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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 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.

939 gains
772 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.

1,711 results
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AI gains · 939

537
HealthStableModerate evidence · 1 source

Machine learning models achieved moderate diagnostic accuracy for predicting clinical pregnancy or live birth after assisted reproductive technology, with pooled sensitivity 0.737 and specificity 0.789.

A systematic review and diagnostic meta-analysis of 20 studies, 14 in quantitative synthesis, evaluated machine learning models to predict clinical pregnancy or live birth after assisted reproductive technology. As of the August 2026 publication, pooled sensitivity was 0.737 and specificity 0.789 with a DOR of 10.49 and acceptable discrimination on SROC, but heterogeneity was very high.

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

Updated Aug 7, 2026 · TRV-2026-0674

67Index score
538
HealthStableModerate evidence · 1 source

In a 34-case glaucoma reasoning test, LLM systems produced structured reasoning with weighted scores overlapping attending ophthalmologists and often included safety-critical diagnostic and management elements.

Researchers compared large language models and clinicians on 34 real-world glaucoma cases, with glaucoma specialists scoring responses on medical accuracy, key-point recall, and logical completeness. AI models produced structured reasoning with weighted mean scores overlapping attending ophthalmologists and exceeding some residents.

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

Updated Aug 7, 2026 · TRV-2026-0673

67Index score
539
HealthStableModerate evidence · 1 source

Large language models such as ChatGPT can provide personalized learning experiences when integrated into medical education.

Published October 22, 2025, this PLOS One scoping review examined literature on AI and large language models like ChatGPT in medical education. It found potential for personalized learning alongside a set of ethical challenges, synthesizing 50 studies from three major databases covering 2010 to August 2024.

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

Updated Aug 6, 2026 · TRV-2026-0670

67Index score
540
HealthStableModerate evidence · 1 source

AI-driven systems improve dietary tracking accuracy and enable personalized diet recommendations and disease-specific nutrition management in clinical and public health practice.

A systematic review published October 14, 2025 synthesized peer-reviewed literature from January 2020 to July 2025 on AI in nutrition and dietetics, covering dietary assessment, personalized nutrition and chronic disease management, generative AI and conversational agents, public health nutrition, sensory science, and ethics.

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

Updated Aug 6, 2026 · TRV-2026-0669

67Index score

AI problems · 772

537
CrimeStableModerate evidence · 1 source

Detection skill did not generalize across modalities, and confidence did not track voice accuracy, leaving people vulnerable to deepfake-enabled identity theft and financial fraud despite some detection ability.

By June 2026, researchers had tested whether people can tell real from synthetic faces and voices and whether that skill transfers across senses. In a preregistered study, participants classified both types of stimuli, and performance for each modality was significantly above chance when measured with signal detection theory.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0151

67Index score
538
EducationStableModerate evidence · 1 source

Same preservice teachers raised concerns that LLM outputs contained inaccuracies, encouraged overreliance, and were less useful for validation phases of modeling.

By April 2026, researchers studied 150 mastere28099s-level preservice teachers at a German university as they collaboratively solved three authentic mathematical modeling problems with large language models, analyzing interaction worksheets, surveys, and interviews.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0148

67Index score
539
HealthStableModerate evidence · 1 source

Longitudinal AI use among cancer specialists led to intuition rust and skill atrophy, gradually dulling expert judgment.

What happened: By April 2026, researchers completed a year-long study of AI use among cancer specialists and found that initial operational gains hid intuition rust, described as the gradual dulling of expert judgment, which later evolved into chronic harms such as skill atrophy and identity commoditization.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0147

67Index score
540
Media & ArtsStableModerate evidence · 1 source

An AI-generated imitation of Drake and The Weeknd's voices in a viral track convinced millions of listeners it was authentic, requiring a label disclosure.

By June 2026, a law review article described how generative AI had been used to create a viral 2023 track, 'Heart on My Sleeve,' that imitated Drake and The Weeknd. The label confirmed the vocals were not the artists but an AI imitation trained on their recordings, after millions had been convinced it was real.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 13, 2026 · TRV-2026-0146

67Index score

Recomputed live from the record · Oct 13, 2026, 6:21 PM