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

241
ScienceStableHigh evidence · 5 sources

Combining machine learning with multiscale modeling creates robust predictive models that integrate underlying physics to manage ill-posed problems and can provide insights into disease mechanisms and treatment strategies.

Published in November 2019, this perspective review argues that breakthrough data collection in biology and medicine requires new analysis strategies. The authors contend that machine learning and multiscale modeling are complementary and demonstrate how their integration can produce physics-aware predictive models that handle massive, heterogeneous datasets.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0204

70Index score
242
ScienceRisingHigh evidence · 5 sources

AlphaFold provides a computational method that regularly predicts protein three-dimensional structures with atomic accuracy from amino acid sequence alone, even without homologous structures.

On July 15, 2021, Nature published the AlphaFold study describing a redesigned neural network that predicts the three-dimensional structure a protein will adopt based solely on its amino acid sequence. The authors reported validation in CASP14, where the model regularly achieved atomic accuracy even when no homologous structure was available and performed competitively with experimental structures.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0203

70Index score
243
CrimeStableHigh evidence · 5 sources

XAI-driven data mining applied to IoT ecosystems can detect anomalies and support automated security decisions through transparent and interpretable reasoning for smart city infrastructure.

Published February 20, 2026, this peer-reviewed survey in Cognitive Computation reviews XAI-driven data mining for self-defending IoT systems. It describes how IoT expansion in smart cities, healthcare, and industrial automation creates need for real-time, scalable security, and how XAI methods aim to detect anomalies and support automated decisions with transparent reasoning.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0201

70Index score
244
EducationStableHigh evidence · 5 sources

Chinese EFL students who received robot-assisted language instruction showed significantly higher classroom engagement and willingness to attend classes after a four-month intervention compared to controls.

In a four-month randomized trial reported April 30, 2026, 155 Chinese EFL students were split into a control group of 80 and an experimental group of 75 that received robot-assisted language instruction. Questionnaires at the start and end measured classroom engagement and willingness to attend classes.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0200

70Index score

AI problems · 770

241
EducationStableModerate evidence · 1 source

In medical education, the same AI tools may reduce training safety by quietly reshaping how future clinicians think and act, functioning as a wedge that undermines training.

A 2026 peer-reviewed paper analyzes artificial intelligence in medical education, noting that AI is rapidly changing training by offering faster workflows and richer learning resources, while also quietly reshaping how future clinicians think and act.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 9, 2026 · TRV-2026-1036

68Index score
242
HealthStableModerate evidence · 1 source

Programming medical robots and AI to appear to have emotions risks creating machine paternalism, where simulated affect influences patients despite machines lacking real emotions.

As of the 2026-10-01 publication, the article describes that in medicine robots and AI machines are used that could be programmed to appear to express positive emotions such as joy, compassion and hope, despite not having emotions.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 9, 2026 · TRV-2026-1035

68Index score
243
HealthStableModerate evidence · 1 source

Machine learning models for autism spectrum disorder prediction that use data augmentation and feature selection have limited external validation and inadequate evaluation frameworks, reducing confidence in reported performance improvements and model generalizability.

This peer-reviewed review examined 26 studies from 2021 to 2024 on machine learning and deep learning for autism spectrum disorder prediction, focusing on data augmentation and feature selection methods. It categorized augmentation into conventional transformations and GAN-based synthetic generation, and feature selection into filter, wrapper, and embedded approaches, evaluating study quality with the Prediction Model Risk of Bias Assessment Tool.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 9, 2026 · TRV-2026-1032

68Index score
244
HealthStableModerate evidence · 1 source

Ambient AI documentation can introduce interpretive drift that alters patients' accounts as they enter the medical record, distorting clinical meaning and shaping downstream diagnostic decisions.

Published September 8, 2026 as an opinion piece, the article argues that ambient AI tools that summarize patient accounts and generate clinical notes can introduce interpretive drift, a subtle change in meaning between what the patient said and what is recorded.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 9, 2026 · TRV-2026-1030

68Index score

Recomputed live from the record · Oct 12, 2026, 6:31 AM