− MEDIA & ARTS The article is devoted to a comprehensive analysis of modern problems of protecting musical works in the cont…+ HEALTH Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (…± HEALTH Rationale and objectives The international application of artificial intelligence (AI) for lesion detection b…± HEALTH Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis…± HEALTH Objectives To conduct a nationwide survey among professionals working in oncology departments in China to inv…+ HEALTH The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area…± HEALTH BACKGROUND . Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) me…+ SCIENCE Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent de…
TruaceTracing the truth around AIThursday, August 27, 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.

1,169 results
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AI gains · 649

68
GainScience· Newly added· Evidence: Moderate (1 source)

AI-assisted analysis of interview data from a quality improvement evaluation generated four themes that were replicable and grounded in the data.

In a quality improvement program evaluation, researchers used artificial intelligence to identify themes in interview data. By the publication date of 2026-08-24, the approach had produced four replicable themes grounded in the data, while also generating two consistently identified themes that were subtle misrepresentations.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%99

Updated Aug 25, 2026 · TRV-2026-0881

68
GainHealth· Newly added· Evidence: Moderate (1 source)

Machine learning and deep learning models trained on MALDI-TOF spectra improved rapid classification of bacteria versus viruses, Gram-positive versus Gram-negative, and individual species, with Extra Trees showing best generalization to an external highly pathogenic bacteria dataset.

Researchers combined MALDI-TOF mass spectrometry with eight machine learning and two deep learning models trained with 5-fold cross validation on 255 spectra from seven bacteria and five viruses, then tested three top performers against an external Robert Koch Institute database of highly pathogenic bacteria.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%99

Updated Aug 25, 2026 · TRV-2026-0880

68
GainHealth· Newly added· Evidence: Moderate (1 source)

A lifecycle-oriented operational framework with nine measures was proposed to engineer transparency into AI-enabled medical devices to improve regulatory readiness and calibrated clinical trust.

Published August 2026, this peer-reviewed synthesis addresses transparency as a foundational condition for trustworthy AI in healthcare. It finds current methods to operationalize transparency across AI-enabled medical devices are fragmented, and proposes a SaMD lifecycle framework to map regulatory and standards requirements to concrete development and governance steps.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%99

Updated Aug 25, 2026 · TRV-2026-0879

AI problems · 520

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

AI integration may reinforce health inequities for marginalised populations in Sub-Saharan Africa due to infrastructure gaps, algorithmic bias, under-representation of African datasets, and weak governance.

This scoping review mapped evidence published up to March 2026 on AI in healthcare in Sub-Saharan Africa, focusing on marginalised populations. Searching four databases and grey literature, the authors included 23 sources and synthesised them thematically.

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

Updated Aug 4, 2026 · TRV-2026-0645

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

AI-enabled sleep estimation tools raise concerns about racial bias in pulse oximetry, regulatory gaps, variable accuracy, and privacy, requiring further validation to ensure equitable and reliable clinical use.

By August 2026, peer-reviewed guidance for neurologists described a shift in obstructive sleep apnea diagnosis from in-laboratory polysomnography alone to home testing augmented by wearables, nearables, and FDA-cleared software-as-a-medical-device platforms that leverage artificial intelligence and multisignal integration to estimate sleep parameters. The article framed these tools as improving accessibility for patients unable or unwilling to undergo lab studies.

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

Updated Aug 4, 2026 · TRV-2026-0644

68
ProblemClimate· Stable· Evidence: Moderate (1 source)

The same presence-prediction model failed to identify 2 of 5 APP-contaminated field sites and could not predict particle concentration with sufficient accuracy.

On 2026-08-03, a peer-reviewed study reported a random forest model trained on 16S rRNA gene sequencing from a field mesocosm to predict antifouling paint particle contamination in sediment. In lab incubation tests it identified 100% of presence samples and 83.3% of absence samples, and when applied to 14 Baltic Sea and Warnow estuary sites it correctly labeled all uncontaminated sites and three of five contaminated sites.

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

Updated Aug 4, 2026 · TRV-2026-0643

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

In prostate cancer and MRI subgroups, AI models showed lower sensitivity than clinicians, indicating inconsistent advantage across tasks.

A meta-analysis of 110 studies up to June 2026 evaluated AI algorithms for diagnosing urological cancers on CT, MRI, and ultrasound, pooling sensitivity, specificity, and AUC and comparing to clinician performance where reported.

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

Updated Aug 4, 2026 · TRV-2026-0640

Recomputed live from the record · Aug 27, 2026, 8:55 PM