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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

229
EducationStableHigh evidence · 5 sources

Generative AI tools improved personalized learning, self-regulated learning strategies, and adaptive feedback in higher education settings.

This March 2026 systematic and thematic review examined generative AI tools such as ChatGPT in higher education, analyzing 46 Web of Science documents and qualitatively synthesizing 27 peer-reviewed articles to map implementation trends.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0342

70Index score
230
ClimateStableHigh evidence · 5 sources

AI enables real-time pollution detection, air and water quality monitoring, and accurate disaster and air quality forecasting to support prompt interventions and sustainable urban management.

Published October 18, 2024, this peer-reviewed overview describes how AI is used in environmental monitoring to provide accurate disaster forecasts, detect pollution sources, and monitor air and water quality in real time, including applications in smart cities.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0341

70Index score
231
HealthStableHigh evidence · 3 sources

Clinicians using ambient listening generative AI digital scribes reported decreased documentation time and improved engagement in real-world academic and outpatient settings by April 2025.

A rapid review published April 29 2025 synthesized 6 real-world studies of digital scribes using ambient listening and generative AI from 1450 screened records spanning academic health systems, community settings, and outpatient practices. Across observational, case report, cohort, and survey designs, authors reported decreased self-reported documentation times with associated increased length of notes.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0330

70Index score
232
HealthStableHigh evidence · 2 sources

In a 20-question TB Q&A test generating 100 responses, GPT-5 produced the most suitable patient-education texts as measured by C-PEMAT-P among five LLMs.

From October 5 to 11, 2025, researchers tested five large language models on 20 pulmonary tuberculosis questions spanning five themes, generating 100 responses and rating them with C-PEMAT-P, GQS, and seven readability measures. GPT-5 ranked highest on C-PEMAT-P followed by Doubao, GQS was similar across models, and models differed significantly on several readability indices.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
99
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0326

70Index score

AI problems · 770

229
HealthStableModerate evidence · 1 source

Deep learning models for MCI-to-AD conversion face substantial barriers to routine clinical use due to heavy reliance on ADNI, lack of diverse multicenter data, overfitting, and poor interpretability.

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
230
HealthStableModerate evidence · 1 source

Digital devices, social media and AI tools influence brain function and cognitive abilities, with potential negative impacts on attention, memory and related functions.

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
231
HealthStableModerate evidence · 1 source

Models were validated only at sample level without patient or centre grouping, and exploratory risk strata were not evaluated for clinical utility or safety, so they do not establish symptomatic UTI or safe antibiotic decisions.

Researchers developed and internally validated machine-learning models to estimate the probability of urine-culture positivity using routinely collected urinalysis data from 2530 sample records across three university hospitals. Using a stratified 75:25 sample-level split, 13 supervised algorithms were tested, with CatBoost showing the highest test-set AUC of 0.858 (95% CI 0.829-0.892) and similar performance to other gradient-boosting models.

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

Updated Sep 10, 2026 · TRV-2026-1053

68Index score
232
ScienceStableModerate evidence · 1 source

AI models for craniofacial soft tissue prediction lack independent external validation, leaving generalizability beyond the study dataset unestablished.

By September 2026, researchers had built an AI-assisted framework using CT scans from 1972 individuals in northwestern India to jointly characterize craniofacial soft tissue thickness and subcutaneous fat thickness at 73 landmarks, analyzing sexual dimorphism, age variation, and bilateral symmetry.

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

Updated Sep 10, 2026 · TRV-2026-1051

68Index score

Recomputed live from the record · Oct 12, 2026, 5:21 AM