+ 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

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

AI and ML are being applied to improve energy efficiency and support climate mitigation through energy optimization, renewable integration, and carbon reduction.

By late 2024, a review of 237 publications from 2010 to 2024 found AI and ML increasingly studied as tools for energy efficiency and climate mitigation, with over 60% of papers appearing in the last two years and focus areas including sustainable construction and climate forecasting.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 19, 2026 · TRV-2026-0279

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

AI models predicted incident hypertension and cardiovascular risk from EHRs, wearables and multimodal data with AUCs around 0.75 to 0.90, supporting personalized therapy and remote monitoring.

A structured narrative review of literature from January 2015 to December 2025 examined AI for hypertension screening, diagnosis, risk stratification, treatment optimization and remote monitoring. It found ML models often outperformed conventional risk scores with AUCs of 0.75 to 0.90 and showed promise for personalized therapy and continuous monitoring.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%93

Updated Jul 19, 2026 · TRV-2026-0269

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

Synthetic relationships with AI companions could reduce loneliness by providing always-available, adaptive, emotionally responsive interaction that fosters companionship and lowers social anxiety.

This review examines generative AI-enabled synthetic relationships, defined as ongoing associations with AI companions designed to simulate human-like bonds, as a potential intervention for loneliness where traditional approaches face availability and scalability limits.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%92

Updated Jul 17, 2026 · TRV-2026-0254

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

Researchers developed the University Policy Development Framework for Generative AI to help universities assess priorities and build sustainable governance capacity.

Researchers conducted a cross-national analysis of generative AI guidelines issued by leading universities in the United States, Japan, and China, coding policies across five Technology Acceptance Model domains to identify 20 themes and to build the University Policy Development Framework for Generative AI (UPDF-GAI).

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%92

Updated Jul 17, 2026 · TRV-2026-0251

AI problems · 514

74
ProblemScience· Newly added· Evidence: Moderate (1 source)

The optimized ANN and Logistic Regression frameworks exhibited the highest overall discriminative power (AUC > 0.99), while the Random Forest algorithm achieved the peak classification accuracy (97.10%).

Estimating a biological profile, such as sex, is a fundamental step in forensic identification when primary identifiers are unavailable for direct individual comparison. In forensic scenarios involving advanced decay, specific taphonomic alterations, or midfacial blunt force impacts, the mandibular ramus serves as a valuable anatomical marker due to its distinct sexual dimorphism and thick cortical structure, making it more resilient to fragmentation than other, more fragile facial bones.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%99

Updated Aug 23, 2026 · TRV-2026-0851

74
ProblemHealth· Newly added· Evidence: Moderate (1 source)

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models: Elevated ACAG was substantially linked to a high risk of mortality in individuals with TLI (hazard ratio (HR) [95% confidence interval (CI)] = 1.115 [1.037-1.199]).

Background The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy is reduced in such patients due to low albumin levels.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%99

Updated Aug 22, 2026 · TRV-2026-0846

74
ProblemHealth· Newly added· Evidence: Moderate (1 source)

The model missed 21.2% of expert-defined high-risk cases with sensitivity of 78.8%, and three-level risk stratification accuracy was only 76.3% with 98.6% of errors being downward shifts that underestimate risk.

Researchers retrospectively tested ChatGPT on 300 histopathologically confirmed oral lichen planus cases with at least 24 months of follow-up, using serial clinical records, intraoral photographs, and histopathology reports. Compared with blinded expert panel consensus, the model achieved 94.7% accuracy for trajectory classification and 78.8% sensitivity with 99.6% specificity for high-risk detection as of the August 2026 publication.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 16, 2026 · TRV-2026-0788

74
ProblemHealth· Newly added· Evidence: Moderate (1 source)

When tuned for ≥95% sensitivity screening, the models suffered a steep parallel decline in specificity and cannot safely eliminate the need for lumbar puncture, with top non-linear models showing severe training optimism.

Researchers retrospectively analyzed 306 infants aged 1 to 90 days hospitalized between 2014 and 2022 in Khorasan Razavi, Iran, using CSF culture via lumbar puncture as the gold standard, to train nine machine learning classifiers on routine non-invasive paraclinical markers with nested cross-validation and SHAP interpretation.

Impact 30%69
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 16, 2026 · TRV-2026-0783

Recomputed live from the record · Aug 27, 2026, 2:52 AM