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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 932 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.

932 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.

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AI gains · 932

93
HealthStableModerate evidence · 1 source

Compared to unassisted diagnosis, AI significantly improved pooled sensitivity (87%, 95% confidence interval [CI]: 84-89%, versus 73%, 95% CI: 69-78%) and maintained high specificity (95%, 95% CI: 92-97%, versus 94%, 95% CI: 89-96%).

Objective We systematically evaluated the diagnostic performance of artificial intelligence (AI)-assisted interpretation versus independent physician assessment for fracture detection. Materials and methods Adhering to PRISMA-DTA guidelines, we searched PubMed and Web of Science for original studies published up to September 17, 2025.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1145

74Index score
94
ScienceStableModerate evidence · 1 source

HyLnc combining transformer embeddings with handcrafted biological features improved lncRNA prediction to 91.3% accuracy on independent validation, outperforming existing tools.

On 2026-09-16, a peer-reviewed study described HyLnc, a framework that merges transformer-based contextual embeddings from a BERT model pre-trained on metazoan RNA with biologically meaningful sequence features for lncRNA prediction.

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

Updated Sep 17, 2026 · TRV-2026-1122

74Index score
95
HealthStableModerate evidence · 1 source

A reliability-oriented hybrid framework combining a periocular ResNet18 and a facial ensemble of ResNet50, EfficientNet-B0 and DenseNet121 increased early ASD risk indication to 90% sensitivity in the periocular pathway, 87.1% sensitivity with 0.948 AUC in the facial pathway, and an analytically estimated 98.71% system

Researchers developed a hybrid deep learning system for early autism spectrum disorder risk indication that fuses a ResNet18 model trained on static periocular images with a multi-CNN facial ensemble of ResNet50, EfficientNet-B0 and DenseNet121, combining outputs through an OR-based reliability rule and using Grad-CAM to highlight decision regions.

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

Updated Sep 16, 2026 · TRV-2026-1111

74Index score
96
HealthStableModerate evidence · 1 source

Combining Aidoc AI with a human radiologist increased sensitivity for intracranial hemorrhage on non-contrast head CT to 96.0% while maintaining 99.4% specificity, detecting cases missed by radiologists alone.

In a retrospective study of 4027 consecutive non-contrast head CT examinations from an emergency hospital in southwest Sweden, researchers compared three commercial AI algorithms for intracranial hemorrhage detection against reports from two radiologists, using two-tier consensus adjudication as the reference standard for 385 positive or discrepant cases.

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

Updated Sep 16, 2026 · TRV-2026-1107

74Index score

AI problems · 770

93
HealthStableModerate evidence · 1 source

Model performance may be influenced by underlying pulmonary abnormalities, and lung-level localization was substantially lower for left-lung pneumothorax at 67.6% compared to right-lung.

Researchers developed and evaluated a ResNet-18-based deep learning model to detect pneumothorax on supine neonatal chest radiographs using a retrospective dataset from a single NICU between 2011 and 2024. The model was pre-trained on normal adult radiographs and tested on held-out neonatal cases with patient-level splitting and a fixed threshold from cross-validation.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
95

Updated Sep 14, 2026 · TRV-2026-1085

72Index score
94
HealthStableModerate evidence · 1 source

Heterogeneous NDC, Multum, and RxCUI identifiers in real-world EHRs undermined semantic consistency, with over half of records needing string reconciliation and up to 57.4% requiring correction due to branded formulation omissions and indication- or route-based ATC ambiguities.

Researchers developed and tested an informatics framework to convert heterogeneous discharge medication identifiers from EHRs of adults 65 and older at Buffalo General Medical Center between 2020 and 2024 into standardized RxCUI ingredient and ATC class codes. Of 214,080 records, 53% were nonstandardized Multum IDs requiring string-based reconciliation, and the team measured mapping success and correction needs after deterministic crosswalks and expert validation.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0957

72Index score
95
HealthStableModerate evidence · 1 source

69% of studies showed high risk of bias from small sample sizes, patch-level data partitioning, and no external test sets, raising concerns about overfitting and data leakage.

This PRISMA 2020 systematic review of thirteen studies from 2016-2025 examined machine and deep learning for Hirschsprung disease diagnosis from histopathological images, evaluating architectures, workflows, and performance with QUADAS-AI and PROBAST.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0942

72Index score
96
HealthStableModerate evidence · 1 source

Automated matching failed for 16.5% of persisting findings and performance fell to 72.8% in participants with more than five nodules, with prospective validation in diverse populations still needed.

Researchers tested a pulmonary AI system for fully automated longitudinal nodule matching in 361 UK Lung Cancer Screening trial participants who had 3-month follow-up low-dose CT. Using a >=100 mm3 solid-component threshold, the AI found 378 baseline nodules in 181 participants; 39 resolved, and it matched 283 of 339 persisting nodules for an 83.5% success rate, with 91.8% success in single-nodule cases and 72.8% when more than five nodules were present.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0933

72Index score

Recomputed live from the record · Oct 11, 2026, 10:43 AM