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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 sourced gains and 771 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.

935 gains
771 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 · 935

389
HealthStableModerate evidence · 1 source

An ultrasound-based habitat subregional radiomics model using SVM achieved strong external validation for preoperative prediction of invasive breast cancer with concomitant DCIS, supporting preoperative risk stratification.

Researchers developed and externally validated an ultrasound-based habitat subregional radiomics model to predict invasive breast cancer with a DCIS component before surgery. Using 1063 patients across two centers, they delineated tumors on 2D ultrasound, clustered them into three intratumoral habitats, and built machine learning models evaluated by AUC, calibration, and decision curve analysis.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 7, 2026 · TRV-2026-1005

68Index score
390
PolicyStableModerate evidence · 1 source

University EFL students receiving AI-mediated language instruction showed higher English achievement across grammar, vocabulary, reading and writing, plus increased L2 motivation and greater use of self-regulated learning strategies compared to traditional instruction.

In a mixed-methods study of 60 university EFL learners in two intact classes, researchers tested AI-mediated language instruction against traditional instruction. By November 2023, they reported that the AI group scored higher on post-tests of grammar, vocabulary, reading and writing, and reported higher L2 motivation and self-regulated learning strategy use, with 14 interviewed students describing more engaging, personalized experiences.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0999

68Index score
391
HealthStableModerate evidence · 1 source

Multi-model machine learning screening of 6PPD-Q-IBD targets identified 60 overlapping targets and prioritized six core genes with NR1H4 as a key mediator of intestinal epithelial injury.

Researchers used network toxicology, multi-model machine learning, molecular docking, and in vitro experiments in human intestinal epithelial cells to probe the tire-derived pollutant 6PPD-quinone. The workflow identified 60 overlapping 6PPD-Q-IBD targets and prioritized six core genes, with NR1H4 as a key mediator that binds strongly to 6PPD-Q.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0998

68Index score
392
HealthStableModerate evidence · 1 source

Canadian medical students who were interested in radiology rated procedural roles as more important and reported greater perceived career sustainability despite AI integration.

By September 2026, researchers surveyed 73 Canadian medical students to test whether perceptions of radiology's procedural scope and AI integration differed by interest in radiology. Interested students rated therapeutic and diagnostic procedures as more important and reported higher perceived career sustainability, while non-interested students reported higher perceived AI impact.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0997

68Index score

AI problems · 771

389
HealthStableModerate evidence · 1 source

Mainstream implementation of AI in healthcare is hindered by data security issues and budget and resource constraints.

This peer-reviewed review from January 2025 examined how AI technologies including robotics, machine learning, deep learning, and natural language processing are being applied in healthcare. Drawing on Web of Science literature from 2014-2024 and case studies such as Google Health and IBM Watson Health, it reported growth in publications and use in patient interaction, predictive analytics, and remote monitoring.

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

Updated Jul 24, 2026 · TRV-2026-0544

67Index score
390
ClimateStableModerate evidence · 1 source

AI for extreme climate events is limited by noisy, heterogeneous, small sample sizes with limited annotations, challenges integrating real-time information, and lack of understandable models needed for stakeholder trust and regulatory compliance.

Published February 24, 2025, this Nature Communications review examines how artificial intelligence is used to model and understand extreme weather and climate events including floods, droughts, wildfires, and heatwaves. It reports that AI has improved weather forecasting, model emulation, parameter estimation, and prediction of extremes, while also discussing methods to identify and explain events more effectively.

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

Updated Jul 24, 2026 · TRV-2026-0543

67Index score
391
EducationStableModerate evidence · 1 source

The same AI-driven neuroadaptive learning systems raise implementation problems for K-12 and adult learners, including data privacy and data security risks, ethical concerns and algorithmic bias, scalability issues, and accessibility disparities.

This February 2025 systematic review of 103 papers examined how Cognitive Load Theory, Educational Neuroscience, and AI/ML combine in adaptive learning. It found that systems using EEG, fNIRS and other physiological signals to feed CNN, RNN and SVM models can automatically manage cognitive load and dynamically adapt learning pathways for K-12 and adult learners.

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

Updated Jul 24, 2026 · TRV-2026-0542

67Index score
392
EducationStableModerate evidence · 1 source

AI adoption in nursing faces barriers including data privacy risks, algorithmic bias, lack of transparency and accountability, resistance to adoption, and disparities in access to AI technologies and standardized education.

On March 12, 2025, an umbrella review in the Journal of Medical Internet Research synthesized 18 reviews from 274 screened records on AI in nursing. It found consistent reports of potential advances in patient care and clinical workflows alongside an urgent push to update nursing curricula with AI-driven tools and ethics training.

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

Updated Jul 24, 2026 · TRV-2026-0538

67Index score

Recomputed live from the record · Oct 13, 2026, 2:45 AM