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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

281
HealthNewly addedModerate evidence · 1 source

A three-module LLM agent system for bilingual clinical text expanded valid SNOMED CT candidates for expert mappers, raising hit rate@1 and R-precision while cutting per-segment mapping time by about half.

At a tertiary academic hospital in South Korea, researchers developed an LLM agent system to map bilingual free-text clinical narratives to SNOMED CT and tested it in a human-AI collaborative workflow against human-only mapping by three health information managers on 2,261 segments.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 4, 2026 · TRV-2026-1278

68Index score
282
ScienceNewly addedModerate evidence · 1 source

Applying network analysis and process mining to 52,412 YouTube comments distinguished scientific from pseudoscientific health discussions, showing mixed-valence evaluation in scientific threads versus affirmation bonding in pseudoscientific threads.

Researchers analyzed 52,412 YouTube comments from health treatment videos posted between 2011 and 2025, classified as scientific or pseudoscientific via an LLM-assisted pipeline, to test whether network analysis and process mining could map how conversations unfold.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 4, 2026 · TRV-2026-1277

68Index score
283
HealthNewly addedModerate evidence · 1 source

Scoping review of 35 studies found AI and digital tools applied to predictive models, imaging analysis, and continuous monitoring showed potential for early diagnosis and prediction of neonatal respiratory outcomes.

A scoping review published October 2, 2026 analyzed 35 studies from 2019-2026 on digital technologies and AI in neonatal respiratory assessment, finding primary uses in predictive models, medical image analysis, and continuous physiological monitoring with reported potential for early diagnosis and clinical decision support.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 4, 2026 · TRV-2026-1275

68Index score
284
ScienceNewly addedModerate evidence · 1 source

In a controlled test of 50 abstracts with fictional author identities, ChatGPT and Claude showed no consistent gender or geographic bias and achieved high scoring reproducibility.

In April 2026, researchers tested ChatGPT and Claude as peer reviewers by having each model score 50 general internal medicine abstracts twice under four fictional author identities, producing 800 evaluations of quality, novelty, and acceptance on a 0-10 scale.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 4, 2026 · TRV-2026-1274

68Index score

AI problems · 771

281
HealthStableModerate evidence · 1 source

Patients with stage II-III colon cancer whose CT body composition showed myosteatosis via machine learning analysis had significantly lower 5-year recurrence-free survival.

Researchers applied a machine learning model called Mosamatic to routine CT staging scans from 615 patients with stage II-III colon cancer treated between 2015-2021 at one center to quantify skeletal muscle area, density, and fat. They found myosteatosis was more common in those who later recurred and was linked to worse recurrence-free survival at 1 and 5 years.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 28, 2026 · TRV-2026-0920

68Index score
282
HealthStableModerate evidence · 1 source

Clinical use of AI in echocardiography carries risk of automation bias in high-volume settings, compounded by inconsistent performance across platforms.

A review published August 26, 2026 describes echocardiography workflows transitioning from manual acquisition and measurement to AI-driven interpretation, citing prospective evaluations where AI reduces examination time, automates measurements, and supports integrated assessments of ejection fraction, myocardial texture, and Doppler hemodynamics.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 28, 2026 · TRV-2026-0913

68Index score
283
HealthStableModerate evidence · 1 source

After accounting for HLA-DQ2.5 enrichment, machine-learning classification of celiac disease from naive TCR repertoires was abolished, and naive BCR repertoires failed to classify disease.

The study used machine learning on naive CD4+ TCR and naive BCR AIRR-seq repertoires to test classification of celiac disease versus controls, building on prior work linking germline HLA variation to naive repertoire composition and earlier BCR-based classification attempts.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 28, 2026 · TRV-2026-0912

68Index score
284
HealthStableModerate evidence · 1 source

AI lesion detection models trained on Western or Eastern DBT data showed reduced performance when applied to the other population due to differences in lesion types.

A study tested YOLO-based AI models for breast lesion detection on digital breast tomosynthesis using a 94-patient Western database from the Cancer Imaging Archive and a 157-patient Eastern database from a single medical center, with lesions grouped into six types.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0908

68Index score

Recomputed live from the record · Oct 12, 2026, 12:21 PM