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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.

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AI gains · 933

161
HealthStableModerate evidence · 1 source

Results The AI model achieved an overall accuracy of 72.2%, a macro F1 score of 0.64, and a weighted F1 score of 0.73 compared with psychiatrists' ratings.

Objectives This study aimed to contribute to the development of an AI-based system that supports worker health and productivity by enabling early detection of presenteeism. We tested whether an AI model could assess mental health-related presenteeism with accuracy comparable to that of psychiatrists and whether the frequency of application use was comparable between avatar-based and real-person interfaces.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1161

72Index score
162
ScienceStableModerate evidence · 1 source

GPT-4.1 scored poster images alone and predicted which AAST abstracts reached publication with 58.5% overall accuracy, rising to 74.2% in Violence, Societal, and Behavioral and 62.2% in Hemorrhage, Resuscitation, and Vascular Control.

Researchers tested whether GPT-4.1 could predict publication from poster images alone for 260 abstracts presented at the 2021-2022 AAST Annual Meetings. By September 2026 publication date, 142 had published, and the model achieved 58.5% accuracy overall, with higher accuracy in Violence, Societal, and Behavioral and Hemorrhage, Resuscitation, and Vascular Control, but chance-level performance in other domains.

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

Updated Sep 16, 2026 · TRV-2026-1114

72Index score
163
HealthStableModerate evidence · 1 source

In implant dentistry, MR-based dynamic navigation achieved sub-millimetric entry deviation and outperformed freehand technique, with up to 72% angular accuracy improvement for inexperienced operators and more conservative tissue removal in endodontic and prosthetic tasks.

By September 2026, a scoping review of literature up to June 2025 synthesized 15 studies on augmented and mixed reality as auxiliary tools in dentistry and their integration with AI. Most evidence was in implant dentistry, where MR-based dynamic navigation achieved sub-millimetric accuracy and outperformed freehand technique, with reported improvements in angular accuracy for inexperienced operators and more conservative tissue removal in endodontics and tooth preparation.

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

Updated Sep 14, 2026 · TRV-2026-1082

72Index score
164
HealthStableModerate evidence · 1 source

An XGBoost model using 43 routine preoperative variables estimated 1-year mortality after total knee and hip arthroplasty with AUROC 0.761 and stable calibration, stratifying patients so the top 5% had 6.2-fold higher mortality than baseline.

In a study published September 9, 2026, investigators built a machine learning calculator to estimate 1-year mortality after primary total knee and hip arthroplasty using 43 routinely available preoperative variables from the TriNetX Research Network. On internal validation the model achieved AUROC 0.761 with Brier score 0.006, and stratified risk monotonically from 0.574% overall to 3.57% in the top 5% of predicted risk.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1050

72Index score

AI problems · 770

161
HealthNewly addedModerate evidence · 1 source

Risk of bias, heterogeneity, and limited external testing restrict confidence that reported accuracy will translate to routine clinical use.

A systematic review and meta-analysis of imaging AI for avascular necrosis of the femoral head pooled nine estimates and reported sensitivity of 0.888, specificity of 0.937, and SROC AUC of 0.967, with MRI-based models showing higher sensitivity than radiography-based models.

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

Updated Oct 5, 2026 · TRV-2026-1280

68Index score
162
ScienceNewly addedModerate evidence · 1 source

YouTube health videos serve as significant vectors for misinformation and pseudoscience, with sequential user interactions that can shape belief formation and community dynamics.

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%
99

Updated Oct 4, 2026 · TRV-2026-1277

68Index score
163
HealthNewly addedModerate evidence · 1 source

Generative AI tools are being used to create or manipulate sexually explicit images using children's likenesses without physical contact, enabling commercial sexual exploitation that can occur without children's awareness and contribute to ongoing psychological harm while complicating clinical recognition and response.

A peer-reviewed article published October 2, 2026 describes how generative artificial intelligence is being used to create or manipulate sexually explicit images using children's likenesses, forming AI-generated CSAM that can be commercialized as CSEC even without physical contact with the child.

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

Updated Oct 4, 2026 · TRV-2026-1276

68Index score
164
HealthNewly addedModerate evidence · 1 source

Same body of studies was found to be not yet ready for widespread clinical implementation due to lack of robust external validation, small samples, and absence of standardized protocols.

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%
99

Updated Oct 4, 2026 · TRV-2026-1275

68Index score

Recomputed live from the record · Oct 11, 2026, 8:09 PM