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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 sourced gains and 771 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.

935 gains
771 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.

1,706 results
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AI gains · 935

365
ClimateStableModerate evidence · 1 source

Reinforcement learning provides intelligent, scalable and adaptive control for electric vehicle charging station operations to address grid constraints, renewable integration and dynamic pricing.

Published October 1, 2025 as a peer-reviewed review in Energies, the paper surveys reinforcement learning for electric vehicle charging station management. It describes growing operational complexity driven by grid constraints, renewable integration, user variability and dynamic pricing, and maps how RL methods have been applied across charging contexts.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1054

68Index score
366
HealthStableModerate evidence · 1 source

Supervised ML models, especially ensemble methods, predicted all-cause mortality in adult infective endocarditis with pooled AUC 0.85 for both in-hospital/early and 6-month mortality, outperforming conventional scores.

A PRISMA-compliant systematic review and meta-analysis of 8 studies with 5503 adult patients evaluated supervised machine learning models to predict all-cause mortality in infective endocarditis, a condition described as often fatal despite surgical and antibiotic advances. Five studies were pooled, showing strong discrimination for both in-hospital/early and 6-month mortality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1049

68Index score
367
HealthStableModerate evidence · 1 source

In systemic sclerosis-associated ILD, AI-based HRCT quantification stratifies FVC decline and long-term survival and correlates with lung function measures to predict mortality.

This peer-reviewed review summarizes recent AI and radiomics work in systemic sclerosis-associated ILD, the leading cause of disease-related mortality in systemic sclerosis, where visual HRCT scoring is reader-dependent. It reports that deep-learning UIP probability and whole-chest quantitative biomarkers stratify FVC decline and survival, and that AI-derived HRCT parameters correlate with DLCO/TLC and support pattern classification across CTD-ILD subtypes.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1048

68Index score
368
HealthStableModerate evidence · 1 source

Participants with schizophrenia recognized an AI companion as a potentially accessible source of support between clinical visits.

Researchers conducted exploratory focus groups at a large academic health center in New York City with 17 patients with schizophrenia spectrum disorders, 7 caregivers, and 4 providers to discuss a hypothetical AI companion tool. Thematic analysis identified five themes about accessibility, uncertainty about role, trust, supplementing human care, and limiting use to low-risk support.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1046

68Index score

AI problems · 771

365
HealthStableModerate evidence · 1 source

AI integration may reinforce health inequities for marginalised populations in Sub-Saharan Africa due to infrastructure gaps, algorithmic bias, under-representation of African datasets, and weak governance.

This scoping review mapped evidence published up to March 2026 on AI in healthcare in Sub-Saharan Africa, focusing on marginalised populations. Searching four databases and grey literature, the authors included 23 sources and synthesised them thematically.

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

Updated Aug 4, 2026 · TRV-2026-0645

67Index score
366
HealthStableModerate evidence · 1 source

AI-enabled sleep estimation tools raise concerns about racial bias in pulse oximetry, regulatory gaps, variable accuracy, and privacy, requiring further validation to ensure equitable and reliable clinical use.

By August 2026, peer-reviewed guidance for neurologists described a shift in obstructive sleep apnea diagnosis from in-laboratory polysomnography alone to home testing augmented by wearables, nearables, and FDA-cleared software-as-a-medical-device platforms that leverage artificial intelligence and multisignal integration to estimate sleep parameters. The article framed these tools as improving accessibility for patients unable or unwilling to undergo lab studies.

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

Updated Aug 4, 2026 · TRV-2026-0644

67Index score
367
ClimateStableModerate evidence · 1 source

The same presence-prediction model failed to identify 2 of 5 APP-contaminated field sites and could not predict particle concentration with sufficient accuracy.

On 2026-08-03, a peer-reviewed study reported a random forest model trained on 16S rRNA gene sequencing from a field mesocosm to predict antifouling paint particle contamination in sediment. In lab incubation tests it identified 100% of presence samples and 83.3% of absence samples, and when applied to 14 Baltic Sea and Warnow estuary sites it correctly labeled all uncontaminated sites and three of five contaminated sites.

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

Updated Aug 4, 2026 · TRV-2026-0643

67Index score
368
HealthStableModerate evidence · 1 source

In prostate cancer and MRI subgroups, AI models showed lower sensitivity than clinicians, indicating inconsistent advantage across tasks.

A meta-analysis of 110 studies up to June 2026 evaluated AI algorithms for diagnosing urological cancers on CT, MRI, and ultrasound, pooling sensitivity, specificity, and AUC and comparing to clinician performance where reported.

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

Updated Aug 4, 2026 · TRV-2026-0640

67Index score

Recomputed live from the record · Oct 12, 2026, 11:46 PM