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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 sourced gains and 770 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.

933 gains
770 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.

1,703 results
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AI gains · 933

189
BusinessStableModerate evidence · 1 source

Manufacturing SMEs in Sweden structure, bundle, and leverage AI resources to transform key business and production operations and create competitive advantage.

Published April 3 2024, this peer-reviewed study investigated AI implementation in manufacturing SMEs in Sweden across packaging, plastic, and metal sectors. It found SMEs build an AI resource portfolio through acquiring and accumulating resources, bundle them into learning and governance capabilities, and leverage them in production.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0444

72Index score
190
ScienceStableModerate evidence · 1 source

AI-driven enzyme engineering enables rapid, precise design of synthetic synzymes that catalyze non-natural reactions for use in pharmaceuticals, biofuels, and environmental remediation.

As of December 2025, researchers synthesized AI methods for enzyme engineering, using structure-prediction, generative, and reinforcement learning models combined with high-throughput screening to design and optimize enzymes, including synthetic synzymes for non-natural reactions.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0435

72Index score
191
BusinessStableModerate evidence · 1 source

Generative AI can promote Industry 5.0 sustainability objectives in manufacturing through ten functions that provide data-driven production insights and enhance operational resilience.

In a peer-reviewed study published 2024-05-26, researchers examined generative AI in manufacturing to actualize Industry 5.0 sustainability goals. Using case studies, interviews and interpretive structural modeling, they developed a strategic roadmap identifying ten distinct functions through which generative AI can support responsible manufacturing, from data-driven production insights to resilience of operations.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0431

72Index score
192
Media & ArtsStableModerate evidence · 1 source

Generative AI can lower barriers to entry for music creation, expanding who can participate in making music.

Published December 5, 2025, this peer-reviewed study investigated how AI and Generative AI affect music streaming. Using two focus groups with users and with artists/performers, it explored perceptions of AI-generated music for listening and for artists' position and opportunities within the streaming model.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0410

72Index score

AI problems · 770

189
HealthStableModerate evidence · 1 source

Identification of key molecular targets in nicotine-induced spontaneous abortion through network toxicology and multi-omics integration: Introduction Spontaneous abortion (SA) remains a prevalent reproductive health challenge, with tobacco-derived nicotine emerging as a significant risk factor.

Introduction Spontaneous abortion (SA) remains a prevalent reproductive health challenge, with tobacco-derived nicotine emerging as a significant risk factor. This study sought to decipher the molecular underpinnings of nicotine-induced pregnancy loss through comprehensive multi-omics profiling to identify novel biomarkers and potential intervention targets.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 26, 2026 · TRV-2026-1190

68Index score
190
HealthStableModerate evidence · 1 source

Rapid serum-based differentiation of ovarian tumors and assessment of post-treatment disease-free status using portable Raman spectroscopy and machine learning: Rapid, low-cost tools for ovarian tumor discrimination and post-treatment assessment remain limited because conventional serum biomarkers such as CA125 have suboptimal sensitivity and specificity.

Rapid, low-cost tools for ovarian tumor discrimination and post-treatment assessment remain limited because conventional serum biomarkers such as CA125 have suboptimal sensitivity and specificity. Serum contains proteins, lipids, carbohydrates, and nucleic acids that generate disease-related spectrochemical fingerprints, which portable Raman spectroscopy can capture with minimal sample preparation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 26, 2026 · TRV-2026-1189

68Index score
191
HealthStableModerate evidence · 1 source

The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.

BackgroundThe safe and efficient transfer of patients to the operating room is a critical component of surgical care. The growing integration of artificial intelligence (AI) in healthcare introduces new possibilities for enhancing clinical decision-making.ObjectiveThis study compared the performance of AI and perioperative nurses with varying experience levels in identifying errors during the preoperative patient transfer process.MethodsA controlled simulation study was conducted involving three nurses (novice, intermediate, and expert) and a ChatGPT-4o AI model.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
97

Updated Sep 26, 2026 · TRV-2026-1186

68Index score
192
HealthStableModerate evidence · 1 source

While agentic AI raises familiar moral concerns regarding safety, accountability and bias, this article focuses on a less explored dimension: its capacity to transform the moral fabric of healthcare itself.

Agentic artificial intelligence (AI) is an emerging development in the digital transformation of healthcare. Unlike conventional generative AI, agentic AI systems can perform autonomous, goal-directed actions and coordinate complex tasks.

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

Updated Sep 23, 2026 · TRV-2026-1178

68Index score

Recomputed live from the record · Oct 11, 2026, 11:41 PM