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

125
HealthStableModerate evidence · 1 source

Among Medicare fee-for-service beneficiaries aged 65+ with ADRD hospitalized in 2023, greater hospital adoption of patient-related AI/ML tools was associated with lower odds of frequent hospitalizations, 30-day readmissions, and preventable acute hospitalizations, with inpatient risk prediction tools also linked to no

A 2023 cross-sectional study of 340,509 Medicare fee-for-service beneficiaries aged 65 or older with Alzheimer's disease and related dementias examined whether hospital adoption of patient-related AI/ML tools was associated with inpatient utilization and spending, using four adoption indicators for predicting inpatient risks, identifying high-risk outpatients, monitoring health, and recommending treatments.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
95

Updated Sep 15, 2026 · TRV-2026-1092

73Index score
126
HealthStableModerate evidence · 1 source

An AI system combining text normalisation with 1931 features maintained stable self-harm detection in prospective validation at its development metropolitan hospital, achieving PR AUC 0.84 over 329,655 triage notes in the following four years.

Researchers validated a previously developed AI system that detects self-harm in emergency department triage notes using extensive text normalisation and 1931 features. They tested it prospectively on 329,655 notes from the original major metropolitan hospital in Melbourne and externally on 316,877 notes from a regional hospital 150 km away covering 2012-2021.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
95

Updated Sep 13, 2026 · TRV-2026-1072

73Index score
127
HealthStableModerate evidence · 1 source

In 2530 paired urinalysis-culture records from three university hospitals, gradient-boosting models estimated culture positivity after urinalysis, with CatBoost achieving test-set AUC 0.858 and high specificity at the reported threshold.

Researchers developed and internally validated machine-learning models to estimate the probability of urine-culture positivity using routinely collected urinalysis data from 2530 sample records across three university hospitals. Using a stratified 75:25 sample-level split, 13 supervised algorithms were tested, with CatBoost showing the highest test-set AUC of 0.858 (95% CI 0.829-0.892) and similar performance to other gradient-boosting models.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1053

73Index score
128
HealthStableModerate evidence · 1 source

District-wide implementation of an AI-enabled wound app with virtual command centre produced high patient satisfaction and perceived benefit, including improved communication and self-management confidence among app users.

Between January 2024 and January 2026, a health district in Australia implemented a digital wound model of care combining an AI-enabled app with a virtual command centre across four hospitals and five community health centres. A post-implementation evaluation surveyed and interviewed 94 patients, 75 frontline clinicians, 9 senior wound nurses and a product manager, reviewing governance minutes to assess acceptability and perceived benefit.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0949

73Index score

AI problems · 770

125
PolicyFallingHigh evidence · 5 sources

Generative AI systems create risks of privacy loss, copyright infringement, misinformation, bias, and deepfake synthetic media that threaten truth, trust, and democratic values.

On 2024-08-09, a peer-reviewed paper in Informatics reported a systematic review of 37 sources on generative AI ethics, identifying concerns spanning privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities, with particular attention to convincing deepfakes and synthetic media.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0369

70Index score
126
PolicyStableHigh evidence · 5 sources

Lifelong learning systems in Singapore and Sweden face increased pressure due to AI-driven skills shortages and mismatches in the digital transition.

Published February 12, 2026, this peer-reviewed comparative case study examines how Singapore and Sweden organize lifelong learning to address demands for basic and advanced AI skills. It compares policy and practice at system, institutional, and programme levels.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0365

70Index score
127
ScienceStableHigh evidence · 5 sources

Widespread use of ChatGPT and other generative AI has raised potential ethical issues in high-stakes health care applications, but ethical discussions have not yet been translated into operationalisable solutions.

Published September 17, 2024, this scoping review in The Lancet Digital Health examined ethical discussions surrounding generative AI in health care, including ChatGPT and other models used to synthesise data such as images for research and practical purposes. The authors found that ethical concerns have been widely noted but not translated into operational solutions.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0360

70Index score
128
HealthStableHigh evidence · 5 sources

AI-enabled nanomedicine development faces persistent challenges with data quality, interpretability, and generalizability that hinder reproducible synthesis and reliable clinical translation.

Published March 17, 2026, this peer-reviewed review in BioNanoScience examines how artificial intelligence and machine learning are used to design and characterize nanoparticles for medical use. It describes AI models that predict physicochemical attributes, optimize synthesis conditions, and analyze characterization data to improve targeted therapeutics.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0356

70Index score

Recomputed live from the record · Oct 11, 2026, 2:55 PM