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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.

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AI gains · 935

361
HealthStableModerate evidence · 1 source

Across 17 studies totaling 243,324 trauma patients, the best-performing ML model showed a small pooled AUC advantage over logistic regression for mortality prediction.

A systematic review and meta-research appraisal examined 20 studies comparing machine learning and logistic regression for trauma mortality prediction, with 17 studies (243,324 patients) in primary synthesis. The pooled within-study AUC difference favoring the best ML model was 0.026 (95% CI 0.009-0.043), 0.017 in co-primary analysis of studies reporting CIs, with extreme heterogeneity and a prediction interval crossing zero.

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

Updated Sep 13, 2026 · TRV-2026-1068

68Index score
362
HealthStableModerate evidence · 1 source

Deep learning models showed promising and often strong performance for predicting conversion from mild cognitive impairment to Alzheimer's disease, supporting early diagnosis and timely therapeutic intervention.

This PRISMA-guided systematic review examined 60 studies published between 2019 and February 2026 that used deep learning to classify Alzheimer's stages and predict conversion from mild cognitive impairment to Alzheimer's disease. It found cross-sectional designs predominant, CNNs dominant for neuroimaging, and growing use of RNNs and transformers for longitudinal data, with multimodal approaches in 24 studies.

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

Updated Sep 13, 2026 · TRV-2026-1067

68Index score
363
ClimateStableModerate evidence · 1 source

Physics-informed categorical chain differential model improved coupled prediction of four ash fusion temperatures, reducing deformation temperature error and eliminating physically impossible temperature inversions to support boiler safety and slagging-risk management.

Researchers developed a Categorical Chain Differential framework that couples prediction of four ash fusion temperatures across heterogeneous solid fuels. Using a regressor chain with fuel category information and non-negative constraints on inter-stage differences, the model enforces the physical order DT ≤ ST ≤ HT ≤ FT.

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

Updated Sep 13, 2026 · TRV-2026-1065

68Index score
364
HealthStableModerate evidence · 1 source

Digital devices, social media and AI tools have brought convenience and connectivity and can have positive impacts on cognitive functions including attention and memory.

A Frontiers in Cognition review published in November 2023 surveyed how digital devices, social media platforms, and AI tools affect human cognition. It described these technologies as integral to daily life, bringing convenience and efficiency, while also examining their positive and negative influences on attention, memory, addiction, perception, decision-making, critical thinking and learning across different age groups.

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

Updated Sep 10, 2026 · TRV-2026-1055

68Index score

AI problems · 771

361
HealthStableModerate evidence · 1 source

Copilot and Claude produced the least accurate reports for jaw lesions, highlighting significant discrepancies in diagnostic accuracy across chatbots.

A cross-sectional study tested four AI chatbots on 97 anonymized CBCT cases of jaw lesions, comparing performance on reconstructed 2D panoramic views and, for Manus, raw 3D DICOM data. Reports were scored for accuracy, relevance and feasibility, revealing statistically significant differences between systems.

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

Updated Aug 5, 2026 · TRV-2026-0653

67Index score
362
HealthStableModerate evidence · 1 source

AI assistance increased cumulative time-to-decision for ECG interpretation, adding an average of 14.7 seconds per ECG strip.

In a prospective randomized crossover study at Carl R. Darnall Army Medical Center, 21 certified physician assistants interpreted 50 de-identified 12-lead ECGs with and without Queen of Hearts AI software by PMcardio. Diagnostic accuracy rose from 79.0% to 92.9% with AI, with sensitivity 95.4% versus 82.5% and specificity 90.5% versus 75.6%, and interrater agreement improved from kappa 0.58 to 0.86.

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

Updated Aug 5, 2026 · TRV-2026-0651

67Index score
363
HealthStableModerate evidence · 1 source

Without governance, AI risks deepening existing rural mental health inequity for regional, rural and remote Australians who already experience poorer outcomes and higher suicide and self-harm rates.

Published 4 August 2026 in Internal Medicine Journal, this peer-reviewed perspective examines rural mental health inequity in Australia and argues AI could help with earlier identification of distress and safer, more timely triage when used with telehealth and clinical decision support.

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

Updated Aug 5, 2026 · TRV-2026-0649

67Index score
364
PolicyStableModerate evidence · 1 source

When AI is embedded in labour market and migration governance infrastructure, continuous classification, worker scoring and automated risk assessment can amplify structural inequalities while human oversight becomes procedural under scale and speed.

Published August 4 2026 in WORK, this peer-reviewed analysis examines AI integration into labour markets, migration governance and social protection systems. It argues AI functions as institutional infrastructure and shows how continuous classification, automated risk assessment and worker scoring can amplify structural inequalities when deployed at scale.

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

Updated Aug 5, 2026 · TRV-2026-0648

67Index score

Recomputed live from the record · Oct 12, 2026, 11:24 PM