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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 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.

939 gains
772 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.

1,711 results
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AI gains · 939

485
CrimeStableModerate evidence · 1 source

A bagging ensemble combining random forest and multilayer perceptron improved landscape ecological vulnerability mapping for riverbank erosion, reaching 0.97 AUC and enabling targeted land management for disaster risk reduction.

On August 15, 2026, a peer-reviewed study reported a hybrid ensemble machine learning framework for assessing landscape ecological vulnerability to riverbank erosion. Using random forest, multilayer perceptron and bagging classifiers with multicollinearity-selected environmental and geomorphological parameters, the bagging ensemble achieved 0.97 AUC and 0.91 accuracy, mapping over half the area into high or very high vulnerability zones in Bihar and West Bengal.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0807

67Index score
486
HealthStableModerate evidence · 1 source

A CoxBoost + survivalSVM prognostic model built from resistance-associated cluster genes achieved C-index 0.686 and stratified LUAD patients with HR 2.54-10.51, with low-risk patients showing greater immune infiltration and ARNTL2 knockdown suppressing proliferation and invasion in A549 and H1299 cells.

By August 2026, researchers had used single-cell RNA sequencing of lung adenocarcinoma patients treated with neoadjuvant immunotherapy to map resistance-associated heterogeneity, identifying a malignant Cluster 2 enriched in non-responders with upregulated KRT17, S100A2, and CST6, and built a CoxBoost combined with survivalSVM prognostic model validated across seven independent cohorts.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0806

67Index score
487
HealthStableModerate evidence · 1 source

AI-enabled intervention delivery for adult cancer survivors was associated with reported improvements in clinical and psychosocial outcomes, with model performance generally moderate to high across symptom tasks.

This scoping review examined 41 empirical studies published from 2015 to March 2026 on AI for cancer symptom management in adult survivors. Twenty-one studies focused on model development using mainly unstructured electronic health record data, 18 on AI-enabled delivery using patient-reported inputs, and 2 on both, employing natural language processing, machine learning, and conversational AI for detection, monitoring, triage, decision support, personalised management, education and counselling.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0805

67Index score
488
ClimateStableModerate evidence · 1 source

Ensemble ML with SHAP clustering achieved R2=0.913 on 2020-2023 regulatory data and identified ten recurrent environmental settings driving PM10, enabling interpretable analysis without chemical speciation.

Researchers applied an explainable AI framework to four years of routine air-quality and meteorological data from a single urban monitoring station to move beyond concentration-only analysis of PM10. The best ensemble model reached R2=0.913, and SHAP-based clustering revealed ten recurrent environmental settings linked to enhancement, reduction, or transitional PM10 behavior.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 17, 2026 · TRV-2026-0803

67Index score

AI problems · 772

485
HealthStableModerate evidence · 1 source

As of the June 2025 search cutoff, evidence did not support claims that AI delivers precision medical education through OSCEs, with weaker performance in relational, situated, and culturally mediated competencies.

By July 2026, this scoping review had mapped the literature on AI in OSCEs, screening 421 records and including 22 studies across health professions education. It categorized uses in preparation, station construction, scoring, and delivery, and stratified findings by evidence maturity using FACETS, SAMR, and P4 frameworks.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0325

67Index score
486
HealthStableModerate evidence · 1 source

MMAI lost independent prognostic value after adjustment for CAPRA clinical risk score and did not outperform established clinical tools for postprostatectomy outcome prediction.

By July 2026, investigators had tested a multimodal AI model originally validated in prostate biopsy specimens on a tissue microarray of 424 prostatectomy cases, generating scores for 414 patients. At 10 years, recurrence-free survival was 74% and metastasis-free survival 96%, and MMAI scores were associated with both endpoints in univariable models.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0324

67Index score
487
HealthStableModerate evidence · 1 source

At the low-volume tertiary care hospital, implementing PhenoMATRIX alone without a timely release workflow was associated with increased time to result reporting due to delays between result availability and reporting.

By July 10 2026, a dual-center Canadian study reported before-and-after results for PhenoMATRIX, an AI-based software that provides continuous culture sorting and interpretation support for urine cultures on laboratory automation. Both a low-volume tertiary hospital and a high-volume community lab saw earlier availability of interpretable results, with measured TTRR changes of approximately 1.3 hours with PM+ automated release and approximately 5.3 hours with earlier manual review.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0322

67Index score
488
HealthStableModerate evidence · 1 source

GPT-4o showed optimism and positional biases in risk-of-bias assessment because it relied on probabilistic language patterns rather than structured clinical reasoning.

By December 2025, researchers conducted a systematic review and meta-analysis of 13 trials (n=684) of five emerging biologics for osteogenesis imperfecta, using GPT-4o to perform parallel title/abstract and full-text screening and to assist risk-of-bias assessment. The AI workflow achieved 97.4% sensitivity at abstract level and 88.9% at full-text, reducing total screening time by over 95% with substantial agreement to humans (kappa 0.778).

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0321

67Index score

Recomputed live from the record · Oct 13, 2026, 1:21 PM