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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 934 sourced gains and 771 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.

934 gains
771 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 · 934

257
ScienceRisingHigh evidence · 5 sources

The development and release of ethics guidelines provides normative principles intended to help harness disruptive potentials of AI systems in research, development and application.

This paper conducts a semi-systematic evaluation that analyzes and compares 22 ethics guidelines released in recent years following advances in research, development and application of AI systems. The guidelines are described as comprising normative principles and recommendations.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
82

Updated Jul 12, 2026 · TRV-2026-0056

70Index score
258
PolicyRisingHigh evidence · 5 sources

Breakthroughs in algorithmic machine learning and autonomous decision-making are engendering new opportunities for continued innovation and augmentation of human tasks.

The source text presents AI as a transformative force comparable to the industrial revolution, highlighting a staggering pace of change driven by breakthroughs in algorithmic machine learning and autonomous decision-making. It states this enables augmentation and potential replacement of human tasks across industrial, intellectual and social applications, with potential disruption to finance, healthcare, manufacturing, retail, supply chain, logistics and utilities.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
82

Updated Jul 12, 2026 · TRV-2026-0049

70Index score
259
EducationNewly addedModerate evidence · 1 source

In higher education, higher multimodal literacy, self-efficacy, and university support increased students' perceived usefulness and ease of use of AI tools, with perceived usefulness driving more positive attitudes and stronger intentions to adopt them.

Published September 8 2025, this peer-reviewed study examined AI tool adoption among 498 higher-education students using a PLS-SEM model that integrated multimodal literacy, self-efficacy, and university support into the technology acceptance model. It found perceived usefulness was the strongest predictor of attitude and intention to use AI tools, with all three antecedents significantly affecting perceived usefulness and ease of use.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
100

Updated Oct 11, 2026 · TRV-2026-1324

69Index score
260
HealthNewly addedModerate evidence · 1 source

An interpretable XGBoost model combining clinical variables and multimodal musculoskeletal ultrasound achieved high accuracy for osteoporosis prediction, offering a practical tool for early risk stratification.

Researchers developed and validated an interpretable machine learning model for osteoporosis prediction using clinical variables and multimodal musculoskeletal ultrasound from participants enrolled at two medical centers. After LASSO feature selection, eight algorithms were compared, and the XGBoost model achieved accuracies of 0.889 in training, 0.825 in internal validation, and 0.773 in external testing, with SHAP used to explain feature contributions.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
100

Updated Oct 11, 2026 · TRV-2026-1323

69Index score

AI problems · 771

257
HealthStableModerate evidence · 1 source

Canadian medical students not interested in radiology reported a higher perceived impact of AI on the field and lower perceived career sustainability, alongside limited adequate radiology exposure.

By September 2026, researchers surveyed 73 Canadian medical students to test whether perceptions of radiology's procedural scope and AI integration differed by interest in radiology. Interested students rated therapeutic and diagnostic procedures as more important and reported higher perceived career sustainability, while non-interested students reported higher perceived AI impact.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0997

68Index score
258
HealthStableModerate evidence · 1 source

Current AI models frequently produce visually plausible but anatomically inaccurate illustrations, with major errors across all models, making them unreliable for unsupervised clinical use.

On September 4, 2026, a peer-reviewed study in Academic Radiology evaluated 25 musculoskeletal imaging cases where three AI systems generated visual summaries from report text alone. Two fellowship-trained MSK radiologists rated each image for anatomical accuracy and clinical usefulness, finding Gemini 3.0 Pro most consistent at 40-42% accurate and 60-65% useful, while ChatGPT and Perplexity frequently produced plausible but inaccurate images.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0996

68Index score
259
HealthStableModerate evidence · 1 source

Adoption of digital pathology and AI in liver disease is constrained by access and logistics barriers, quality issues, lack of guidance, and unproven real-world effectiveness and clinical safety.

A September 2026 review in The Lancet Digital Health summarizes how digital pathology, image analysis, and AI, including deep learning on high-resolution whole-slide images, are being applied to liver disease, liver cancer diagnosis, and transplantation assessment. The authors describe expanding use from long-standing research applications to increasing clinical practice access.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0995

68Index score
260
EducationStableModerate evidence · 1 source

Gen X and Gen Y teachers reported heightened concerns that student use of generative AI in higher education could lead to overreliance and create ethical and pedagogical problems without proper guidelines and policies.

A November 2023 peer-reviewed study surveyed Generation Z students and Generation X and Generation Y teachers about generative AI in higher education. Gen Z respondents were generally optimistic about benefits such as productivity and personalized learning and said they intended to use the tools for educational purposes, while Gen X and Gen Y teachers acknowledged benefits but reported stronger concerns about overreliance and ethical and pedagogical implications.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0988

68Index score

Recomputed live from the record · Oct 12, 2026, 10:20 AM