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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.

1,703 results
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AI gains · 933

233
HealthStableHigh evidence · 3 sources

Generative AI that generates computer-based summaries of patient details improved efficiency, reduced cognitive burden, and standardized information transfer during nurse-to-nurse handoffs from the emergency department to inpatient units.

By June 19, 2026, a team reported a project to improve handoff communication between the emergency department and inpatient units using generative artificial intelligence. The system generates computer-based summaries to convey key patient details, developed through a collaborative hackathon and rapid prototyping with frontline nurses.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0309

70Index score
234
BusinessStableHigh evidence · 3 sources

Australian creatives would retain ownership and control of their work and receive payment through licensing deals with AI companies, rather than having their books, music, art and news used for free to train models.

On 15 July 2026, Prime Minister Anthony Albanese announced a new office of AI and said Australia will legislate the strongest possible protection for creatives against unlicensed use of their work to train AI models, while also imposing strict new rules on large energy-intensive datacentres.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 16, 2026 · TRV-2026-0227

70Index score
235
ScienceStableHigh evidence · 5 sources

Secure federated learning allows organizations to build data networks and share knowledge without compromising user privacy.

In a January 2019 peer-reviewed survey, researchers described two persistent barriers for AI: data siloed as isolated islands and tightening privacy and security requirements. They proposed a comprehensive secure federated-learning framework that includes horizontal, vertical, and transfer variants, and surveyed existing work on definitions, architectures, and applications.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0212

70Index score
236
ScienceStableHigh evidence · 5 sources

A three-track neural network that jointly processes sequence, distance, and coordinate information enables accurate prediction of protein structures and protein-protein complexes, approaching DeepMind's accuracy.

In work published August 20, 2021, researchers built on the CASP14-era DeepMind approach to protein folding. They developed RoseTTAFold, a three-track network that processes sequence, distance, and coordinate information simultaneously, achieving accuracies approaching those of DeepMind.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
100
Recency 10%
83

Updated Jul 13, 2026 · TRV-2026-0211

70Index score

AI problems · 770

233
HealthStableModerate evidence · 1 source

Half of included studies had identified risk of bias and clinical adoption remains limited, requiring multicenter prospective validation and interpretable frameworks before bedside use.

A PRISMA-compliant systematic review and meta-analysis of 8 studies with 5503 adult patients evaluated supervised machine learning models to predict all-cause mortality in infective endocarditis, a condition described as often fatal despite surgical and antibiotic advances. Five studies were pooled, showing strong discrimination for both in-hospital/early and 6-month mortality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1049

68Index score
234
HealthStableModerate evidence · 1 source

Visual HRCT scoring remains reader-dependent and AI outputs lack prospective multicenter validation and protocol harmonization needed to serve as treatment-triggering biomarkers.

This peer-reviewed review summarizes recent AI and radiomics work in systemic sclerosis-associated ILD, the leading cause of disease-related mortality in systemic sclerosis, where visual HRCT scoring is reader-dependent. It reports that deep-learning UIP probability and whole-chest quantitative biomarkers stratify FVC decline and survival, and that AI-derived HRCT parameters correlate with DLCO/TLC and support pattern classification across CTD-ILD subtypes.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1048

68Index score
235
HealthStableModerate evidence · 1 source

Chatbots sometimes confused similar congenital anomalies and provided less detailed anatomical descriptions in complex tumor cases, requiring caution and verification by qualified professionals before clinical use.

On September 9, 2026, a peer-reviewed comparative study reported testing ChatGPT, Gemini, and Microsoft Copilot on 20 radiological cases split between congenital anomalies and tumors. Each system received the same questions and images and was scored for diagnostic accuracy and explanatory completeness, with ChatGPT scoring 19 correct, Gemini 17, and Copilot 16.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1047

68Index score
236
HealthStableModerate evidence · 1 source

Participants identified trust as the central barrier to using an AI companion, driven by privacy concerns and vulnerabilities specific to schizophrenia.

Researchers conducted exploratory focus groups at a large academic health center in New York City with 17 patients with schizophrenia spectrum disorders, 7 caregivers, and 4 providers to discuss a hypothetical AI companion tool. Thematic analysis identified five themes about accessibility, uncertainty about role, trust, supplementing human care, and limiting use to low-risk support.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1046

68Index score

Recomputed live from the record · Oct 12, 2026, 5:25 AM