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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

585
HealthStableModerate evidence · 1 source

Deep learning models using orbital MRI predicted continuous clinical activity score with low error and inferred patient characteristics including age, sex, and smoking status.

Researchers developed and evaluated ResNet-50 models on the TOM500 orbital MRI dataset to predict continuous clinical activity score and patient characteristics in thyroid eye disease, training on 360 cases, validating on 100, and testing on 40.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
85

Updated Jul 30, 2026 · TRV-2026-0592

67Index score
586
HealthStableModerate evidence · 1 source

Machine learning models trained on TriNetX data stratified patients receiving immune checkpoint inhibitors into risk tiers for cardiac immune-related adverse events within 90 days, achieving moderate discrimination.

Researchers built and tested machine learning models to predict cardiac immune-related adverse events shortly after starting immune checkpoint inhibitor therapy. Using records for 61,117 patients from TriNetX from 2010 to 2023, they defined events by diagnosis codes within 90 days plus hospital visits and compared elastic net logistic regression, gradient boosted trees, and random forest models.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
85

Updated Jul 30, 2026 · TRV-2026-0591

67Index score
587
HealthStableModerate evidence · 1 source

A LightGBM model trained on metabolomic data predicted incident major depressive disorder among obese participants with AUCs around 0.82-0.84 over 3 to 9 years and outperformed existing clinical models.

Researchers developed metabolomics-based machine learning models to stratify future major depressive disorder risk among 41,459 obese participants followed for a median of 14.4 years. The optimized LightGBM model achieved AUCs of 0.844, 0.824 and 0.834 for 3-, 5- and 9-year predictions and outperformed existing clinical models, with temporal validation showing AUCs of 0.738-0.776.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
85

Updated Jul 30, 2026 · TRV-2026-0590

67Index score
588
HealthStableModerate evidence · 1 source

Advances in AI have enabled opportunistic identification of osteoporosis and other pathologies on imaging performed for other indications.

This opinion article examines the concept of clinical justification for osteoporosis imaging, noting that advances in imaging and AI now permit opportunistic identification of osteoporosis and other conditions beyond the original scan purpose, and discusses how these opportunistic tools might eventually become standalone justified diagnostic pathways.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
85

Updated Jul 30, 2026 · TRV-2026-0589

67Index score

AI problems · 772

585
ClimateStableModerate evidence · 1 source

Expanding PFAS production to meet AI datacentre and semiconductor demand risks creating a new tidal wave of persistent forever chemicals that never degrade and undermines emerging restrictions.

On 14 September 2026 The Guardian reported a ChemSec survey of the 10 biggest PFAS manufacturers finding nearly all plan to increase production at sites in Europe, Asia and North America to meet AI industry demand. Companies frame investments around the AI revolution, citing PFAS use in semiconductor fabrication and in two-phase immersion cooling where servers are immersed in low-boiling-point PFAS fluid.

Impact 30%
49
Evidence 25%
62
Scale 20%
60
Confidence 15%
62
Recency 10%
94

Updated Sep 14, 2026 · TRV-2026-1074

61Index score
586
Media & ArtsStableModerate evidence · 1 source

AI-enabled piracy and deepfakes pose legal and economic risks to Kerala's film and music industry, prompting calls for stronger copyright laws.

On September 12, 2026, India Today reported that at a Kerala seminar on music, cinema and law, V D Satheesan called for stronger copyright laws to protect the film industry from piracy and AI threats.

Impact 30%
49
Evidence 25%
62
Scale 20%
60
Confidence 15%
62
Recency 10%
94

Updated Sep 13, 2026 · TRV-2026-1064

61Index score
587
Media & ArtsStableModerate evidence · 1 source

Songwriters and musicians face use of their work to help power AI music systems without required compensation, prompting concerns and a push for rules mandating pay

On 2026-08-28, the source reported concerns as the music industry pushes for rules that would require AI companies to compensate songwriters and musicians whose work helps power their systems.

Impact 30%
49
Evidence 25%
62
Scale 20%
60
Confidence 15%
62
Recency 10%
91

Updated Aug 29, 2026 · TRV-2026-0922

61Index score
588
Media & ArtsStableModerate evidence · 1 source

AI-generated tracks are excluded from Australia's official music charts under new rules requiring human songwriting and performance of lead vocal and primary instruments.

On August 25, 2026, Australia's music industry implemented a ban on AI-generated tracks from its official charts, establishing that tracks can be eligible only if humans wrote the song and performed the lead vocal and primary instruments, among other requirements.

Impact 30%
49
Evidence 25%
62
Scale 20%
60
Confidence 15%
62
Recency 10%
91

Updated Aug 26, 2026 · TRV-2026-0885

61Index score

Recomputed live from the record · Oct 13, 2026, 11:37 PM