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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 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.

935 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 · 935

197
ClimateStableModerate evidence · 1 source

AI-based automation in Industry 4.0/5.0 reduces energy consumption and waste on production lines by optimizing processes and minimizing downtime.

By March 2026, this peer-reviewed overview described how AI-based automation in Industry 4.0 optimized production, logistics, and resource management to reduce waste and energy use, and how Industry 5.0 expanded that with human-machine collaboration, generative AI, digital twins, and decentralized smart grids and microgrids.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 19, 2026 · TRV-2026-0281

72Index score
198
ScienceStableModerate evidence · 1 source

AI and machine learning techniques enable analysis of high-throughput scientific data to obtain insights, categorize, predict, and support evidence-based decisions across fundamental sciences.

On 2021-10-28 a peer-reviewed survey in The Innovation reviewed how artificial intelligence coupled with machine learning is being developed and applied across information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0207

72Index score
200
HealthStableModerate evidence · 1 source

ChatGPT-5.0 achieved 75.77% accuracy and the highest sensitivity at 76.68% for orthodontic extraction decisions, performing comparably to XGBoost and significantly better than random forest, SVM, logistic regression and MLP.

A comparative study published August 7, 2026 evaluated ChatGPT-5.0 against five supervised machine learning algorithms for orthodontic extraction decisions. Using 520 cases (42.88% extraction, 57.12% non-extraction) and 23 clinical, cephalometric and photographic variables, with expert consensus as reference, ChatGPT-5.0 achieved 75.77% accuracy and 76.68% sensitivity under 5-fold cross-validation, compared to 78.08% accuracy for XGBoost.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
87

Updated Aug 8, 2026 · TRV-2026-0689

71Index score

AI problems · 771

197
HealthStableModerate evidence · 1 source

Artificial intelligence (AI) is increasingly present in dental education and clinical practice through systems that support image interpretation, lesion detection, risk estimation, treatment planning, documentation, and access to clinical information.

Artificial intelligence (AI) is increasingly present in dental education and clinical practice through systems that support image interpretation, lesion detection, risk estimation, treatment planning, documentation, and access to clinical information. Existing curriculum frameworks appropriately emphasize foundational knowledge, ethical use, governance, and human-centered practice.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1165

68Index score
198
HealthStableModerate evidence · 1 source

An application of random forest regression for predicting healthcare costs using administrative databases: Performance was assessed at the individual-level using error metrics, and at the population-level using prediction error (PE).

Background Accurate healthcare cost predictions are essential for health economic decision-making. We propose a bottom-up approach that aggregates individual-level predictions to estimate expenditures for specific population segments without relying on a priori segmentation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1163

68Index score
199
HealthStableModerate evidence · 1 source

[Tailoring care to the mind: The emerging role of artificial intelligence in precision psychiatry. Part II: Validating, regulating and integrating artificial intelligence into care]: Particular attention was given to discrimination, calibration, decision usefulness, external validation, bias, acceptability, interpretability, responsibility, and human oversight.

Objectives Precision psychiatry seeks to tailor care to the clinical, biological, cognitive, behavioral, and contextual characteristics of each patient. Artificial intelligence may help achieve this goal by combining multimodal data, identifying complex patterns, following individual trajectories, and refining prognostic or therapeutic stratification.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 21, 2026 · TRV-2026-1158

68Index score
200
HealthStableModerate evidence · 1 source

Multiscale model calibrated with a Bayesian algorithm predicts long-term outcomes of treatments for mitral valve regurgitation in animal models: In primary mitral valve regurgitation (MR), a failing valve induces volume overload (VO) of the left ventricle (LV), triggering neurohormonal responses and myocardial remodeling.

In primary mitral valve regurgitation (MR), a failing valve induces volume overload (VO) of the left ventricle (LV), triggering neurohormonal responses and myocardial remodeling. Surgical valve repair is the most effective therapy for MR, improving symptoms and in some patients reversing cardiac hypertrophy.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 21, 2026 · TRV-2026-1157

68Index score

Recomputed live from the record · Oct 13, 2026, 2:10 AM