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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

517
HealthStableModerate evidence · 1 source

Random survival forests models incorporating oxidative stress index score improved prediction of disease-free and overall survival for locally advanced rectal cancer patients receiving neoadjuvant therapy, outperforming conventional ypStage and enabling risk stratification.

Researchers developed a novel oxidative stress index score from liver enzyme biomarkers and built machine learning models to predict disease-free survival and overall survival in 970 locally advanced rectal cancer patients treated at Sun Yat-Sen University Cancer Center. The training cohort received total neoadjuvant therapy and the validation cohort received neoadjuvant chemoradiotherapy, followed by surgery.

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

Updated Aug 14, 2026 · TRV-2026-0754

67Index score
518
HealthStableModerate evidence · 1 source

The CODEX Action Incubator at UCSF convened 30 stakeholders and reached consensus on two priority metrics of AI scribe usage by primary care physicians to assess diagnostic excellence, including timely follow-up of abnormal breast and colorectal cancer screening results.

On September 2025, the CODEX Action Incubator at UCSF brought together 30 stakeholders from health systems, patient advocacy, industry and policy to address how to measure AI's effect on diagnostic excellence. Participants focused on AI scribes and, via a modified Delphi process, narrowed 17 candidate measures to two priority metrics tied to primary care physician usage rates: timely follow-up of abnormal breast and colorectal cancer screening results and patient-reported diagnostic experience.

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

Updated Aug 14, 2026 · TRV-2026-0751

67Index score
519
ScienceStableModerate evidence · 1 source

Generative AI applied to supply chain and operations management can enhance decision-making and optimize processes across areas like demand forecasting and inventory management to improve efficiency, accuracy, and resilience.

Researchers developed a capability-based framework to analyze where artificial intelligence and generative AI fit into supply chain and operations management. Using capabilities like learning, perception, prediction, interaction, adaptation and reasoning, they mapped applications across 13 decision areas including demand forecasting, inventory management, supply chain design and risk management.

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

Updated Aug 12, 2026 · TRV-2026-0739

67Index score
520
HealthStableModerate evidence · 1 source

Guideline-specific and general-purpose LLMs produced responses broadly consistent with EAU erectile dysfunction recommendations, with Gemini 2.5 Pro and EAU Guidelines Bot achieving the highest composite scores.

On 2026-08-07, a peer-reviewed comparative study tested five AI systems including the EAU Guidelines Bot, ChatGPT-5, Gemini 2.5 Pro, Copilot - Smart GPT-5, and Perplexity Pro on 13 questions drawn from strongly recommended EAU erectile dysfunction statements. Three senior reviewers rated each answer for relevance, clarity, structure, clinical utility, and factual accuracy.

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

Updated Aug 10, 2026 · TRV-2026-0727

67Index score

AI problems · 772

517
LaborStableModerate evidence · 1 source

AI adoption is associated with restructuring of work roles and widening wage gaps between AI-skilled and non-AI-skilled workers.

A peer-reviewed study published April 17, 2026 reviewed literature from 2020 to 2025 on AI in the labour market, examining changes in job roles, skill requirements, and HR practices through technological, organisational, and institutional lenses.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0196

67Index score
518
LaborStableModerate evidence · 1 source

AI-driven automation that substitutes for routine tasks reduces demand and wages for low-skilled workers, causing absolute welfare losses for workers below a critical ability threshold and widening income inequality.

On 2026-04-02, a peer-reviewed paper presented a general equilibrium model of AI-driven automation with heterogeneous workers and irreversible skill investments. It finds automation reduces demand and wages for low-skilled workers in routine tasks while enhancing productivity where AI complements high-skilled labor, leading to higher aggregate output and total welfare but a higher skill premium and wider inequality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0195

67Index score
519
EducationStableModerate evidence · 1 source

Generative AI now mediating core parts of learning risks crossing into manipulation and deception and accelerating drift from educational goods to metrics, reshaping development of critical thinking and creativity.

As of May 2026, generative AI mediates core parts of learning, prompting a peer-reviewed conceptual paper to propose criteria for distinguishing legitimate pedagogical uses from manipulative and deceptive ones. It introduces three principles — moral legitimacy, developmental integrity, and value preservation — to assess influence and protect reflective judgement.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0190

67Index score
520
EducationStableModerate evidence · 1 source

Art students classified as authorship guardians and conflicted co-creators report resistance to AI co-authorship and struggles with ownership, disclosure, authenticity, and earned pride when collaborating with generative AI.

Published February 28 2026, this peer-reviewed study examined how art students negotiate creative identity when working with generative AI. Forty students from visual arts, design, music, and animation sorted 42 statements about collaboration, authorship, ethics, and control, and factor analysis revealed five distinct perspectives ranging from human-centered direction to enthusiastic exploration.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0188

67Index score

Recomputed live from the record · Oct 13, 2026, 4:40 PM