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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 934 sourced gains and 771 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.

934 gains
771 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.

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AI gains · 934

273
HealthNewly addedModerate evidence · 1 source

Random Forest and flag-based models integrating exposure, lipophilicity, mitotoxicity and cytotoxicity improved early DILI risk prediction with high specificity, supporting safer molecule triage to reduce hepatotoxicity-related attrition.

By the publication date of 2026-10-03, researchers reported a refined multiparametric in vitro strategy for early DILI risk mitigation built on 170 reference drugs with known clinical outcomes. They combined cytotoxicity, mitotoxicity and transporter inhibition assays with exposure data and machine learning, showing that a >=2 flag system and Random Forest models improved specificity and balanced accuracy for early compound triage.

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

Updated Oct 6, 2026 · TRV-2026-1295

68Index score
274
HealthNewly addedModerate evidence · 1 source

A Random Forest model trained on first-24-hour EMR data predicted severe influenza in hospitalized children with AUROC 0.88, outperforming five other models on decision curve analysis, with blood glucose ranked as top predictor.

Researchers conducted a retrospective cohort study of 436 hospitalized children with influenza, using electronic medical record data from the first 24 hours after admission to train and test six machine learning models. The Random Forest model achieved the highest performance with an AUROC of 0.88 and greater net benefit on decision curve analysis, with SHAP analysis highlighting blood glucose as the most significant predictive variable.

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

Updated Oct 6, 2026 · TRV-2026-1293

68Index score
275
HealthNewly addedModerate evidence · 1 source

A DenseNet121 model trained on 11,806 breast DWI slices can automatically flag hyper- and hypointense artifacts on high b-value images with high discrimination, supporting quality assessment for lesion detection.

Researchers developed convolutional neural networks to automatically flag intensity artifacts on high b-value breast diffusion-weighted MRI. Using 11,806 slices from 156 exams, DenseNet121 distinguished hyper- and hypointense artifacts with AUROCs of 0.91 and 0.95 on binary tasks and weighted AUROCs of 0.84 and 0.90 on multiclass tasks.

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

Updated Oct 6, 2026 · TRV-2026-1291

68Index score
276
HealthNewly addedModerate evidence · 1 source

AI models analyzing sinus-rhythm ECGs can identify atrial risk phenotypes and help select patients for prolonged rhythm monitoring to improve AF detection yield.

This narrative review examined conventional P-wave markers and AI-based ECG analysis for identifying an atrial risk phenotype that predicts occult or future atrial fibrillation despite a normal sinus-rhythm resting ECG.

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

Updated Oct 5, 2026 · TRV-2026-1286

68Index score

AI problems · 771

273
LifestyleStableModerate evidence · 1 source

Portable Raman spectroscopy coupled with machine learning for rapid identification and source apportionment of plastic particles in aquaculture wastewater: MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks.

Aquaculture expansion has exacerbated microplastics (MPs) contamination in aquaculture water bodies. MPs readily adsorb heavy metals and organic pollutants to form composite contamination and accumulate through food chains, imposing ecological and human health risks.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 2, 2026 · TRV-2026-0961

68Index score
274
HealthStableModerate evidence · 1 source

Among Medicare beneficiaries with cancer, enrollment in stand-alone Prescription Drug Plans versus integrated Medicare Advantage Prescription Drug plans remained associated with significantly higher Medicare and beneficiary out-of-pocket spending after AI-enabled causal adjustment.

Using Medicare Current Beneficiary Survey data linked to claims from 2019 to 2022, researchers studied 3,140 cancer patients aged 65 and older representing 22.2 million beneficiaries to estimate the causal effect of stand-alone Prescription Drug Plans versus integrated Medicare Advantage Prescription Drug plans. They compared conventional regression, two-stage residual inclusion instrumental variables, and an AI-enabled Doubly Robust Machine Learning IV approach using county-level PDP penetration and white-collar worker percentage as instruments.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0958

68Index score
275
HealthStableModerate evidence · 1 source

Use of AI to automate prior authorization raises concerns about transparency, bias, and overreliance, with payer-deployed systems potentially denying claims without adequate clinical review.

Published September 1, 2026 in Journal of Managed Care & Specialty Pharmacy, this viewpoint proposes a pharmacist-overseen, AI-enabled prior authorization model for provider organizations and health systems. It outlines a conceptual workflow where AI automates routine data extraction and submissions and routes complex cases to pharmacists for clinical verification.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0956

68Index score
276
HealthStableModerate evidence · 1 source

In the same vignettes, experts identified safety risks in AI-generated discharge instructions not found in nurse texts, and nurses significantly outperformed AI on empathy and readability.

A prospective double-blind vignette study compared discharge instructions created by GPT-4 and by five registered nurses across five standardized scenarios. Fifteen experts rated accuracy and 38 patients rated empathy and readability, with NLP analysis of text complexity, using paired tests and a generalized linear mixed model.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0952

68Index score

Recomputed live from the record · Oct 12, 2026, 11:14 AM