Truace
Subscribe
The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 934 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.

934 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.

1,705 results
Show filters and sorting

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

AI gains · 934

261
HealthNewly addedModerate evidence · 1 source

A four-gene glycolysis risk model built with machine learning effectively forecasted survival and treatment efficacy for skin cutaneous melanoma patients.

By October 2026, researchers had combined GTEx and TCGA data with machine learning to build a four-gene glycolysis signature for skin cutaneous melanoma. The signature, based on GOT2, CLN6, GYS1, and IDUA, was developed into a risk model reported to forecast patient survival and treatment efficacy, with additional in vitro work on GYS1 and CLN6.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
100

Updated Oct 11, 2026 · TRV-2026-1322

69Index score
262
LaborNewly addedModerate evidence · 1 source

Emotional AI systems like Replika and Xiaoice offer accessible companionship and promise of connection for users facing isolation or psychological distress.

A peer-reviewed article in Frontiers in Psychology published September 18, 2025 examines emotional AI systems including Replika, Xiaoice, companion chatbots and therapeutic apps that interpret emotional states and simulate empathic responses, marketed as AI friends for users facing isolation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
100

Updated Oct 10, 2026 · TRV-2026-1320

69Index score
263
HealthNewly addedModerate evidence · 1 source

Integration of artificial intelligence-assisted middleware into total laboratory automation enables standardized validation and improves diagnostic accuracy for low-concentration analytes in endocrinology and therapeutic drug monitoring.

Published October 2026, this review examines integration of liquid chromatography-tandem mass spectrometry into Total Laboratory Automation, highlighting fully automated sample-to-result modules such as Roche cobas i 601 and Shimadzu CLAM-2040 and the role of intelligent middleware and AI-assisted algorithms for data validation and monitoring.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
100

Updated Oct 10, 2026 · TRV-2026-1319

69Index score
264
HealthStableModerate evidence · 1 source

In a randomized double-blind crossover study of text consultations, AMIE showed greater diagnostic accuracy than primary care physicians.

Researchers introduced AMIE, an LLM-based system for diagnostic dialogue, and tested it against 20 primary care physicians in 159 text-based scenarios with patient-actors from Canada, the UK and India. Specialist and patient-actor raters scored performance across 32 and 26 axes including history-taking and management.

Impact 30%
55
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
85

Updated Jul 24, 2026 · TRV-2026-0530

69Index score

AI problems · 771

261
HealthStableModerate evidence · 1 source

The Challenges of Predicting Rare Outcomes: A Critical Appraisal of Machine Learning Using the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model in a Tanzanian Injury Registry: These findings highlight the challenge of predicting a rare outcome, and emphasize the need to increase pediatric registry sample sizes to develop more accurate models for mortality risk stratification in LMICs.

Background Injuries are responsible for 950,000 deaths per year among children and adolescents under 18 years old. Trauma prediction scores are useful in determining severity and prognosis of injury patients.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0987

68Index score
262
HealthStableModerate evidence · 1 source

When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups.

Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial intelligence methods, though, average performance alone does not show whether a dataset is artificial intelligence ready, whether the endpoint is clinically valid, or whether errors are unevenly distributed across patient subgroups.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0986

68Index score
263
ScienceStableModerate evidence · 1 source

Explainable deep learning improves human mental models of self-driving cars: The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .

Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity of these black-box planners makes it challenging to accurately anticipate when they will fail 4-6 , with potentially catastrophic consequences 7-9 .

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0981

68Index score
264
HealthStableModerate evidence · 1 source

A novel computational solution, termed BioPINN-LM, integrates 2 computational methods for the rapid real-time prediction of aortic wall stress: a physics-informed neural network (PINN) trained with mechanical simulation data to predict wall stress and a multimodal large language model that uses output data from the PINN along with image-based geometry descriptors to provide interpretable risk assessments from both ends of the aorta.

Axial dissections of the thoracic artery are common causes of death in people diagnosed with aortic dissection; however, decisions to intervene on ascending thoracic aortic patients are determined by the size of the ascending thoracic aorta based on its diameter. Diameter-based criteria fail to take into consideration the biomechanical properties of the aorta as well as other characteristics of the patient, and finite element analysis (in determining aortic wall stresses) would ideally address the issues associated with today's diameter-based criteria.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0979

68Index score

Recomputed live from the record · Oct 12, 2026, 9:15 AM