivity under 5-fold cross-validation, compared to 78.08% accuracy for XGBoost.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 8, 2026 · TRV-2026-0689

Recomputed live from the record · Aug 27, 2026, 6:34 PM