+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
TruaceTracing the truth around AIWednesday, August 26, 2026
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The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,155 results
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AI gains · 641

79
GainHealth· Newly added· Evidence: Moderate (1 source)

Robotic ultrasound systems improve reproducibility and global accessibility by decoupling the operator from the patient and using 5G telesonography to project diagnostic expertise.

This comprehensive review traces ultrasound from operator-dependent manual imaging to robotic ultrasound systems developed over the past two decades, including teleoperated telesonography over 5G and increasingly autonomous platforms using force control and path planning.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%100

Updated Aug 26, 2026 · TRV-2026-0898

79
GainHealth· Newly added· Evidence: Moderate (1 source)

Among 2453 tertiary-referred children with GDD, a Platt-calibrated L2 logistic regression integrating routine clinical, neurophysiological and genetic data stratified progression to ID with AUC 0.783 and near-ideal calibration, enabling high-PPV triage.

Researchers retrospectively analyzed 2453 children diagnosed with GDD between January 2014 and December 2023 at a provincial tertiary children's rehabilitation centre, followed to at least 60 months. Using 28 predictors across perinatal, developmental, neuroimaging, electrophysiological, genetic and comorbidity domains, they trained L2- and L1-regularized logistic regression, random forest, XGBoost and LightGBM, with Platt scaling for the L2 model, and evaluated on a held-out 30% test set.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%100

Updated Aug 25, 2026 · TRV-2026-0878

79
GainHealth· Stable· Evidence: Moderate (1 source)

Patients recovering from orthopedic surgery reported 80.3% willingness to use patient-facing AI systems for transitional care, with priorities shifting to functional safety and rehabilitation guidance after discharge.

Researchers surveyed 752 orthopedic surgery patients across 33 hospitals in Guangdong, China, asking them to rate standardized descriptions of AI functions such as chatbots, vision-based monitoring, and wearables for education, motion correction, and risk alerts during the hospital-to-home transition. By August 2026 publication, 80.3% reported willingness to use such systems, with care priorities moving from information and instructions in hospital to functional safety and rehabilitation support at home.

Impact 30%69
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%96

Updated Aug 5, 2026 · TRV-2026-0647

79
GainPolicy· Stable· Evidence: Moderate (1 source)

In benchmark experiments reported by May 2026, the unified framework increased supply chain disruption-prediction accuracy to 94.1%, reduced demand-forecast error, and raised marketing campaign ROI from 14.2% to 45.3%.

By May 12 2026, researchers reported development and benchmark testing of a Unified Business Intelligence framework that combines machine learning, predictive analytics, and explainable AI for supply chain management, financial risk assessment, and digital marketing. The paper draws on 43 studies from 2023-2026 and reports measured improvements in disruption prediction, demand forecasting, credit-risk classification, and campaign targeting within experimental settings.

Impact 30%69
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0134

AI problems · 514

78
ProblemHealth· Stable· Evidence: Moderate (1 source)

Clinical translation of AI mortality prediction after road traffic crashes remains limited by insufficient external validation, inconsistent handling of class imbalance, and incomplete reporting of tuning and missing data strategies.

A systematic review published 7 August 2026 examined 18 retrospective studies from 2014-2025 that used AI or machine learning to predict death after road traffic crashes, drawing mostly on national or regional databases, hospital records, and police or insurance tabular data.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%97

Updated Aug 10, 2026 · TRV-2026-0729

78
ProblemMedia & Arts· Stable· Evidence: Moderate (1 source)

The same system showed substantially weaker longer-range referential and narrative continuity, lacking the sustained plotting and thematic organisation observed in Orwell's novel.

Published 2026-08-04, this peer-reviewed comparative case study examines passages from George Orwell's Nineteen Eighty-Four and Ross Goodwin's 2018 sensor-driven LSTM book 1 the Road to assess how generative systems handle literary coherence and creative agency.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%96

Updated Aug 8, 2026 · TRV-2026-0694

78
ProblemHealth· Stable· Evidence: Moderate (1 source)

Medical AI systems built on Western biomedical traditions may inflict ontological harm and diminish trust in patient-clinician relationships by conflicting with relational and Indigenous understandings of health.

Published 6 August 2026 in the South African Medical Journal, this peer-reviewed essay examines data science and medical AI through four lightly fictional but reality-informed case studies from Bamenda to Mthatha to Toronto. It argues that AI tools largely built on Western biomedical traditions may conflict with relational, spiritual and Indigenous understandings of health, manifesting as epistemic friction and diminished trust.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%96

Updated Aug 8, 2026 · TRV-2026-0691

78
ProblemHealth· Stable· Evidence: Moderate (1 source)

High algorithmic accuracy in AI models for ADHD has not yet translated into routine clinical utility without advances in explainability and multimodal fusion.

A bibliometric review of 722 Scopus-indexed papers from 2011 to 2024 tracked how artificial intelligence has been applied to ADHD prediction. Using Python and VOSviewer, the authors found exponential growth peaking in 2023, a concentration of output in the United States and China, and a technological shift from support vector machines to deep learning with EEG becoming the favored data modality.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%95

Updated Aug 3, 2026 · TRV-2026-0630

Recomputed live from the record · Aug 26, 2026, 11:45 PM