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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

939 gains
772 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

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AI gains · 939

445
HealthStableModerate evidence · 1 source

An unsupervised AI framework discovered a 13-marker cellular morphometric signature from colorectal whole-slide images that transferred to gastric and esophageal cancers and enabled risk stratification of precancerous lesions and early-stage cancers to guide surveillance and intervention.

Researchers developed an unsupervised, interpretable AI framework to define tissue-agnostic cellular morphometric biomarkers that capture conserved tumor microenvironment organization across gastrointestinal organs. Discovered in colorectal cancer slides and validated in gastric and esophageal cancers in a 2,602-patient multi-center cohort, a 13-marker signature showed prognostic value and enabled risk stratification of precancerous lesions and early-stage cancers.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 26, 2026 · TRV-2026-0890

68Index score
446
Media & ArtsStableModerate evidence · 1 source

Quantitative stylometry using Burrows' Delta can reliably separate LLM-generated short stories from human-authored stories, providing a measurable tool for authenticity and authorship checks.

Researchers compared human-authored short stories with stories generated by GPT-3.5, GPT-4, and Llama 70b in response to the same prompts, using Burrows' Delta and clustering methods including hierarchical clustering and multidimensional scaling to visualize stylistic relationships.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0883

68Index score
447
ScienceStableModerate evidence · 1 source

AI-assisted analysis of interview data from a quality improvement evaluation generated four themes that were replicable and grounded in the data.

In a quality improvement program evaluation, researchers used artificial intelligence to identify themes in interview data. By the publication date of 2026-08-24, the approach had produced four replicable themes grounded in the data, while also generating two consistently identified themes that were subtle misrepresentations.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0881

68Index score
448
HealthStableModerate evidence · 1 source

Machine learning and deep learning models trained on MALDI-TOF spectra improved rapid classification of bacteria versus viruses, Gram-positive versus Gram-negative, and individual species, with Extra Trees showing best generalization to an external highly pathogenic bacteria dataset.

Researchers combined MALDI-TOF mass spectrometry with eight machine learning and two deep learning models trained with 5-fold cross validation on 255 spectra from seven bacteria and five viruses, then tested three top performers against an external Robert Koch Institute database of highly pathogenic bacteria.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 25, 2026 · TRV-2026-0880

68Index score

AI problems · 772

445
CrimeStableModerate evidence · 1 source

AI lowers barriers to sophisticated cybercrime by automating phishing, deepfake identity fraud, and adversarial attacks across critical sectors.

This peer-reviewed review from December 2025 examines how AI is reshaping cybersecurity, documenting a rise in AI-powered threats such as automated phishing, deepfake identity fraud, and adversarial machine learning attacks affecting communication networks, healthcare, finance, and government.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0422

67Index score
446
PolicyStableModerate evidence · 1 source

Ecuadorian participants perceived that generative AI was used to disseminate disinformation during the 2025 presidential campaign, that it influenced the election outcome, and that it failed to increase trust in candidates or electoral legitimacy.

Between November 2024 and July 2025, researchers studied Ecuadorian perceptions of generative AI in the 2025 presidential election using surveys, expert interviews and sentiment analysis. They found participants believed AI-generated disinformation affected the result, did not improve trust in candidates or the electoral process, and produced widespread concern, mistrust and fear.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0421

67Index score
447
EducationStableModerate evidence · 1 source

AI implementation in educational settings is associated with validated concerns about reduced human connection, data privacy and security risks, algorithmic bias, lack of transparency, and equity and reliability issues.

Published May 2024, this peer-reviewed study examined the downsides of AI in education through a systematic review of 56 studies and a validation survey of 260 participants from a Saudi Arabian university. It developed and tested a model that confirms concerns spanning human connection, data privacy and security, algorithmic bias, transparency, critical thinking, access equity, ethics, teacher development, reliability, and AI-generated content.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0420

67Index score
448
EducationStableModerate evidence · 1 source

Integrating Google Gemini into education presents significant challenges and ethical considerations that must be addressed.

Published May 23, 2024, this emerging technology report reviews Google Gemini as a multimodal generative AI tool, describing its ability to process text, image, audio, and video inputs and generate diverse content, and summarizing recent empirical studies and technology-in-practice examples in education.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0419

67Index score

Recomputed live from the record · Oct 13, 2026, 8:43 AM