− 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

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

AI systems using RNNs, Transformers and GANs can compose coherent melodies and full pieces, increasing efficiency and diversity of music creation.

This peer-reviewed overview from November 2025 surveys how music generation moved from rule-based approaches to deep learning models such as RNNs, Transformers and GANs, detailing their architectures and training and noting current ability to produce coherent melodies and complete pieces.

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

Updated Jul 20, 2026 · TRV-2026-0448

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

Adoption of AI, automation and data analytics in HR is improving how organizations handle talent acquisition, development and retention and enabling more agile organizational designs.

Published 19 November 2025, this peer-reviewed review in Administrative Sciences consolidates recent literature on technology-driven change in human resource management. It examines AI, automation and data analytics as drivers and assesses their impact on talent acquisition, development and retention and on organizational design.

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

Updated Jul 20, 2026 · TRV-2026-0446

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

AI and related digital tools are transforming how organizations attract, develop, and retain talent, enabling human capital to act as a strategic driver of sustainable competitive advantage, as illustrated by corporate use of predictive analytics and people analytics.

Published November 11, 2025, this peer-reviewed paper examines how AI, RPA, blockchain, and immersive technologies are redefining strategic human resource management. Drawing on a systematic literature review, institutional reports, and illustrative cases from IBM, Walmart, Unilever, and UiPath, it argues human capital has shifted from passive input to strategic enabler and proposes a conceptual model linking emerging technologies, SHRM practices, and competitiveness.

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

Updated Jul 20, 2026 · TRV-2026-0445

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

State-of-the-art AI techniques are advancing quantum computing development across the hardware and software stack from device design to applications, with QC scaling challenges resting on AI developments.

A December 2, 2025 review in Nature Communications examines how AI is being applied to quantum computing. It describes AI's data-driven learning as well-suited to QC's counterintuitive nature and high-dimensional mathematics and reviews existing uses across the stack needed for useful QC, from device design to applications.

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

Updated Jul 20, 2026 · TRV-2026-0442

AI problems · 520

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

Customer concern that the Reveal LINQ implantable monitor's AI adjudication classified true pause episodes as false, following undersensing and false pause episodes

The FDA received a malfunction report for the Reveal LINQ implantable cardiac monitor made by Medtronic europe sarl. The report describes false pause episodes due to undersensing and a customer concern that the device's AI algorithm adjudicated episodes the customer considered true pauses as false. The device remains implanted.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%91

Updated Jul 12, 2026 · TRV-2026-0041

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

The Accurhythm za420 pause monitor's AI algorithm rejected true pause episodes as false, causing a detection failure

The FDA received a malfunction report for the Accurhythm za420 (pause) device made by Medtronic, inc. The report stated that the device's artificial intelligence algorithm rejected true pause episodes as false. The source notes that no patient complications have been reported as a result of this event.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%91

Updated Jul 12, 2026 · TRV-2026-0040

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

An FDA malfunction report described the Linq II insertable cardiac monitor's AI algorithm rejecting true pause episodes and generating false ventricular tachycardia detections, with no complications reported to date.

The FDA received a malfunction report for the Linq ii insertable cardiac monitor made by Medtronic singapore operations. According to the report, the device's AI algorithm rejected true pause episodes as false and detected false ventricular tachycardia episodes. The monitor remains implanted and in use, and the report states no patient complications have been reported as a result of this event.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%91

Updated Jul 12, 2026 · TRV-2026-0039

60
ProblemLabor· Stable· Evidence: Moderate (1 source)

Companies began citing AI in layoffs and recent graduates faced 5.6% unemployment, with AI identified as a possible contributing factor.

A year after predictions that AI would wipe out half of entry-level white-collar jobs, a Guardian report on 12 August 2026 cites a Stanford Institute for Economic Policy Research analysis finding no major displacement since 2022, with the most AI-exposed workers seeing a 0.77 point unemployment rise versus 0.85 for the least-exposed.

Impact 30%63
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%97

Updated Aug 13, 2026 · TRV-2026-0740

Recomputed live from the record · Aug 27, 2026, 10:06 PM