− 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 AIFriday, August 28, 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
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AI gains · 649

68
GainHealth· Newly added· Evidence: Moderate (1 source)

A convolutional neural network using standard 12-lead ECGs can estimate elevated NT-proBNP levels with strong correlation and good discrimination in internal and external validation.

Investigators built and validated an AI model that estimates serum NT-proBNP levels from routine 12-lead ECGs, training on nearly 85,000 ECG-lab pairs and testing internally in over 8,500 patients and externally in 679 patients at two tertiary centers.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 15, 2026 · TRV-2026-0773

68
GainPolicy· Newly added· Evidence: Moderate (1 source)

The review proposes an integrated dual-layer governance model that aligns AI-specific risk identification with ISO 14971 processes and EU AI Act obligations, giving regulators and manufacturers a clearer actionable pathway for lifecycle monitoring.

A peer-reviewed review published August 13, 2026 examined how AI-enabled medical devices challenge traditional safety-risk management. Drawing on 19 academic and regulatory sources, it found ISO 14971, AAMI CR34971 and the EU AI Act each cover parts of device safety and algorithmic governance but remain fragmented in practice.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 15, 2026 · TRV-2026-0771

68
GainScience· Newly added· Evidence: Moderate (1 source)

A delta-ML graph neural network using GFN2-xTB geometries and DFT single-points as low-fidelity inputs predicted PBE0-D3BJ and PBE-D3BJ level properties of transition metal complexes with higher accuracy and better data efficiency than a conventional benchmark.

Researchers presented a delta-ML approach for transition metal complexes that uses GFN2-xTB geometry optimizations combined with DFT single-point calculations to produce low-fidelity inputs, which are converted to graph representations for a graph neural network trained to predict high-fidelity quantum properties from the tmQMg dataset.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 15, 2026 · TRV-2026-0770

68
GainHealth· Newly added· Evidence: Moderate (1 source)

ML-based phenomapping of 106,490 Danish KC patients identified 7 distinct subgroups differentiated by disease burden, comorbidities and socioeconomic status, providing a basis for tailored clinical pathways.

On 2026-08-13, a peer-reviewed study reported machine-learning-based phenomapping of 106,490 keratinocyte carcinoma patients from the Danish Skin Cancer Registry (2014-2022). The model derived seven clusters ranging from young, well-educated, high-income, medically noncomplex females with low-risk BCCs to highly comorbid patients with more SCCs and immunosuppressive drug exposure.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 15, 2026 · TRV-2026-0768

AI problems · 520

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

AI text-to-music systems can generate folk and rock songs almost instantly but simulate stylistic language without situated experience, while social media platforms recode musical authenticity into visibility metrics and algorithmic circulation.

Published June 12, 2026, this peer-reviewed analysis examines generative AI's shift of music production from specialist studios to prompt-based, platform-mediated creation, focusing on folk, rock, and protest traditions. Using a qualitative critical-hermeneutic approach, it analyzes selected songs and literature on text-to-music generation, copyright, and embodied creativity.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 22, 2026 · TRV-2026-0469

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

In academia, the same ChatGPT use for personal skill development may impair a person's capacity for critical thought and content production and provide false information tied to training dataset errors.

As of March 27 2024, this peer-reviewed review examined ChatGPT, described as Chat Generative Pre-trained Transformer, often referred to as ChatGPT-4, and its uses in academia. Using a noticing-collecting-thinking approach to review literature and online resources, it categorized opportunities and constraints into research, education, personal skill development, and social aspects.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0464

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

LLM chatbots supporting GBV survivors can generate hallucinations or errors that create unintended harms for individual users.

Published 7 November 2025 in International Review of the Red Cross, this peer-reviewed analysis examined proposals to use LLM-powered chatbots to provide direct care, information and mental health and psychosocial support to women and girls in armed conflicts and humanitarian emergencies, with a focus on gender-based violence survivors.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0463

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

Emotional AI risks hollowing human connection by replacing shared vulnerability with predictive performance, fostering emotional substitution and blurring authentic care with algorithmic response.

Published November 17 2025 in Frontiers in Psychology, the article examines how AI has moved into emotion through affective computing that detects and simulates affect. It describes chatbots like Replika, Woebot and Kuki and therapeutic bots that use warmth and validation cues, and argues they create a compassion illusion where recognition is mistaken for resonance.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0462

Recomputed live from the record · Aug 28, 2026, 4:28 AM