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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 932 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.

932 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 · 932

109
HealthStableModerate evidence · 1 source

Development of a Machine Learning Algorithm for Differential Diagnosis Between Primary Immune Thrombocytopenia and Connective Tissue Disease-Related Thrombocytopenia in Pediatric Patients: Based on the above evaluation indicators, the BPNN model had the best performance, with an F1 score of 0.95, an accuracy rate of 94.87%, and an AUC of 0.9738.

Background To develop a machine learning model for early differentiation of primary immune thrombocytopenia (pITP) from connective tissue disease-related thrombocytopenia (CTD-TP) in children presenting with thrombocytopenia. Method A retrospective study was conducted on 387 newly diagnosed children with thrombocytopenia.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0975

74Index score
110
HealthStableModerate evidence · 1 source

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study: In internal validation (Stage I), it achieved an accuracy of 0.980 (95% CI 0.977-0.983) and an ROC-AUC of 0.995 (95% CI 0.994-0.997).

Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 2, 2026 · TRV-2026-0962

74Index score
111
OtherStableModerate evidence · 1 source

Wearable PLF sensors achieved up to 85% accuracy for lameness detection and over 97% accuracy for stress-related physiological changes in extensive sheep systems.

This peer-reviewed review from September 2026 synthesized more than 35 studies on precision livestock farming for extensive sheep production, where large grazing areas and limited supervision make early health detection difficult. It assessed wearable sensors, GPS collars, computer vision, AI and environmental monitoring including drones and ground-based sensors.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0951

74Index score
112
HealthStableModerate evidence · 1 source

Deep learning models for Hirschsprung disease histopathology achieved over 90% ganglion cell detection and cut diagnostic time by 50-95%, increasing accuracy and accelerating clinical decision-making.

This PRISMA 2020 systematic review of thirteen studies from 2016-2025 examined machine and deep learning for Hirschsprung disease diagnosis from histopathological images, evaluating architectures, workflows, and performance with QUADAS-AI and PROBAST.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0942

74Index score

AI problems · 770

109
ClimateStableModerate evidence · 1 source

Expanding AI and digital infrastructure for automation increases demand for computing energy, raising concerns about data center efficiency and carbon footprint under the Green AI vs Red AI divide.

By March 2026, this peer-reviewed overview described how AI-based automation in Industry 4.0 optimized production, logistics, and resource management to reduce waste and energy use, and how Industry 5.0 expanded that with human-machine collaboration, generative AI, digital twins, and decentralized smart grids and microgrids.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 19, 2026 · TRV-2026-0281

72Index score
110
ScienceStableModerate evidence · 1 source

Customers exposed to AI agent implementation in substitution and adoption contexts responded less favorably on average as of the 2026 meta-analysis

As of the April 4 2026 publication date, a meta-analysis of 468 effect sizes from 95 articles with 82,751 participants examined AI agent implementation across substitution and adoption contexts and found that customers, on average, responded less favorably to AI agent implementation.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0155

72Index score
111
LaborStableModerate evidence · 1 source

The same vision treats human professional expertise as an extractable resource whose value is judged relative to AI, with potential to transform and revalue expert careers.

By June 2026, researchers analyzed public messaging from five AI data annotation firms and their CEOs, finding a consistent vision in which expert gig labor is used to build AI systems that can substitute for human professionals. The study documents this discourse from social media and podcasts rather than measuring employment or wage outcomes.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0142

72Index score
112
BusinessStableModerate evidence · 1 source

Enterprises governing AI as a broad technology program see many initiatives fail to scale or generate sustained business value

By July 2026, the authors described an AI-investment paradox in enterprises: continued heavy investment alongside initiatives that fail to scale. They proposed a decision-centric portfolio framework that reframes governance around discrete investable decision opportunities within workflows, introducing AI-Investable Process Nodes as bounded points where benefits, risks and costs can be assessed ex ante.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0111

72Index score

Recomputed live from the record · Oct 11, 2026, 12:46 PM