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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

449
HealthStableModerate evidence · 1 source

A lifecycle-oriented operational framework with nine measures was proposed to engineer transparency into AI-enabled medical devices to improve regulatory readiness and calibrated clinical trust.

Published August 2026, this peer-reviewed synthesis addresses transparency as a foundational condition for trustworthy AI in healthcare. It finds current methods to operationalize transparency across AI-enabled medical devices are fragmented, and proposes a SaMD lifecycle framework to map regulatory and standards requirements to concrete development and governance steps.

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

Updated Aug 25, 2026 · TRV-2026-0879

68Index score
450
HealthStableModerate evidence · 1 source

An interpretable machine learning model integrating genetic risk scores, corneal biomechanical parameters, and tear molecular biomarkers was developed and validated to predict progression risk in patients with confirmed normal-tension glaucoma, providing a quantitative reference for risk stratification and personalised

On August 24, 2026, a peer-reviewed study reported development and validation of an interpretable machine learning model to predict progression risk in 342 patients with normal-tension glaucoma enrolled at a tertiary hospital. The team integrated corneal biomechanical parameters, genetic risk scores, and tear molecular biomarkers, selecting predictors with LASSO and multivariable logistic regression, and compared random forest, SVM, and logistic regression models using AUC, calibration, and decision curve analysis with SHAP interpretation.

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

Updated Aug 25, 2026 · TRV-2026-0875

68Index score
451
HealthStableModerate evidence · 1 source

AI applications improved early detection and risk stratification for depression, anxiety, PTSD and suicidal ideation and expanded access through chatbots and mobile platforms for monitoring and self-management.

This umbrella review synthesized 27 systematic reviews with over 14 million participants to examine AI applications in mental health care between 2021 and 2025. It found AI improved early detection and risk stratification for depression, anxiety, stress, PTSD and suicidal ideation, and that chatbots and mobile platforms expanded access and engagement.

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

Updated Aug 25, 2026 · TRV-2026-0874

68Index score
452
BusinessStableModerate evidence · 1 source

Integration of AI and blockchain promises enhanced resource efficiency, optimized supply chains, and improved product lifecycle management to advance the circular economy.

A December 2023 peer-reviewed analysis in Environmental Technology & Innovation used bibliometric methods to examine how new technologies, especially blockchain and artificial intelligence, are being applied to circular economy goals. The authors describe production and consumption as environmentally unsustainable and assess literature on opportunities and challenges.

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

Updated Aug 24, 2026 · TRV-2026-0870

68Index score

AI problems · 772

449
PolicyStableModerate evidence · 1 source

ChatGPT deployment raises unresolved issues across sustainability, privacy, digital divide, and ethics that the authors argue require formal SPADE evaluation

Published May 5 2024, this peer-reviewed review argues that ChatGPT and subsequent conversational bots should be assessed through a Sustainability, PrivAcy, Digital divide, and Ethics (SPADE) lens. It surveys issues and concerns raised over ChatGPT in those four areas and briefly discusses the recent EU AI Act in that context.

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

Updated Jul 20, 2026 · TRV-2026-0418

67Index score
450
PolicyStableModerate evidence · 1 source

Generative AI may expand misinformation, distribute workplace benefits unevenly, widen the digital divide in education, and deepen pre-existing inequalities in healthcare.

Published May 31, 2024 in PNAS Nexus, this peer-reviewed overview examines how generative AI could both exacerbate and ameliorate socioeconomic inequalities across information, work, education, and healthcare. It notes potential gains like democratized content creation, productivity boosts, personalized learning, and improved diagnostics alongside risks of misinformation proliferation and unevenly distributed benefits.

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

Updated Jul 20, 2026 · TRV-2026-0417

67Index score
451
Media & ArtsStableModerate evidence · 1 source

Current copyright regimes are ineffective at assigning ownership and authorship for music autonomously composed by non-human AI creators.

A December 2025 peer-reviewed paper in ShodhKosh examines management of AI-generated music intellectual property. It describes autonomous composition via deep-learning and neural networks, analyzes how human and AI creativity differ on intent and originality, and finds existing copyright regimes ineffective at assigning ownership and authorship to non-human creators.

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

Updated Jul 20, 2026 · TRV-2026-0416

67Index score
452
LaborStableModerate evidence · 1 source

AI use in recruitment and hiring creates a heightened risk of concealing and reproducing organizational inequalities through algorithmic invisibility and growing legitimacy of AI solutions.

A December 2025 review in Human Relations examined the growing use of artificial intelligence in recruitment and hiring and its implications for organizational inequalities. Using a hybrid scoping and problematizing approach, the authors synthesized multidisciplinary literature and found asymmetries in conceptualization, a heightened potential for AI to conceal inequalities, and ongoing contestation over regulation.

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

Updated Jul 20, 2026 · TRV-2026-0415

67Index score

Recomputed live from the record · Oct 13, 2026, 8:42 AM