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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 929 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.

929 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.

1,699 results
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AI gains · 929

53
HealthStableModerate evidence · 1 source

AI analysis of electronic health records, medical imaging and genomic data can reduce clinical errors, optimize resources and improve patient outcomes while expanding access in low-resource settings.

Published September 23 2025 as a peer-reviewed review, the article surveys how AI is being applied across healthcare, from analyzing electronic health records and medical imaging to supporting drug discovery, predictive analytics, telemedicine and wearable biosensors, with emphasis on low-resource and remote settings.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0484

77Index score
54
CrimeStableModerate evidence · 1 source

Review synthesizes evidence that supervised, unsupervised and hybrid machine learning approaches can be applied to detect credit card fraud, financial statement fraud, insurance fraud and money laundering in real-world banking data.

On 2025-11-05, Applied Sciences published a comprehensive review of machine learning for financial fraud detection. The authors surveyed supervised, unsupervised and hybrid approaches across credit card, financial statement, insurance and money laundering fraud, reviewed datasets and metrics, and included two case studies applying supervised models to real-world banking data.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0477

77Index score
55
HealthStableModerate evidence · 1 source

Integrating multi-omics with AI-enabled imaging and digital tools improves risk prediction and informs clinical decision-making across interconnected cardiovascular conditions.

On 2026-01-13, a peer-reviewed integrative review in Diseases synthesized 2015-2025 literature on cardiovascular diseases as an interconnected continuum, examining how multi-omics data combined with AI-enabled imaging and digital tools are applied across seven major condition clusters.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0472

77Index score
56
ScienceStableModerate evidence · 1 source

Self-optimizing attention-coupled neural network potential automates crystal structure prediction and iteratively refines itself, enabling exploration of nearly 10 million configurations with ab initio accuracy and substantial speedup over first-principles calculations.

Researchers reported a self-optimizing automated workflow for materials design that couples crystal structure prediction with an attention-coupled neural network interatomic potential. The system samples local minima of the potential energy surface and iteratively refines itself to improve generalization to unknown structures while reducing manual intervention.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0407

77Index score

AI problems · 770

53
HealthStableModerate evidence · 1 source

AI integration in surgery risks liability gaps from diluted authority chains and bias that exacerbates health inequalities, compounded by concentration of research in resource-rich nations.

This peer-reviewed analysis from May 2026 examines how AI and robotics ecosystems are entering the operating room, using multimodal data from patients, staff, robots and the environment for workflow recognition, performance benchmarking and decision support, while robots evolve toward autonomous systems with human-in-the-loop control.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0191

77Index score
54
LifestyleStableModerate evidence · 1 source

Higher social-interaction burnout and subjective loneliness predict stronger emotional attachment to AI companions among young adults, with parasocial interaction mediating the relationship.

A June 2026 peer-reviewed survey of 1,200 young adults in Palembang, Indonesia examined why socially active youth turn to large-language-model AI companions. Using validated scales and mediation-moderation analysis, it found burnout, loneliness, and parasocial interaction strongly predicted emotional attachment to AI, with judgment apprehension amplifying the loneliness effect.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0164

77Index score
55
HealthStableHigh evidence · 5 sources

Digital mental health tools are hampered by engagement challenges, industry setbacks, methodological critiques, and gaps in evidence and scaling that limit real-world applicability.

As of May 2025, this review in World Psychiatry examined how smartphone apps, virtual reality, and generative AI including large language models are being applied to mental health, evaluating evidence across well-being, depression, anxiety, schizophrenia, eating disorders and substance use, and outlining advances in digital phenotyping and generative outputs.

Impact 30%
49
Evidence 25%
100
Scale 20%
60
Confidence 15%
100
Recency 10%
85

Updated Jul 24, 2026 · TRV-2026-0523

75Index score
56
SportsStableHigh evidence · 5 sources

Rapid adoption of AI in sports raises complex legal challenges involving data protection, intellectual property, liability, and ethics that current frameworks may not adequately address.

A peer-reviewed article from April 2026 examines how artificial intelligence is being used in the sports industry for performance analysis, fan engagement, and decision-making, and analyzes the legal foundations governing that use.

Impact 30%
49
Evidence 25%
100
Scale 20%
60
Confidence 15%
100
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0238

75Index score

Recomputed live from the record · Oct 11, 2026, 6:12 AM