Truace
Subscribe
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.

1,706 results
Show filters and sorting

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

AI gains · 935

297
ScienceNewly addedModerate evidence · 1 source

AI-assisted predictive microbiome design helps identify and deploy beneficial microbial inoculants that lower pest and disease burden and enhance productivity in cereal, legume, and horticulture crops.

Published October 1 2026, this peer-reviewed review synthesizes ecological, molecular, and technological work on eco-smart biocontrol, positioning the rhizosphere as a hotspot for plant-microbe interactions and describing how multi-omics and artificial intelligence-assisted predictive microbiome design are used to discover and design microbial biocontrol agents.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1249

68Index score
298
HealthNewly addedModerate evidence · 1 source

AI-based tools including chatbots and clinical decision-support systems were associated with improved accessibility of mental health support and more efficient risk assessment in psychiatric mental health nursing contexts.

This October 2026 scoping review examined how AI tools such as chatbots, large language models, digital therapeutics and clinical decision-support systems are being applied in mental health care, synthesizing 42 sources from January 2022 to May 2026 to assess implications for psychiatric mental health nursing.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1248

68Index score
299
HealthNewly addedModerate evidence · 1 source

Deep learning models applied to H&E whole-slide images can reliably identify melanomas at risk of recurrence and progression, improving diagnosis and personalized risk stratification for stage I-III cutaneous melanoma.

By October 2026, a review in the International Journal of Dermatology examined how computational pathology and convolutional neural networks applied to H&E whole-slide images could improve melanoma diagnosis and risk stratification beyond current AJCC staging, including integration with transcriptomic and spatial proteomic data.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1247

68Index score
300
HealthNewly addedModerate evidence · 1 source

Training 13 machine learning classifiers on the NEW10 macrophage module enabled accurate distinction of acute coronary syndrome from stable coronary artery disease and ruptured from stable plaques, with SVM linear and Naive Bayes reaching AUC 0.9333 in independent testing.

Researchers constructed a single-cell atlas of human coronary atherosclerosis and identified six macrophage subpopulations, focusing on a THBS1+ subset linked to hypoxia adaptation and TGF-β signaling. They derived a 12-module co-expression network with NEW10 as a THBS1+ signature, predicted THBS1-CD36 crosstalk with lymphatic endothelial cells, and computationally screened SMS121 against CD36.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 2, 2026 · TRV-2026-1245

68Index score

AI problems · 771

297
EducationStableModerate evidence · 1 source

AI auto-contouring raises concerns that future radiation therapy practitioners may have reduced ability to critically evaluate auto-generated contours.

This peer-reviewed educational perspective from August 2026 examined how organ-at-risk contouring is taught in an Australian undergraduate radiation therapy program as AI auto-contouring enters clinical workflows. The authors reviewed curriculum scope and technologies and examined students' preferred methods, confidence across OARs, and perceived factors affecting quality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 24, 2026 · TRV-2026-0863

68Index score
298
HealthStableModerate evidence · 1 source

The TyG-FI showed limited incremental predictive advantage over FI-Lab alone and reduced discrimination on external validation, with AUROCs dropping to 0.694 and 0.667 in eICU compared to 0.764 and 0.769 internally.

Researchers derived and tested the triglyceride-glucose frailty index in 2230 MIMIC-IV adults with KDIGO-defined AKI, examining associations with ICU, in-hospital, 28-day, 90-day and 365-day mortality, identifying two consensus phenotypes, and evaluating 12 prediction algorithms with SHAP and LIME interpretation and external validation in 1831 eICU patients.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 24, 2026 · TRV-2026-0862

68Index score
299
ClimateStableModerate evidence · 1 source

Sentinel-2 and CART-based operational AGB mapping in Pinus brutia produced total estimates that differed by more than 700,000 Mg across the same 13,687 ha area depending solely on which allometric reference was used, making reference selection a dominant source of uncertainty.

On 2026-08-22, a peer-reviewed study reported testing how three different allometric reference datasets affect Sentinel-2-based aboveground biomass mapping in Pinus brutia. Using 112 field plots and CART as primary model with Random Forest as robustness check, authors mapped biomass over 13,687 ha and compared totals to forest management plan data.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 24, 2026 · TRV-2026-0861

68Index score
300
HealthStableModerate evidence · 1 source

Technostress mediated the relationship, such that when openness to change and positive AI attitudes were low, higher technostress was associated with reduced innovative work behavior among the same hospital staff.

A peer-reviewed study in Journal of Health Organization and Management examined how healthcare workers respond to AI-driven transformation. Using face-to-face surveys of 305 staff at a university hospital in Istanbul in early 2026, the authors tested whether openness to organizational change and attitudes toward AI affect innovative work behavior via technostress.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 24, 2026 · TRV-2026-0859

68Index score

Recomputed live from the record · Oct 12, 2026, 2:30 PM