+ 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…± HEALTH Objective To examine whether the outputs of machine learning algorithms designed to predict risk of cardiovas…
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.

1,402 results
Show filters and sorting

AI gains · 778

67
GainMedia & Arts· Stable· Evidence: Moderate (1 source)

Generative AI can learn from large music datasets to create new compositions and realistically reproduce a specific artist's voice.

By June 2026, a law review article described how generative AI had been used to create a viral 2023 track, 'Heart on My Sleeve,' that imitated Drake and The Weeknd. The label confirmed the vocals were not the artists but an AI imitation trained on their recordings, after millions had been convinced it was real.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0146

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

AI models using EEG, speech, motor data, wearables and video have been applied to detect concussion and quantify head-impact exposure while reducing false-positive events.

By December 2025, a scoping review of six databases identified 55 studies of artificial intelligence across the concussion care pathway, from detection and diagnosis using EEG, speech and motor data to monitoring with wearables, mouthguards and video, plus prognosis and prevention modeling.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0141

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

Higher critical thinking in AI use was associated with better detection of inaccurate information during a GPT-powered chatbot interaction, including use of more verification strategies.

By May 2026, researchers had developed and validated a 13-item critical thinking in AI use scale across six studies totaling 1341 participants. The work defined the construct as verifying AI-generated information, understanding how models work and fail, and reflecting on implications of reliance, and confirmed a structure of Verification, Motivation, and Reflection.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0136

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

Japanese university students using GenAI develop conditional trust and reflexive evaluation through an ongoing process of moral calibration.

By July 2026, researchers had surveyed 69 Japanese university students about everyday GenAI use and analyzed responses with systematic grounded theory. They found students moved through an iterative moral trajectory from recognizing power and risk to feeling ethical anxiety, then to reflexive evaluation and conditional trust, captured in the NECoAI model.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0129

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

Co-design workshops with 32 young people generated system requirements for reconceptualising the professional-focused Mia genAI chatbot for young consumers and integrating it into Australian youth services, with potential to benefit youth mental health.

On 2026-06-19, a peer-reviewed study reported online workshops with 32 young people to examine the Mental health Intelligence Agent Mia, a genAI chatbot originally designed for professionals in Australian youth services. Following co-design, participants explored perceptions of genAI chatbots in youth mental health and developed recommendations for reconceptualising Mia for consumers and integrating it into services, with four themes identified through reflexive thematic analysis.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0127

67
GainMedia & Arts· Stable· Evidence: Moderate (1 source)

Singing datasets enable development of high-fidelity AI voice synthesis models.

By July 2026, researchers examined singing data collection as AI voice synthesis advanced, analyzing three singing datasets with the Ethically Aligned Stakeholder Elicitation framework. They found data-contributors have event-centric roles with minimal authority over licensing and access, while data-collectors retain control.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0125

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

Expert-guided and LLM-augmented feature selection improved generalizability of data-driven models for nitrous oxide emission prediction at a full-scale wastewater plant, maintaining temporal dynamics under out-of-distribution high-flow conditions where attention-based deep learning failed.

By the publication date of 2026-07-12, researchers tested a knowledge-driven feature selection framework for data-driven wastewater modeling, comparing classic attention-based deep learning against expert-guided and LLM-augmented selection. In the reported case study of N2O emissions at a full-scale plant, expert-guided selection achieved mean R2 0.723 and MAE 0.033, slightly above the best attention model at R2 0.712, while LLM-augmented reached R2 0.596 and MAE 0.041.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0120

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

AIGC lowers prior constraints on user innovation and drives innovative behavior through external technical and content factors and internal user factors.

As of the July 10 2026 publication date, researchers reported a grounded theory study of 1,502 public articles and more than 120,000 words of interviews to examine how AIGC influences user innovation. They built a TCEU framework where technical factors and content factors act as external drivers and user factors act as internal drivers, with technology popularity and platform convenience as moderators.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0112

AI problems · 624

No problems on this page.

Recomputed live from the record · Sep 14, 2026, 1:53 PM