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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.

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AI gains · 933

205
BusinessStableModerate evidence · 1 source

AI automation of tasks is estimated to produce modest aggregate gains of no more than 0.66% TFP growth over 10 years based on task exposure and task-level cost savings.

This peer-reviewed paper models AI's macroeconomic impact as task-level automation and complementarity, using Hulten's theorem to translate the fraction of tasks impacted and average cost savings into GDP and TFP effects. Using existing exposure estimates, it calculates no more than a 0.66% TFP increase over 10 years, then revises down to less than 0.53% after accounting for the shift from easy-to-learn to hard-to-learn tasks.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0378

71Index score
206
HealthStableModerate evidence · 1 source

In a 308-person online survey of mostly urban women with high HPV history, 73.7% said they trusted an AI preliminary screening plus physician confirmation workflow for cervical screening, with 76% believing AI could shorten result waiting times.

On July 10 2026, a peer-reviewed cross-sectional study reported results from 308 online questionnaire responses about cervical HPV screening experiences. Most respondents were urban women aged 25-35, 76.30% reported a history of HPV infection, and 91.56% had undergone TCT. The study measured current distress points and attitudes toward AI-assisted diagnosis.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0329

71Index score
207
HealthStableModerate evidence · 1 source

In two Canadian diagnostic laboratories, AI-based PhenoMATRIX urine culture assessment enabled earlier availability of interpretable results and reduced time to result reporting by about 1.3 hours with automated PM+ release at a tertiary hospital and about 5.3 hours with earlier manual screening at a community lab.

By July 10 2026, a dual-center Canadian study reported before-and-after results for PhenoMATRIX, an AI-based software that provides continuous culture sorting and interpretation support for urine cultures on laboratory automation. Both a low-volume tertiary hospital and a high-volume community lab saw earlier availability of interpretable results, with measured TTRR changes of approximately 1.3 hours with PM+ automated release and approximately 5.3 hours with earlier manual review.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0322

71Index score
208
HealthStableModerate evidence · 1 source

Custom GPT models completed all QUIPS domain judgments and reduced data-extraction time from 30.4 to 5.7 minutes per study in rheumatology systematic reviews.

Researchers nested a two-part methodological study within two PROSPERO-registered reviews to test customized GPT models on complex rheumatology evidence synthesis. Fifteen SLE metabolomics studies were used to compare human and GPT data extraction, and nineteen rheumatology prognostic studies were reappraised in 2025 with GPT-Reviewer against adjudicated human QUIPS ratings using weighted kappa.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0308

71Index score

AI problems · 770

205
HealthStableModerate evidence · 1 source

Artificial Intelligence in Extended Reality and Digital Rehabilitation Technologies for Fall Prevention in Older Adults: A Rapid Review of the Evidence: BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dependence and healthcare losses.

BackgroundFall-related injuries in older adults remain a major health concern, contributing to functional dependence and healthcare losses. Traditional prevention programs, while effective, remain underutilized due to poor adherence and participation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1144

68Index score
206
ClimateStableModerate evidence · 1 source

Gene-environment interactions shape cytokine-mediated inflammation and cardiovascular risk: An exposome-wide association study identified environmental determinants of inflammation, and gene-environment interactions were assessed using multi-ancestry polygenic risk scores.

Background Chronic inflammation is a major driver of cardiovascular diseases, but mechanisms linking systemic inflammation to cardiac remodelling remain incompletely understood. We aimed to evaluate the role of cytokine-mediated signalling and the influence of environmental and genetic factors on inflammation and cardiac phenotypes in a large population-based cohort.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1143

68Index score
207
HealthStableModerate evidence · 1 source

This study aimed to develop and externally validate a machine learning-based prognostic model for predicting acute coronary syndrome risk at multiple time points in patients with heart failure with preserved ejection fraction and obstructive sleep apnea.

Background Heart failure with preserved ejection fraction is a heterogeneous syndrome, and comorbid obstructive sleep apnea further increases the risk of acute coronary syndrome. However, effective tools for long-term acute coronary syndrome risk stratification in this population remain limited.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1139

68Index score
208
HealthStableModerate evidence · 1 source

This narrative review maps this evolving convergence of AI and endohepatology across four functional pillars: intelligent hemodynamic assessment, virtual histology, precision tissue acquisition, and integrated risk stratification with therapeutic and decision support.

Endohepatology (the implementation of contemporary advanced endoscopy in hepatology) has reached a point where endoscopic ultrasound (EUS)-guided liver biopsy, portal pressure gradient measurement, parenchymal elastography, and variceal screening and therapy can be integrated into a single procedural session of liver-directed endoscopy. Concurrently, artificial intelligence (AI) has revolutionized luminal endoscopy and is advancing rapidly across hepatology imaging, digital pathology, and outcome prediction, yet its translation into the liver-targeted endoscopic workflow has never been synthesized into a coherent domain.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1138

68Index score

Recomputed live from the record · Oct 12, 2026, 2:47 AM