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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 929 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.

929 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.

1,699 results
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AI gains · 929

21
ClimateStableModerate evidence · 1 source

Conformal Sampling-derived MLIPs predicted reaction energies and barriers for CO2 hydrogenation on Cu(100) and Ni(100) with maximum errors of 0.05 eV and 0.03 eV, enabling rapid transfer from copper to nickel.

A peer-reviewed study introduced the Conformal Sampling of Catalytic Processes workflow to model heterogeneous CO2 hydrogenation on copper and nickel. Starting from exhaustive DFT on Cu(100), the team trained a Machine Learning Interatomic Potential and transferred it to Ni(100) to test cross-metal portability.

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

Updated Sep 17, 2026 · TRV-2026-1119

78Index score
22
HealthStableModerate evidence · 1 source

A three-step ML framework using SHAP and cross-validation achieved high LOOCV AUCs and flagged self-reported factors like treatment expectations and self-efficacy as candidate predictors of recovery at 3 months in spinal pain patients receiving chiropractic care.

Researchers developed a three-step machine learning approach to explore self-reported predictors of recovery in 96 spinal pain patients undergoing chiropractic care, using baseline questionnaires to predict binary recovery at 3 months across pain, disability, quality of life and global impression measures.

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

Updated Sep 16, 2026 · TRV-2026-1109

78Index score
23
PolicyStableModerate evidence · 1 source

An ensemble of machine learning models analyzed 2.4 million physiological samples from simulator drives to classify driver cognitive states, with XGBoost identified as the most robust classifier.

A peer-reviewed simulator study in Kuwait tested 60 licensed drivers under high-density, low-density, and no-advertising conditions, recording about 2.4 million samples of pupil diameter, blink density, and gaze velocity with wearable eye-tracking and classifying cognitive states with an ensemble including XGBoost.

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

Updated Sep 16, 2026 · TRV-2026-1105

78Index score
24
BusinessStableModerate evidence · 1 source

Organizations integrating AI can advance sustainability and contribute to UN Sustainable Development Goals by aligning strategy, infrastructure and continuous improvement to produce social, environmental and economic benefits.

This peer-reviewed systematic review examined 57 articles on artificial intelligence and sustainable development, finding that contributions cluster in organizational integration, technical algorithm development, and internal processing changes. It proposes a conceptual model for organizations to incorporate AI into sustainability efforts.

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

Updated Sep 15, 2026 · TRV-2026-1101

78Index score

AI problems · 770

21
HealthStableModerate evidence · 1 source

Implementation of AI for cardiovascular prevention remains limited by insufficient prospective evidence and randomized trials, lack of validation in heterogeneous populations, limited model interpretability, and inadequate digital and regulatory infrastructures, leaving a gap between guideline recommendations and real‑

This peer-reviewed review examines AI, including machine learning and deep learning, for cardiovascular prevention in Italy and globally, where cardiovascular diseases remain the leading cause of mortality and morbidity. It surveys evidence that AI can deliver more precise, dynamic and personalized risk stratification than traditional scores and support early detection of subclinical disease via AI-enabled electrocardiography and opportunistic imaging.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0955

78Index score
22
HealthStableModerate evidence · 1 source

AI application in xenotransplantation is limited by lack of clinical data, species-specific differences, and missing standardized definitions and ground truth datasets for xenograft injury.

On September 1, 2026, a peer-reviewed roadmap in Xenotransplantation outlined how artificial intelligence could be integrated into early clinical xenotransplantation to address organ shortage. It prioritizes digital pathology, machine perfusion monitoring, multimodal graft injury detection, and xenozoonotic infection surveillance, emphasizing clinician-supervised, auditable systems combined with gene-edited donors.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0953

78Index score
23
HealthStableModerate evidence · 1 source

Conventional ultrasound imaging's profound reliance on operator expertise restricts reproducibility and global accessibility.

This comprehensive review traces ultrasound from operator-dependent manual imaging to robotic ultrasound systems developed over the past two decades, including teleoperated telesonography over 5G and increasingly autonomous platforms using force control and path planning.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
91

Updated Aug 26, 2026 · TRV-2026-0898

78Index score
24
EducationStableModerate evidence · 1 source

In educational use, ChatGPT can generate wrong information and reflect training-data biases that may augment existing biases, raising privacy issues.

Published December 7, 2023, this exploratory synthesis examines ChatGPT after its November 30, 2022 public release and rapid adoption, reviewing recent literature on how the tool is being used in education. It identifies potential benefits for personalized and interactive learning and for formative assessment, while also noting drawbacks.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
90

Updated Aug 19, 2026 · TRV-2026-0835

78Index score

Recomputed live from the record · Oct 11, 2026, 2:32 AM