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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 929 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.

929 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.

1,699 results
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AI gains · 929

13
ClimateStableModerate evidence · 1 source

Explainable AI-Supported Cyber-Physical Collaboration for Sustainable Manufacturing in Industry 5.0: Experimental findings show that the proposed CNN-Transformer architecture achieves 98.5% accuracy and outperforms existing CPHS systems while maintaining low latency.

In this study, we present an innovative approach to the sustainable manufacturing of industrial parts using an explainable artificial intelligence (XAI)- based cyber-physical collaboration system for Industry 5.0. Current cyber-physical human systems (CPHSs) have been found to integrate AI only to a limited extent and often lack explainability.

Impact 30%
69
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
93

Updated Sep 3, 2026 · TRV-2026-0970

79Index score
15
HealthNewly addedModerate evidence · 1 source

Patients using guideline-grounded rheumatology chatbots reported high usability and satisfaction, with most answers rated safe and correct in real-world deployment.

Researchers co-developed ten disease-specific chatbots grounded in German rheumatology guidelines and deployed them through 13 rheumatology centres and six patient organisations from September 2025 to January 2026. They analyzed 6291 user questions, 2671 user ratings of responses, and 602 questionnaire responses, plus a six-dimensional LLM-as-a-judge assessment compared with rheumatologist ratings in subsets.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
99

Updated Oct 6, 2026 · TRV-2026-1292

78Index score
16
HealthNewly addedModerate evidence · 1 source

In patients with advanced NSCLC receiving first-line immune checkpoint inhibitors, higher baseline serum albumin predicted by machine learning models was associated with significantly prolonged time to next treatment or death.

On 2026-10-03, a peer-reviewed study reported development and external validation of machine learning models, including Elastic Net, to predict immune checkpoint inhibitor-induced hypothyroidism in advanced non-small cell lung cancer using two nationwide Japanese claims databases. Baseline TSH was the strongest predictor, while serum albumin consistently emerged as an important predictor across models.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
99

Updated Oct 5, 2026 · TRV-2026-1285

78Index score

AI problems · 770

13
HealthStableModerate evidence · 1 source

Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis: Cognitive performance and genetic risk scores for multiple sclerosis severity did not differ across subtypes, but within each subtype advancing SuStaIn stage was associated with worse global and domain-specific cognitive performance (all p≤0.030).

Multiple sclerosis is characterized by marked biological heterogeneity that is only partly captured by conventional clinical phenotypes and age-at-onset categories. MRI can detect focal lesions, diffuse microstructural damage and atrophy, but these measures are usually considered separately.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
97

Updated Sep 26, 2026 · TRV-2026-1187

78Index score
14
ScienceStableModerate evidence · 1 source

AI was primarily applied for health outcome prediction and risk stratification, whereas comparatively limited attention was directed toward preventive applications such as exposure monitoring and noise control.

Occupational noise remains one of the most prevalent workplace hazards worldwide and is associated with a wide range of adverse health effects, particularly noise‑induced hearing loss (NIHL). Recently, artificial intelligence (AI) has been increasingly applied in occupational noise research; however, the scope and focus of these approaches have not been systematically mapped.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
96

Updated Sep 22, 2026 · TRV-2026-1162

78Index score
15
HealthStableModerate evidence · 1 source

Can't see the forest for the trees? Statistical considerations for disease macroecology: Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk.

Disease macroecology relies on large, complex datasets to understand the biotic and abiotic factors shaping parasite distributions and emerging infectious disease risk. These datasets span local to global host-parasite interactions and often integrate diverse host or parasite traits across evolutionary histories.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
96

Updated Sep 19, 2026 · TRV-2026-1142

78Index score
16
HealthStableModerate evidence · 1 source

Predictive performance dropped from LOOCV to ensemble CV and the small N=96 phenotypically rich dataset means results remain hypothesis-generating without prospective replication in adequately powered cohorts.

Researchers developed a three-step machine learning approach to explore self-reported predictors of recovery in 96 spinal pain patients undergoing chiropractic care, using baseline questionnaires to predict binary recovery at 3 months across pain, disability, quality of life and global impression measures.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
95

Updated Sep 16, 2026 · TRV-2026-1109

78Index score

Recomputed live from the record · Oct 11, 2026, 1:37 AM