Truace
Subscribe
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
Show filters and sorting

Download every matching row, not just this page:Export CSVExport JSON

AI gains · 939

497
HealthStableModerate evidence · 1 source

AI models predicted operative difficulty in laparoscopic cholecystectomy with pooled discrimination of 0.848 in training and 0.818 in validation, with ensemble and multimodal models performing best.

By August 13, 2026, a systematic review and meta-analysis of 18 studies found AI models predicted laparoscopic cholecystectomy difficulty with pooled AUCs of 0.848 in training and 0.818 in validation, with ensemble models reaching 0.889 and 0.861. The review searched four databases to March 2, 2026 and used PROBAST and GRADE to assess bias and certainty.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0787

67Index score
498
HealthStableModerate evidence · 1 source

Causal AI integrated into healthcare informatics systems improves personalized clinical decision-making by estimating individual treatment effects and simulating intervention outcomes.

On 2026-08-13, a review in Personalized Medicine examined integration of causal artificial intelligence and data-driven decision intelligence within healthcare informatics to advance personalized medicine. Using a narrative review of literature from PubMed, Scopus, Web of Science, IEEE Xplore and ScienceDirect, the authors synthesized evidence on causal inference methods and clinical applications.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0786

67Index score
499
LaborStableModerate evidence · 1 source

A gradient boosting machine trained on survey data from 318 ICU nurses predicted high moral distress with high discrimination using six predictors, preserving accuracy after feature reduction.

In a multicentre cross-sectional study of 318 ICU nurses in China, researchers developed a machine learning risk-profiling model for moral distress, which was present in 28.6% of participants. A gradient boosting machine achieved the best balanced performance and, after SHAP-guided reduction, retained full accuracy with six predictors: monthly night shifts, financial responsibility role, psychological resilience, sleep quality, nurse-to-patient ratio, and weekly working hours.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0785

67Index score
500
HealthStableModerate evidence · 1 source

L2-regularized logistic regression trained on routine non-invasive paraclinical markers achieved stable discrimination with minimal generalization gap and robust calibration for early prediction of bacterial infections in infants aged 1 to 90 days.

Researchers retrospectively analyzed 306 infants aged 1 to 90 days hospitalized between 2014 and 2022 in Khorasan Razavi, Iran, using CSF culture via lumbar puncture as the gold standard, to train nine machine learning classifiers on routine non-invasive paraclinical markers with nested cross-validation and SHAP interpretation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0783

67Index score

AI problems · 772

497
LaborStableModerate evidence · 1 source

Automation and AI have affected HRM professional roles and taken over some HRM functions, imposing new competency requirements that existing research has not fully mapped.

Published March 23, 2026, this peer-reviewed integrative review examined how automation, artificial intelligence and disruptive technologies are changing human resource management. Using thematic analysis of secondary data, the authors identified four competency themes and proposed a framework intended to make HRM professionals aware of skills needed to be future-ready.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0284

67Index score
498
PolicyStableModerate evidence · 1 source

Generative AI development outpaces governance, creating risks to human autonomy, operational safety from non-deterministic outputs, and intellectual property.

Published March 26, 2026, this peer-reviewed article revisits the Six Human-Centered AI Grand Challenges in light of generative AI. It argues that while generative systems move AI toward creative interaction, benefits depend on addressing governance gaps and new technical risks.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0282

67Index score
499
HealthStableModerate evidence · 1 source

Biased medical AI can lead to substandard clinical decisions and perpetuate healthcare disparities, with performance deteriorating differentially across patient subgroups when deployed outside training cohorts.

This 2024 peer-reviewed discussion examines how biases arise and compound throughout the medical AI lifecycle, from data features and labels through model development, evaluation, deployment, and publication, and how those biases affect clinical decision-making.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0278

67Index score
500
HealthStableModerate evidence · 1 source

Deep learning models for medical diagnosis struggle to maintain performance when faced with adversarial or noisy inputs, and are vulnerable to adversarial attacks that deceive models and privacy attacks that extract sensitive patient information.

Published November 8, 2024, this review examines whether deep learning models for medical diagnosis can maintain performance when exposed to adversarial or noisy inputs, analyzing influences such as model complexity, training data quality, and hyperparameters.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0276

67Index score

Recomputed live from the record · Oct 13, 2026, 2:09 PM