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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

137
HealthStableModerate evidence · 1 source

Solvent extraction of urine analyzed by GC-MS and XGBoost distinguished bladder cancer patients from controls with AUROC 0.869 and 85% balanced sensitivity and specificity using an 8-metabolite panel.

On 2026-08-07, researchers reported a urine-based test for urothelial bladder cancer that combines solvent extraction, GC-MS profiling, and machine learning. In 100 participants, an XGBoost model using an 8-metabolite panel achieved AUROC 0.869, improving on classical statistics at 0.752, with 85% balanced sensitivity and specificity.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 10, 2026 · TRV-2026-0728

73Index score
138
HealthRisingModerate evidence · 1 source

AI-based machine learning applied to cholangioscopy images achieved high pooled diagnostic performance for indeterminate and malignant biliary strictures, with 95% sensitivity and 88% specificity.

By August 2026, researchers published a systematic review and meta-analysis of AI combined with digital cholangioscopy for indeterminate and malignant biliary strictures. The analysis pooled five studies totaling 675 lesions and 2,685,674 images, finding pooled sensitivity of 95%, specificity of 88%, and SROC accuracy of 97% for AI-assisted diagnosis.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0679

73Index score
139
HealthStableModerate evidence · 1 source

A Bi-LSTM and CNN fusion model combining text and visual data achieved 91.3% precision and 90.1% F-score on emotion recognition and up to 85.32% accuracy on depression classification, outperforming single-modal baselines.

A peer-reviewed study published August 6, 2026 proposes a deep learning framework for dynamic mental health assessment that fuses text and visual modalities. The model uses Bi-LSTM for text and CNN for images, trained on a jointly annotated dataset labeled with self-assessment questionnaires and expert annotations.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0678

73Index score
140
HealthStableModerate evidence · 1 source

A microorganism-based random forest model built from plasma metagenomic profiles predicted subsequent infection in newly diagnosed hematological patients with AUC 0.942, identifying 99.1% of those who later developed infections, and improved to AUC 0.953 when combined with clinical metrics, supporting targeted prophyl-

In a prospective study registered as ChiCTR2100042992, investigators collected plasma for metagenomic next-generation sequencing from 230 newly diagnosed hematological patients before and after chemotherapy and used machine learning to map a complex microecological landscape linked to neutropenia and subsequent infection.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 7, 2026 · TRV-2026-0672

73Index score

AI problems · 770

137
CrimeStableHigh evidence · 5 sources

Cybercriminals use AI to create sophisticated attack tools including advanced phishing, deepfakes and hard-to-detect malware.

By April 2026, a peer-reviewed analysis described AI's growing dual role in cyberspace, where it automates anomaly detection, data analysis and incident response to enhance protection, while also enabling cybercriminals to build advanced phishing, deepfakes and hard-to-detect malware.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0199

70Index score
138
PolicyStableHigh evidence · 3 sources

In low- and middle-income countries, AI for healthcare faces systemic barriers including contextual bias from non-representative datasets and low governance and workforce readiness.

Published April 13, 2026, this scoping review mapped literature on AI in healthcare in low- and middle-income countries, screening sources from 2000-2025 and including 60 studies that addressed ethical, regulatory, or implementation issues.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0194

70Index score
139
PolicyStableHigh evidence · 4 sources

Current discussion of AI in higher education focuses on adoption and efficiency with insufficient attention to interpretive and governance conditions needed for responsible institutional use

As of May 2026, a peer-reviewed study examined AI adoption in higher education, noting its growing use to support teaching, learning, administration, quality assurance, and institutional planning. Based on interviews with 16 key informants, a focus group with 9 additional participants, and document analysis, the authors identified themes including AI as an institutional governance project and as a system-shaping force.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0189

70Index score
140
ScienceStableHigh evidence · 2 sources

Local journalists in Germany do not fully leverage AI to support data-related reporting work, linked to limited awareness of what AI can do.

By April 13 2026, researchers reported results from 21 semi-structured interviews with local journalists in Germany examining use of data and AI, challenges in interaction, and perceived opportunities for AI-supported reporting systems.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
99
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0156

70Index score

Recomputed live from the record · Oct 11, 2026, 5:17 PM