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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

169
HealthStableModerate evidence · 1 source

A CatBoost model integrating 22 granular nursing and emergency department features collected within the first 24 hours improved early in-hospital mortality prediction for acute ischemic stroke patients, achieving higher discrimination than established ICU scores in both internal and external validation.

Researchers developed and externally validated an interpretable machine learning framework to predict in-hospital mortality in acute ischemic stroke using high-granularity bedside data from the first 24 hours. Using 5,014 patients from three tertiary centers between 2019-2023, the best CatBoost model with 22 features achieved AUC-ROC 0.917 internally and 0.891 externally, outperforming traditional ICU scores.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 28, 2026 · TRV-2026-0919

72Index score
170
HealthStableModerate evidence · 1 source

AI-based ICE module created left atrial shell from the right atrium without a pre-ablation mapping catheter and guided pulsed field ablation with 100% acute success and 77.7% paroxysmal and 71.8% non-paroxysmal freedom from arrhythmia at 270 days after blanking.

Researchers tested a no-pre-mapping workflow using the AI-based CARTOSOUND FAM module to build a three-dimensional left atrial shell from intracardiac echocardiography acquired in the right atrium, then used that shell alone to guide pulsed field ablation with a Variable Loop Circular Catheter. In 210 patients, including 76 with concomitant left atrial appendage occlusion, all pulmonary vein isolations were completed with frequent additional posterior wall and superior vena cava lesions.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0902

72Index score
171
SportsStableModerate evidence · 1 source

Using the fixed threshold (score > 5 = survival), clinicians achieved higher overall accuracy than AI (75.6% periodontists, 74.9% GDs, 69.2% AI; p < 0.05), with sensitivity low and comparable across groups (14.7%-22.7%).

To compare the prognostic performance of an artificial intelligence (AI) model with that of experienced clinicians in predicting tooth loss over a 10-year period. An AI model trained on structured clinical and radiographic data was compared with 12 periodontists and 11 general dentists (GDs), who independently assigned prognostic scores (0-10 scale) to 300 teeth with known 10-year outcomes.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 23, 2026 · TRV-2026-0856

72Index score
172
ClimateStableModerate evidence · 1 source

Integrating an NDVI-based Productivity Index with regional prediction uncertainty to select four field samples reduced mean field-scale SOC prediction error from 0.24% to 0.18% RMSE, achieving accuracy comparable to using all available field observations.

Researchers tested a field-scale targeted sampling strategy inside a regional hybrid model that combines machine learning and geostatistics for soil organic carbon mapping. The method pairs a long-term satellite NDVI-based Productivity Index with regional prediction uncertainty to choose sampling locations, with new observations incorporated only via local residual kriging. Across 28 agricultural fields, the regional model alone averaged 0.24% SOC RMSE, while adding all field samples reached 0.17% and the four-sample targeted approach reached 0.18%.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 18, 2026 · TRV-2026-0823

72Index score

AI problems · 770

169
HealthNewly addedModerate evidence · 1 source

Computer-assisted navigation remains limited by registration inaccuracy and manual dependence, and robot-assisted surgery is constrained by longer setup times, high costs, and predominantly low-level evidence from small series and cadaveric studies.

This peer-reviewed review from October 2026 synthesizes digital orthopedic techniques for scaphoid fractures, including AI-based radiograph interpretation, finite element analysis, virtual surgical planning, navigation, robotics, and 3D printing for guides and custom prostheses.

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

Updated Oct 3, 2026 · TRV-2026-1259

68Index score
170
HealthNewly addedModerate evidence · 1 source

AI shows limitations in contextual reasoning, cultural sensitivity, and clinical safety when applied to mental health navigation roles that rely on lived experience.

As AI expands across mental health services, this peer-reviewed article asks whether it can replace digital health navigators with lived experience and concludes the role resists full automation. It identifies least-substitutable strengths in service knowledge, cultural grounding, and trust, and proposes a conavigation model where AI supports triage, documentation, and engagement monitoring.

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

Updated Oct 3, 2026 · TRV-2026-1257

68Index score
171
ScienceNewly addedModerate evidence · 1 source

Eco-smart biocontrol using AI-assisted design faces large-scale adoption barriers including inconsistent field performance and limited microbial survival and competitiveness.

Published October 1 2026, this peer-reviewed review synthesizes ecological, molecular, and technological work on eco-smart biocontrol, positioning the rhizosphere as a hotspot for plant-microbe interactions and describing how multi-omics and artificial intelligence-assisted predictive microbiome design are used to discover and design microbial biocontrol agents.

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

Updated Oct 2, 2026 · TRV-2026-1249

68Index score
172
HealthNewly addedModerate evidence · 1 source

Integration of AI into mental health care raised consistent concerns about privacy, algorithmic bias, lack of transparency, and erosion of therapeutic relationships in psychiatric nursing.

This October 2026 scoping review examined how AI tools such as chatbots, large language models, digital therapeutics and clinical decision-support systems are being applied in mental health care, synthesizing 42 sources from January 2022 to May 2026 to assess implications for psychiatric mental health nursing.

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

Updated Oct 2, 2026 · TRV-2026-1248

68Index score

Recomputed live from the record · Oct 11, 2026, 9:18 PM