Truace
Subscribe
The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 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.

933 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.

1,703 results
Show filters and sorting

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

AI gains · 933

202
HealthStableModerate evidence · 1 source

A random forest model using only routine preinjury clinical data predicted posttraumatic epilepsy onset up to 10 years after TBI in veterans, achieving AUC around 0.73-0.75 and identifying 17.5% of 5-year cases at 2.3% false positive rate.

Researchers developed and validated machine learning models to predict posttraumatic epilepsy onset at 2, 5, and 10 years after first TBI documentation in 107,987 post-9/11 US veterans, using only routine preinjury clinical data up to the month of injury. An optimized random forest achieved AUCs of 0.75 to 0.73 across horizons on held-out test data and enabled high-risk stratification.

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

Updated Aug 5, 2026 · TRV-2026-0652

71Index score
203
HealthStableModerate evidence · 1 source

Monthly allocation of Medicaid care-management outreach by predicted individualized treatment effect prevented substantially more ED visits or hospital admissions than risk-based allocation at the same 10% capacity.

Researchers compared two ways to allocate scarce Medicaid care-management phone outreach each month for 164,063 beneficiaries in Washington and Virginia. Using a causal forest to estimate individualized treatment effects, they found targeting the top decile by predicted effect prevented 13.3 acute events per 2000 members per month, compared with 2.5 events under conventional top-decile risk targeting.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
85

Updated Jul 26, 2026 · TRV-2026-0571

71Index score
204
SportsStableModerate evidence · 1 source

Integrated AI systems in sports biomechanics reduced reinjury rates by 23% and enabled technique assessment and injury prediction with high accuracy.

By August 2025, a scoping review of 73 studies published between 2015 and 2024 examined AI in sports biomechanics, focusing on wearable technology, motion analysis, and injury prevention. It reported that convolutional neural networks reached 94% agreement with experts, computer vision was within 15 mm of marker-based systems, and integrated AI systems were associated with a 23% reduction in reinjury rates.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0492

71Index score

AI problems · 770

201
EducationStableModerate evidence · 1 source

To develop a machine learning (ML)-based predictive model for assessing the risk of zero-dose children using Demographic Health Survey data in sub-Saharan Africa.

To develop a machine learning (ML)-based predictive model for assessing the risk of zero-dose children using Demographic Health Survey data in sub-Saharan Africa. This study analysed pooled Demographic and Health Survey data from 27 countries in sub-Saharan Africa collected between 2016 and 2024.

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

Updated Sep 20, 2026 · TRV-2026-1153

68Index score
202
HealthStableModerate evidence · 1 source

The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC).

The objective of this study is to develop and validate a Picture Archiving and Communication System-integrated artificial intelligence (PACS-AI) tool for automated neck-level localization, three-dimensional (3D) segmentation, and metastasis risk prediction of cervical lymph nodes (LNs) in patients with papillary thyroid carcinoma (PTC). CT images from 710 patients with PTC, including 4942 LNs, were retrospectively collected from two medical centers and divided into training, internal test, and external test cohorts.

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

Updated Sep 20, 2026 · TRV-2026-1150

68Index score
203
HealthStableModerate evidence · 1 source

Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs.

Background Large language models (LLMs) are increasingly accessed by patients for gastrointestinal health information. Despite their growing use, concerns persist regarding accuracy, empathy, actionability, and readability of responses generated by LLMs.

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

Updated Sep 20, 2026 · TRV-2026-1148

68Index score
204
HealthStableModerate evidence · 1 source

Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application: The primary concern is the disparity between pixel-level performance metrics and their clinical significance.

Artificial intelligence in ophthalmology encounters a continual challenge: Systems proficient in picture classification seldom yield quantifiable enhancements in patient outcomes. The primary concern is the disparity between pixel-level performance metrics and their clinical significance.

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

Updated Sep 20, 2026 · TRV-2026-1147

68Index score

Recomputed live from the record · Oct 12, 2026, 1:47 AM