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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

157
SportsStableModerate evidence · 1 source

An AI decision support system using ID3 entropy and enhanced Monte Carlo tree search delivered real-time basketball strategy evaluation in about 3.13 seconds with 74% win rate and 84% decision rationality.

Researchers built a real-time strategic decision support system for sports that combines an ID3 decision tree using entropy change with an enhanced Monte Carlo tree search that picks the maximum UCT node. Tested in basketball contexts, the system averaged about 3.13 seconds per decision and was reported to reach 74% win rate and 84% decision rationality.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 17, 2026 · TRV-2026-0237

73Index score
158
HealthStableModerate evidence · 1 source

In a real-world care facility test by April 2026, the ZigBee-based AI telehealth framework monitoring elderly and chronically ill patients achieved 95% accuracy with 100% recall for health discrepancy detection while operating at 120 ms transmission delay and 3.8 mW/h power consumption.

Researchers built and tested a ZigBee-based wireless system that connects wearable sensors for heart rate, temperature and oxygen to cloud AI models including random forest, SVM and logistic regression. By April 2026, tests in a care facility reported 95% accuracy, 100% recall, 120 ms delay and 3.8 mW/h power use.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0143

73Index score
159
HealthStableModerate evidence · 1 source

Emergency departments (EDs) worldwide face increasing pressure to optimize triage processes amidst rising patient volumes and resource constraints. Artificial intelligence (AI) has emerged as a potential solution to enhance triage accuracy and efficiency, yet its real-world clinical impact remains inadequately characterized. We conducted a systematic review following Preferred Reporting Items f...

Emergency departments (EDs) worldwide face increasing pressure to optimize triage processes amidst rising patient volumes and resource constraints. Artificial intelligence (AI) has emerged as a potential solution to enhance triage accuracy and efficiency, yet its real-world clinical impact remains inadequately characterized.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
59
Recency 10%
82

Updated Jul 11, 2026 · TRV-2026-0018

73Index score
160
ClimateNewly addedHigh evidence · 5 sources

AI deployment was associated with reduced physical climate risks across 51 countries from 1996 to 2023.

A peer-reviewed study published October 1, 2026 analyzed 51 countries between 1996 and 2023 to test whether AI helps manage physical climate risks. Using a two-way fixed effects model, it reported that AI can govern such risks through sensing, seizing, and integrating actions.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1231

72Index score

AI problems · 770

157
HealthNewly addedModerate evidence · 1 source

An AI-based P-wave risk score derived from sinus-rhythm ECG does not establish an AF diagnosis and alone does not constitute an indication for treatment, risking overinterpretation in screening.

This narrative review examined conventional P-wave markers and AI-based ECG analysis for identifying an atrial risk phenotype that predicts occult or future atrial fibrillation despite a normal sinus-rhythm resting ECG.

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

Updated Oct 5, 2026 · TRV-2026-1286

68Index score
158
HealthNewly addedModerate evidence · 1 source

Model had limited sensitivity and calibration, with sensitivity dropping to 0.390 in temporal validation and Brier score worsening to 0.137, limiting clinical utility.

Using CHARLS data, researchers built an interpretable machine-learning framework to identify MCI at time of assessment in older adults with chronic conditions. After LASSO selection of 27 predictors, a prespecified XGBoost model achieved AUROC 0.692 internally in 2018 and 0.702 in 2020 temporal validation, with TreeSHAP highlighting number of living children, sleep duration, education, ADL and age.

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

Updated Oct 5, 2026 · TRV-2026-1283

68Index score
159
HealthNewly addedModerate evidence · 1 source

Sensitivity was heterogeneous and low for low-prevalence symptoms, 11 of 35 variables failed the reliability threshold under either pipeline, and neither pipeline supports individual-level decisions, with the generative model incurring far higher compute cost.

Researchers tested two automated pipelines on 110 randomly selected 2021 forensic medical certificates from 12 units of the French ORFeAD network, comparing a rule-based system using segmentation, lexicons and negation detection to a locally served Llama 3 8B model via Ollama, against physician coding of 35 binary variables.

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

Updated Oct 5, 2026 · TRV-2026-1282

68Index score
160
HealthNewly addedModerate evidence · 1 source

Deep learning beamforming models are constrained by limited diverse datasets, black-box interpretability, and overfitting risk that hinder reliable clinical adoption.

This peer-reviewed review from October 2026 surveys deep learning approaches to ultrasound beamforming, including CNNs, GANs, transformers and hybrid models, and evaluates them against traditional delay-and-sum methods. It also examines datasets, benchmarks, and hardware strategies such as FPGA acceleration and cloud-edge inference for real-time use.

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

Updated Oct 5, 2026 · TRV-2026-1281

68Index score

Recomputed live from the record · Oct 11, 2026, 7:24 PM