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

489
ClimateStableModerate evidence · 1 source

A probabilistic causal machine learning framework trained on long-term monitoring data from a full-scale wastewater plant can predict N2O hot moments and convert interpretable outputs into risk-decision rules for adaptive aeration and load regulation to reduce emissions.

Researchers developed a probabilistic causal machine learning framework using long-term online monitoring data from a full-scale biological wastewater treatment plant to address intermittent nitrous oxide emission hot moments. The approach combined predictive modeling with cohort-based SHAP for nonlinear effects, LiNGAM-based causal discovery for pathway identification, and copula-based joint probability analysis for risk quantification.

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

Updated Aug 17, 2026 · TRV-2026-0802

67Index score
490
HealthStableModerate evidence · 1 source

AI provides powerful computational methods to extract diagnostic, biological and epidemiological insights from high-volume microbiology datasets, with potential to enhance diagnostics, antimicrobial stewardship and infection prevention when integrated into lab workflows.

Published August 15, 2026, this narrative review in Clinical Microbiology and Infection introduces the machine learning lifecycle from a clinical microbiology perspective, covering data preparation, model development, evaluation, and deployment, drawing on applied research and AI development guidelines for healthcare.

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

Updated Aug 17, 2026 · TRV-2026-0800

67Index score
491
HealthStableModerate evidence · 1 source

A multi-modal deep learning framework using Heckmatt scores from six key muscles improved speed and diagnostic performance for muscle ultrasound, predicting neuromuscular pathology with an area under the precision-recall curve of 0.87 on a test set of 320 patients.

Researchers developed a single-center multi-modal deep learning framework that fuses muscle ultrasound Heckmatt scores from six key muscles with patient BMI and age to screen for neuromuscular pathology. Tested on 320 patients, the model achieved an area under the precision-recall curve of 0.87 for distinguishing presence versus absence of disease.

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

Updated Aug 17, 2026 · TRV-2026-0799

67Index score
492
HealthStableModerate evidence · 1 source

Converting numerical clinical data into 24-bit rectangular coded images and training ResNet architectures improved heart failure survival prediction, reaching reported accuracy up to 0.9617.

On August 15, 2026, a peer-reviewed paper described a method that converts numerical heart failure data into 24-bit rectangular coded images to fit deep learning input sizes, then augments the dataset through horizontal augmentation and rotation in multiples of 15b0. The resulting images were used to train ResNet18 and ResNet50 models for survival prediction.

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

Updated Aug 17, 2026 · TRV-2026-0797

67Index score

AI problems · 772

489
HealthStableModerate evidence · 1 source

AI-based risk stratification tools in cardiothoracic surgery face limited interpretability, dataset bias, inconsistent external validation, and uncertain real-world implementation.

The peer-reviewed article reviews risk stratification in cardiothoracic surgery, noting that EuroSCORE II and STS scores are standard but static, while AI and machine learning studies have reported improved predictive discrimination in selected cohorts by handling complex data.

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

Updated Jul 20, 2026 · TRV-2026-0317

67Index score
490
HealthStableModerate evidence · 1 source

Adaptive AI tools for cardiovascular care challenge conventional HTA frameworks designed for static interventions, resulting in indirect alignment with EU HTAR requirements and inconsistent engagement of cardiologists in evaluation.

A systematic review published June 30, 2026 searched PubMed, Scopus and ScienceDirect for 2020-2025 literature on AI in cardiology and HTA. After screening 223 records, six studies were included, covering stroke outcome prediction, atrial fibrillation screening and wearable-based monitoring, supported by 17 documents, and compared against three HTA frameworks for EU HTAR alignment.

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

Updated Jul 20, 2026 · TRV-2026-0316

67Index score
491
HealthStableModerate evidence · 1 source

A small proportion of chatbot responses were potentially misleading due to missed red-flag features and poor actionability with insufficient action-oriented recommendations.

Researchers evaluated six AI chatbots using 36 simulated English-language patient questions about nipple discharge, generating 216 first-turn responses scored against clinical guidelines. By the July 2026 publication date, 87.5% of responses were rated safe, 8.8% had minor omissions, and 3.7% were potentially misleading, with an overall red-flag recognition rate of 90.6%.

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

Updated Jul 20, 2026 · TRV-2026-0315

67Index score
492
HealthStableModerate evidence · 1 source

Standard 5.0 mm chest CT reconstructions obscure mild calcification due to partial volume effects, causing significant false-negative CAC zero assessments in routine screening.

By July 2026, researchers had tested whether native thin-slice images could recover coronary calcification missed on standard 5.0 mm chest CT. Using a validated deep learning algorithm to quantify CAC on paired reconstructions, they found 19.0% of internal cohort patients and 10.2% of NLST participants were reclassified from CAC =0 to CAC >0 on thinner slices.

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

Updated Jul 20, 2026 · TRV-2026-0314

67Index score

Recomputed live from the record · Oct 13, 2026, 1:20 PM