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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.

1,711 results
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AI gains · 939

453
EducationStableModerate evidence · 1 source

AI/ML integration in adaptive e-learning systems personalizes learning experiences and optimizes learning paths, leading to higher student engagement, retention, and academic performance including increased test scores.

Published December 6, 2023, this peer-reviewed literature review in Education Sciences examined 63 articles from 2010 onward on AI and machine learning in e-learning. It found adaptive algorithms are used to tailor learning paths to individual needs, with multiple studies reporting improved engagement, retention, and academic performance.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0869

68Index score
454
Media & ArtsStableModerate evidence · 1 source

Proposes a Four-Dimensional Evaluation Framework for agentic AI in journalism covering technical quality, human-organizational alignment, ethical-governance responsibility, and trust-value impact to balance innovation with accountability.

As of November 2025, this peer-reviewed review examines agentic AI in newsrooms that can autonomously plan, decide, and generate content. Analyzing 46 sources from 2015-2025, it finds current evaluations emphasize technical accuracy and efficiency while neglecting trust, governance, and collaboration.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0868

68Index score
455
HealthStableModerate evidence · 1 source

Integrated single-cell analysis and three machine learning algorithms identified GLS and LPIN2 as upregulated key genes in kidney stones that promote fibroblast-to-myofibroblast transition, ROS production, calcium deposition and osteogenic-like differentiation, supporting a risk prediction nomogram and drug prediction.

By August 2026, a multi-omics study combined single-cell and bulk RNA-seq from GEO with three machine learning algorithms to screen for kidney stone drivers, identifying GLS and LPIN2 as upregulated in fibroblasts and building a risk prediction nomogram.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0867

68Index score
456
HealthStableModerate evidence · 1 source

Causal Forest analysis of Stage II-III gastric cancer patients found lymph node ratio was the dominant predictor of individualized benefit from adjuvant chemotherapy, with high-LNR patients showing improved survival.

Researchers used a Causal Forest causal machine learning model on a retrospective cohort of gastric cancer patients treated between 2007 and 2017 to estimate who benefits from adjuvant chemotherapy. The model identified lymph node ratio as the most dominant predictor of benefit, with a significant interaction at a 0.25 threshold in a propensity-matched Stage II-III cohort.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0866

68Index score

AI problems · 772

453
HealthStableModerate evidence · 1 source

AI integration in drug discovery still limited by data quality and bias, lack of transparency and interpretability, high computational demands, and privacy and fairness risks.

Published December 12, 2025 as a peer-reviewed review, the article synthesizes how AI and bioinformatics are being applied across pharmaceutical R&D, from target identification to clinical use. It highlights advances in deep learning, graph networks, transformers, foundation models, and tools like AlphaFold, RFdiffusion, and AlphaFold3, reporting observed capabilities such as large-scale structure prediction and workflow compression from five years to 12-18 months.

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

Updated Jul 20, 2026 · TRV-2026-0414

67Index score
454
PolicyStableModerate evidence · 1 source

Use of the AI system for traffic violation appeals impacts decisions made by legal experts and creates tensions between street-level bureaucrats, screen-level bureaucrats and street-level algorithms.

Researchers conducted action research on the development and deployment of an AI system to process traffic violation appeals at a Dutch court, using interviews, observations, documents and a user-experiment to compare decisions made by, with and without the system.

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

Updated Jul 20, 2026 · TRV-2026-0413

67Index score
455
HealthStableModerate evidence · 1 source

ChatGPT responses contained incorrect information in more than one instance and were written at a college graduate reading level, requiring caution for patient education.

In a study published June 22, 2024, two hip preservation surgeons graded ChatGPT 3.5 answers to ten common hip arthroscopy questions drawn from patient education sites, using an A-to-D scale and readability scores FRES and FKGL.

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

Updated Jul 20, 2026 · TRV-2026-0412

67Index score
456
Media & ArtsStableModerate evidence · 1 source

Applying conventional Western-rooted AI tools to Mijikenda musical heritage risks perpetuating digital colonialism through decontextualisation of sacred practices, infringement of data sovereignty, and cultural appropriation.

This peer-reviewed paper examines ethical implications of applying artificial intelligence to Indigenous musical heritage of the Mijikenda communities on Kenya's coast. It finds a divergence between AI's extractive logic and holistic Indigenous Knowledge Systems, with risks of decontextualising sacred practices and infringing data sovereignty.

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

Updated Jul 20, 2026 · TRV-2026-0409

67Index score

Recomputed live from the record · Oct 13, 2026, 9:28 AM