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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

129
ScienceStableModerate evidence · 1 source

Logistic regression screening model using routine indicators like total protein and hemoglobin achieved AUC 0.843 and provides an accessible tool for early identification of individuals at high risk of M-protein in resource-limited primary care.

Researchers developed and validated an M-protein screening model using routine laboratory indicators from 5217 participants across three Chinese hospitals. They compared eight machine learning algorithms and selected a logistic regression model incorporating sex, age, total protein, albumin, albumin/globulin ratio, and hemoglobin, achieving an AUC of 0.843 in training and 0.843, 0.801, and 0.800 in internal and two external validations with a five-tier risk stratification.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
92

Updated Aug 29, 2026 · TRV-2026-0924

73Index score
130
HealthStableModerate evidence · 1 source

A hybrid attention-enhanced segmentation plus morphological feature classification framework classified OA progression stages at 18-month and 30-month follow-ups from OAI longitudinal knee MRI, achieving 86.67% accuracy with Random Forest to support timely treatment decisions.

Researchers developed a two-phase hybrid framework for osteoarthritis staging using longitudinal knee MRI from the OAI dataset. They segmented cartilage with an attention-enhanced position-aware encoder-decoder network, then extracted and statistically selected morphological shape features to classify progression at 18-month and 30-month follow-ups with machine learning classifiers.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0804

73Index score
131
HealthStableModerate evidence · 1 source

A DWT-HVG framework with Soft Voting Ensemble enabled automated classification of resting-state EEG for ASD screening with 93.54% accuracy and 98.17% AUC in stratified 10-fold cross-validation.

On 2026-08-15, a peer-reviewed study described a dual-domain computational framework for automated ASD detection from resting-state EEG, combining time-frequency analysis with Horizontal Visibility Graph modelling and machine learning classification.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0801

73Index score
132
HealthStableModerate evidence · 1 source

An AI system using modified U-Net segmentation automatically classified inferior alveolar nerve proximity to impacted mandibular third molars on CBCT with 90.1% accuracy and reduced analysis time from ~189 seconds to ~4.8 seconds compared to expert radiologists.

Researchers retrospectively tested a deep learning system that automatically segments the inferior alveolar nerve canal and impacted mandibular third molars on CBCT and classifies their spatial relationship. On an independent hold-out set of 486 sites, the system reached 90.1% overall accuracy and 0.925 weighted AUC against two senior radiologists, with processing time of 4.75 seconds versus 189.12 seconds for experts.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0798

73Index score

AI problems · 770

129
EducationStableHigh evidence · 5 sources

Generative AI adoption in higher education created persistent risks to academic integrity, data privacy, equity, and responsible governance.

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
130
HealthStableHigh evidence · 3 sources

Use of the same ambient AI scribes was associated with increased length of notes and no change in physician productivity measured by billing metrics.

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
131
HealthStableHigh evidence · 2 sources

The same five LLMs showed significant differences on several readability indices for TB education texts, creating uneven reading difficulty that may undermine patient understanding and adherence.

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
132
HealthStableHigh evidence · 3 sources

Opaque AI models embedded in clinical trial infrastructure risk amplifying existing disparities in the evidence base by shaping who is identified and analyzed.

As of the July 2026 publication date, the authors describe AI being embedded in clinical trial infrastructure and argue that opaque models risk amplifying disparities. They propose embedded transparency as a structural prerequisite for equitable trials and outline governance recommendations.

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

Updated Jul 20, 2026 · TRV-2026-0313

70Index score

Recomputed live from the record · Oct 11, 2026, 4:06 PM