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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

470
BusinessNewly addedModerate evidence · 1 source

High AI company valuations and exposure to US AI assets are currently supporting the global economy through wealth effects.

On 13 October 2026 the IMF warned in its Global Financial Stability Report that elevated valuations, rising leverage, and circular financing among AI companies create a risk that an AI share price correction ripples across global markets.

Impact 30%
49
Evidence 25%
62
Scale 20%
85
Confidence 15%
62
Recency 10%
100

Updated Oct 13, 2026 · TRV-2026-1328

67Index score
471
HealthStableModerate evidence · 1 source

Integration of genomics, transcriptomics, proteomics and metabolomics using machine learning and deep learning has produced biomarker panels that support cancer diagnosis, prognosis and therapeutic decision-making.

By November 2025, a review in Molecular Biomedicine synthesized multi-omics strategies integrating genomics, transcriptomics, proteomics and metabolomics, with emphasis on machine learning and deep learning for horizontal and vertical integration, including single-cell and spatial technologies.

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

Updated Aug 18, 2026 · TRV-2026-0831

67Index score
472
HealthStableModerate evidence · 1 source

Computational AI analysis of HRCT provides automated, objective quantification of interstitial lung disease in patients with inflammatory rheumatic disorders, enabling precise volumetric measurement and pattern classification.

This peer-reviewed review examines computer-based image analysis including AI for quantifying interstitial lung disease on high-resolution CT in patients with inflammatory rheumatic disorders, a population where ILD drives morbidity and mortality.

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

Updated Aug 18, 2026 · TRV-2026-0830

67Index score

AI problems · 772

469
HealthStableModerate evidence · 1 source

Human-in-the-loop AI faces persistent concerns about algorithmic accountability, interpretability, and safety, plus challenges with workflow integration and regulatory gaps for adaptive systems.

A February 2026 narrative review in the International Journal of Medical Informatics synthesized studies from 2018 to 2025 on human-in-the-loop AI in healthcare. It found HITL approaches applied across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis, with evidence of improved diagnostic accuracy, reduced medical errors, and increased clinician trust versus automated or traditional care.

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

Updated Jul 20, 2026 · TRV-2026-0379

67Index score
470
HealthStableModerate evidence · 1 source

AI systems designed to enhance flourishing may systematically erode the human capacities needed to achieve wellbeing

Published February 20 2026, this peer-reviewed paper argues that while AI offers scalable mental health support, emerging patterns suggest those same systems may undermine wellbeing. It introduces the AI-IARA framework naming six capacities - Awareness, Interpretation, Intention, Action, Relational Agency and Autonomy - as essential for wellbeing when AI mediates experience.

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

Updated Jul 20, 2026 · TRV-2026-0376

67Index score
471
HealthStableModerate evidence · 1 source

Routine reliance on generative AI chatbots can lead people to develop inaccurate beliefs and delusional self-narratives, with extreme cases described as AI-induced psychosis.

A peer-reviewed philosophy paper published February 11, 2026 argues that false outputs from systems like ChatGPT, Claude, Gemini, DeepSeek and Grok should be understood not only as AI hallucinating at users, but as humans hallucinating with AI. Through distributed cognition theory, it describes how routine reliance on chatbots to think, remember and narrate can embed errors and reinforce users' own distorted beliefs.

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

Updated Jul 20, 2026 · TRV-2026-0375

67Index score
472
ScienceStableModerate evidence · 1 source

Misuse of LLMs in manuscript preparation and peer review risks hallucinations, confidentiality breaches, erosion of critical skills, and proliferation of low-quality manuscripts.

By February 2026, 53 editors-in-chief or delegates in anaesthesiology and pain medicine completed a three-round modified Delphi to produce 59 statements on responsible LLM use. The group agreed LLMs may help with limited editorial and authorial tasks if fully disclosed and human-verified, but must not create original ideas, data, references, conclusions, full manuscripts, or make editorial or peer-review decisions.

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

Updated Jul 20, 2026 · TRV-2026-0374

67Index score

Recomputed live from the record · Oct 13, 2026, 11:21 AM