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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

465
HealthStableModerate evidence · 1 source

Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning: The best single-segment performance was achieved in the left mandibular corpus with Logistic Regression (Accuracy=0.833, F1=0.832, AUC=0.958).

Objective This study aimed to evaluate the structural characteristics of mandibular alveolar bone in patients with Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and systemically healthy controls using panoramic radiography-based radiomic analysis combined with machine learning algorithms. Materials and methods A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 22, 2026 · TRV-2026-0844

68Index score
466
ScienceStableModerate evidence · 1 source

Optimal Initialization Scale for Neural Networks With Locally Quadratic Loss Landscapes: An SGD Dynamics Perspective: The results show that, for the simple network model, if the variance of the initialization distribution satisfies our theoretical optimal condition, then the corresponding network achieves lower final training loss and higher test accuracy than the conventional He-normal initialization.

Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a continuous-time approximation as a Fokker-Planck equation for Langevin dynamics, a viewpoint that has motivated many theoretical studies. Within this framework, we study the relationship between the quasi-stationary distribution derived from this equation and the initial distribution through the Kullback-Leibler (KL) divergence.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 21, 2026 · TRV-2026-0842

68Index score
467
LifestyleStableModerate evidence · 1 source

Redefining the design innovation process: Embedding sustainability through AI-generated camping cookware: In addition, AIGC can accurately reproduce design schemes, improve design efficiency and promote creativity generation..

With the increasing interest in glamping, the demand for more functional camping cookware has grown, driving innovation in its design. This study aims to explore innovative solutions for camping cookware, and establishes a systematic product conceptual design process through the integration of artificial intelligence-generated content (AIGC) and appropriate data analysis tools.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 21, 2026 · TRV-2026-0841

68Index score

AI problems · 772

465
Media & ArtsStableModerate evidence · 1 source

Writers who used LLM-generated story ideas produced short stories that were more similar to each other, reducing collective diversity and producing a narrower scope of novel content.

In an online experiment reported July 12 2024, researchers gave some writers LLM-generated story ideas and had independent evaluators rate the resulting short stories. By that date they observed that access to AI ideas caused higher ratings for creativity, writing quality, and enjoyment, especially for less creative writers.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0386

67Index score
466
PolicyStableModerate evidence · 1 source

Deployment of AI systems leaves individuals increasingly unable to understand or seek accountability for resulting harms, eroding the human rights framework's core function of empowering individuals against power disparities

Published 2024-08-19, this peer-reviewed article argues that AI's impact on human rights extends beyond discrete violations to a deeper attritional degradation. Using the concept of slow violence, it contends individuals lose capacity to comprehend and contest AI-driven harms, discrete rights lose their normative justifications, and even broad notions of human dignity fail to capture new challenges

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0382

67Index score
467
HealthStableModerate evidence · 1 source

Complexity and volume of wearable sensor data create substantial modeling challenges, with additional barriers of data quality, computational requirements, interpretability, and privacy concerns for LLM deployment.

As of August 4, 2024, this peer-reviewed survey in Sensors reviewed early trends in using large language models such as GPT-4 and Llama to model vast wearable sensor data for human activity recognition, health monitoring, and behavioral modeling, integrating them with time series and deep learning methods.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0381

67Index score
468
HealthStableModerate evidence · 1 source

AI-integrated microfluidic technologies face persistent challenges in manufacturing, clinical validation, and system integration that limit translation into routine clinical and public health practice.

A February 2026 review in Biosensors summarizes how lab-on-a-chip systems have been advanced through 3D printing, modular substrates, and biosensor integration, and how coupling with AI and machine learning has created smart platforms for cancer diagnostics, infectious disease detection, point-of-care testing, and therapeutic monitoring.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0380

67Index score

Recomputed live from the record · Oct 13, 2026, 10:24 AM