TruaceTracing the truth around AIFriday, August 28, 2026
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.

1,182 results
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AI gains · 658

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

Final-year dental students reported frequent LLM use to save time and support learning, including clarifying and understanding complex concepts.

A cross-sectional survey of 454 final-year dental students in the UAE, Jordan, Malaysia, Oman, and Brazil examined LLM use, motivations, and safeguards. Published August 6 2026, it found ChatGPT predominated at 95.9%, with 39.2% using LLMs several times per week and 28.6% daily for tasks like understanding complex concepts and summarising lecture notes.

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

Updated Aug 7, 2026 · TRV-2026-0675

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

Machine learning models achieved moderate diagnostic accuracy for predicting clinical pregnancy or live birth after assisted reproductive technology, with pooled sensitivity 0.737 and specificity 0.789.

A systematic review and diagnostic meta-analysis of 20 studies, 14 in quantitative synthesis, evaluated machine learning models to predict clinical pregnancy or live birth after assisted reproductive technology. As of the August 2026 publication, pooled sensitivity was 0.737 and specificity 0.789 with a DOR of 10.49 and acceptable discrimination on SROC, but heterogeneity was very high.

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

Updated Aug 7, 2026 · TRV-2026-0674

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

In a 34-case glaucoma reasoning test, LLM systems produced structured reasoning with weighted scores overlapping attending ophthalmologists and often included safety-critical diagnostic and management elements.

Researchers compared large language models and clinicians on 34 real-world glaucoma cases, with glaucoma specialists scoring responses on medical accuracy, key-point recall, and logical completeness. AI models produced structured reasoning with weighted mean scores overlapping attending ophthalmologists and exceeding some residents.

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

Updated Aug 7, 2026 · TRV-2026-0673

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

Large language models such as ChatGPT can provide personalized learning experiences when integrated into medical education.

Published October 22, 2025, this PLOS One scoping review examined literature on AI and large language models like ChatGPT in medical education. It found potential for personalized learning alongside a set of ethical challenges, synthesizing 50 studies from three major databases covering 2010 to August 2024.

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

Updated Aug 6, 2026 · TRV-2026-0670

AI problems · 524

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

Fraudsters using AI to commit financial fraud cause significant damage to financial institutions and their clients.

A January 2026 peer-reviewed article in Journal of Banking Regulation examines AI-driven financial fraud, noting AI is integral to bank operations while also being used by fraudsters to inflict significant damage on institutions and clients.

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

Updated Jul 20, 2026 · TRV-2026-0396

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

Generative art systems accelerate inequities in visual arts by appropriating intellectual property from marginalised artists and reinforcing Eurocentric and gendered commodification.

Published January 2026 in AI & SOCIETY, this peer-reviewed paper examined AI artistic collaborations, art competition controversies, interviews with professionals, and gallery experiments to test how generative models handle creativity, authorship, labour and representation.

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

Updated Jul 20, 2026 · TRV-2026-0394

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

In creative applications, AI hallucinations require essential human oversight for creative direction, alongside emerging challenges of copyright concerns, bias mitigation, high computational demands, and lack of robust regulatory frameworks.

Published January 24, 2026, this systematic review examines AI advances since 2022, particularly generative AI, LLMs, and diffusion models, and their application across the creative production pipeline from creation to compression and quality assessment.

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

Updated Jul 20, 2026 · TRV-2026-0392

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

Users reported AI limitations, lack of cohesive and predictable interactions, and user interface issues that detracted from experience with Wysa.

A peer-reviewed study in mHealth analyzed 159 Google Play reviews of Wysa, a commercial AI-driven mental health conversational agent, posted between January 2020 and March 2024. Using thematic analysis, the authors identified seven themes capturing both positive perceptions and frustrations with the chatbot.

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

Updated Jul 20, 2026 · TRV-2026-0391

Recomputed live from the record · Aug 28, 2026, 9:56 AM