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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 932 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.

932 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 · 932

65
LifestyleStableModerate evidence · 1 source

Synthetic relationships with AI companions could reduce loneliness by providing always-available, adaptive, emotionally responsive interaction that fosters companionship and lowers social anxiety.

This review examines generative AI-enabled synthetic relationships, defined as ongoing associations with AI companions designed to simulate human-like bonds, as a potential intervention for loneliness where traditional approaches face availability and scalability limits.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0254

77Index score
66
PolicyStableModerate evidence · 1 source

Researchers developed the University Policy Development Framework for Generative AI to help universities assess priorities and build sustainable governance capacity.

Researchers conducted a cross-national analysis of generative AI guidelines issued by leading universities in the United States, Japan, and China, coding policies across five Technology Acceptance Model domains to identify 20 themes and to build the University Policy Development Framework for Generative AI (UPDF-GAI).

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0251

77Index score
67
PolicyStableModerate evidence · 1 source

Chinese and South Korean regulatory toolkits for AI journalism on platforms like Toutiao and Naver were found to mitigate risks of digital infodemics and algorithmic bias.

This comparative study analyzed China and South Korea's distinct approaches to governing AI journalism and algorithmic news curation, examining policy documents and evidence from Toutiao and Naver to assess how each balances fairness and accountability.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
84

Updated Jul 17, 2026 · TRV-2026-0247

77Index score
68
HealthStableModerate evidence · 1 source

AI combined with multimodal retinal imaging provides a non-invasive method for early risk stratification and screening for cardiovascular, metabolic and neurodegenerative disorders using retinal vasculature and nerve layer changes.

A May 2026 review in Graefe's Archive describes AI combined with multimodal retinal imaging as a non-invasive approach to detect and monitor systemic vascular and neurodegenerative conditions. It outlines how fundus photography, OCT, OCTA and metabolic-sensitive imaging capture retinal vascular and nerve changes that reflect cardiovascular, metabolic and neurological disease, analyzed with deep learning and multimodal fusion.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0192

77Index score

AI problems · 770

65
HealthStableModerate evidence · 1 source

Image-Based Diagnosis of Oral Lesions: Performance of a Vision-Language Model versus Human Clinicians: Urgency assignment was correct in 70% of cases (κ = 0.716), with a conservative tendency to overestimate risk.

Objective To evaluate the real-world diagnostic performance of a multimodal large language model (LLM) for image-based assessment of oral mucosal lesions compared with clinicians of varying expertise. Study design Prospective international multicenter diagnostic accuracy study.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1166

74Index score
66
HealthStableModerate evidence · 1 source

Overall abstraction accuracy was 99.33% (61 errors) for the LLM versus 98.19% (164 errors) for human abstraction (McNemar p Conclusions In this proof-of-concept validation study, a customized LLM achieved significantly higher abstraction accuracy than conventional human review for general and breast reconstruction NSQIP variables.

Background National Surgical Quality Improvement Program (NSQIP) data collection depends on labor-intensive manual chart abstraction, limiting efficiency, increasing cost, and necessitating patient sampling. This study evaluated whether a large language model (LLM) could accurately abstract unstructured NSQIP breast reconstruction variables compared with conventional human abstraction.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1141

74Index score
67
HealthStableModerate evidence · 1 source

Artificial intelligence-driven decision-making after endoscopic resection for early gastric cancer: Current guideline-based strategies, including the eCura system, provide structured risk stratification but rely on categorical decision-making and may lead to overtreatment, as nearly 90% of patients undergoing additional surgery do not have LNM.

Early gastric cancer (EGC) is increasingly managed by endoscopic resection (ER); however, lymph node metastasis (LNM), which occurs in approximately 5%-10% of cases, remains the key determinant for recommending additional gastrectomy. Current guideline-based strategies, including the eCura system, provide structured risk stratification but rely on categorical decision-making and may lead to overtreatment, as nearly 90% of patients undergoing additional surgery do not have LNM.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0984

74Index score
68
HealthStableModerate evidence · 1 source

Four machine learning (ML) algorithms (Random Survival Forest, XGBoost, Elastic Net-regularized Cox, Support Vector Machine) were trained (80%) and tested (20%) to predict overall survival (OS).

Background Enfortumab vedotin (EV) has transformed treatment for advanced urothelial carcinoma (aUC), but outcomes vary. Machine learning (ML) with explainable artificial intelligence (XAI) may improve survival prediction.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0978

74Index score

Recomputed live from the record · Oct 11, 2026, 7:07 AM