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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 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.

933 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.

1,703 results
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AI gains · 933

153
HealthStableModerate evidence · 1 source

AIPatient simulated patient system using six LLM agents and a knowledge graph built from MIMIC-III achieved 94.15% EHR-based QA accuracy and accessible readability, with medical students rating it high fidelity and matching or exceeding human-simulated patients for history-taking.

On 2025-12-19, a peer-reviewed study in Communications Medicine described AIPatient, a simulated patient system powered by six LLM-based agents and a knowledge graph derived from MIMIC-III. Testing reported 94.15% accuracy on EHR-based medical QA, high validity, accessible readability scores, and stable performance across robustness tests, with a medical student user study finding high fidelity and educational value.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0427

73Index score
154
HealthStableModerate evidence · 1 source

ChatGPT provided satisfactory answers to common patient hip arthroscopy questions, with half of responses graded A and another 30% graded B by fellowship-trained surgeons.

In a study published June 22, 2024, two hip preservation surgeons graded ChatGPT 3.5 answers to ten common hip arthroscopy questions drawn from patient education sites, using an A-to-D scale and readability scores FRES and FKGL.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0412

73Index score
156
HealthStableModerate evidence · 1 source

Convolutional neural networks trained on four Scheimpflug-based corneal maps differentiated keratoconus from normal astigmatic eyes with up to 99.2% accuracy and AUC 1.00, with external validation retaining 97-98% accuracy.

By July 2026, a cross-sectional study at Al-Shifa Trust Eye Hospital in Pakistan developed four CNN models on 5602 Scheimpflug-derived corneal maps from 1411 eyes to distinguish keratoconus from normal eyes, reporting internal accuracies of 98.1% to 99.2% and AUCs up to 1.00, with external validation on 85 participants confirming 97.1% to 98.3% accuracy.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 18, 2026 · TRV-2026-0256

73Index score

AI problems · 770

153
HealthNewly addedModerate evidence · 1 source

No AI-designed polymeric-lipid nanoparticle formulation for breast cancer has entered registered clinical trials, leaving scalable production and regulatory clearance unresolved.

On October 3, 2026, a peer-reviewed assessment in International Journal of Pharmaceutics reviewed smart polymeric-lipid nanoparticles for breast cancer that combine ligand-functionalized targeting, stimuli-responsive release, and machine learning to optimize lipid-polymer ratios, drug loading, and predicted in vivo behavior.

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

Updated Oct 6, 2026 · TRV-2026-1298

68Index score
154
HealthNewly addedModerate evidence · 1 source

Review detected possible publication bias, found no studies reporting clinical endpoint data, and found abdominal radiograph triage models had substantially lower external accuracy, limiting readiness for clinical implementation.

This PROSPERO-registered systematic review and meta-analysis evaluated 11 studies comprising 37 model variants and 36,863 patients assessing machine learning for pediatric intussusception. Internally validated ultrasound models achieved pooled sensitivity 0.914 and specificity 0.980, with externally validated models showing sensitivity 0.946 and specificity 0.958, while abdominal radiograph triage models performed lower externally.

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

Updated Oct 6, 2026 · TRV-2026-1297

68Index score
155
HealthNewly addedModerate evidence · 1 source

LLM responses for orthopaedic anaesthesia showed variable consistency, with later models exhibiting greater partial variability despite avoiding fully contradictory outputs.

In a controlled prompting study of 34 orthopaedic anaesthesia questions, three LLMs and three expert anesthesiologists generated answers, with GPT-4o and GPT-5.2 tested both with and without clinical practice guidelines. An independent guideline-informed LLM judge performed blinded pairwise comparisons, recording preference, consistency and confidence.

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

Updated Oct 6, 2026 · TRV-2026-1296

68Index score
156
LifestyleNewly addedModerate evidence · 1 source

Higher artificial intelligence competence anxiety was indirectly associated with greater sleep disturbance through increased repetitive negative thinking and future anxiety.

On 2026-10-04, a peer-reviewed study in Behavioral Sleep Medicine reported an investigation of 364 adults aged 18-60 into how AI competence anxiety relates to sleep disturbance. Using two-step structural equation modeling with bootstrap tests and controlling for gender, age, and frequency of AI tool use, the authors found significant indirect associations through repetitive negative thinking and future anxiety.

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

Updated Oct 6, 2026 · TRV-2026-1294

68Index score

Recomputed live from the record · Oct 11, 2026, 7:09 PM