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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

473
HealthStableModerate evidence · 1 source

AI systems are being adopted in oncology practice to support cancer detection, risk stratification, treatment planning, and clinical documentation workflows.

As of the August 2026 commentary, AI was increasingly integrated into oncology for detection, risk stratification, treatment planning, and documentation. The authors reviewed evidence that these systems can reproduce or amplify disparities and examined technical sources of bias and competing statistical definitions of fairness.

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

Updated Aug 18, 2026 · TRV-2026-0829

67Index score
474
HealthStableModerate evidence · 1 source

Large language models can restructure prostate MRI reports and extract discrete variables to support supervised summaries and patient-facing explanations.

Published August 17 2026 in Abdominal Radiology, this Perspective examines large language models applied to prostate MRI reporting, a task where laterality, sector, size, PI-RADS, and staging language directly affect biopsy and treatment decisions and where patients often see reports via portals before clinician discussion.

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

Updated Aug 18, 2026 · TRV-2026-0828

67Index score
475
HealthStableModerate evidence · 1 source

Deep-learning based AIIR reconstruction improved CT image quality and diagnostic accuracy for gastric cancer, increasing tumor conspicuity and raising AUC for detecting serosal invasion compared to hybrid iterative reconstruction.

Researchers prospectively tested a deep-learning based artificial intelligence iterative reconstruction algorithm against conventional hybrid iterative reconstruction in 132 gastric cancer patients undergoing preoperative abdominal CT before surgery or staging laparoscopy. By August 2026, they reported higher Likert scores for tumor margin and enhancement, higher contrast-to-noise ratios in arterial and portal venous phases, and higher AUC for detecting serosal invasion with AIIR.

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

Updated Aug 18, 2026 · TRV-2026-0827

67Index score
476
HealthStableModerate evidence · 1 source

An integrated framework using WBAF preprocessing, MResU-Net segmentation, IPHOG feature extraction and IShuffleNet-PCNN classification achieved 0.933 accuracy and 0.991 NPV for spinal cord injury-related fracture classification from CT images.

Researchers developed a four-stage deep learning framework for CT-based spinal cord injury fracture assessment, combining Weighted Balanced Anisotropic Filtering for denoising, Modified Residual U-Net for spinal cord segmentation, Improved Pyramid Histogram of Oriented Gradients for feature extraction, and a new IShuffleNet-Parallel CNN classifier with Group Normalization and Adaptive Swish-Mish activation.

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

Updated Aug 18, 2026 · TRV-2026-0826

67Index score

AI problems · 772

473
ScienceStableModerate evidence · 1 source

AI models in geoscience are hindered by data scarcity, computational demands, data privacy concerns, and black-box opacity, while traditional physics models struggle to capture Earth's complexities.

This peer-reviewed review from August 2024 examines the evolution of geoscience inquiry from traditional physics-based numerical models to modern data-driven ML and DL approaches enabled by advances in AI and data collection. It describes how data-driven models leverage large geoscience datasets and how hybrid models that embed domain knowledge aim to improve efficiency and reduce training data needs.

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

Updated Jul 20, 2026 · TRV-2026-0370

67Index score
474
PolicyStableModerate evidence · 1 source

Synthetic audio-visual deepfakes are proliferating across digital life, creating governance challenges for policymakers and societies.

Published September 2024, this peer-reviewed study interviewed ten academic and commercial deepfake developers and ethics representatives to understand what values guide professional development of synthetic audio-visual media and how incentives shape their sense of agency.

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

Updated Jul 20, 2026 · TRV-2026-0364

67Index score
476
HealthStableModerate evidence · 1 source

The EU AI Act's horizontal approach insufficiently addresses patient interests in healthcare, leaving gaps that require additional sector-specific guidelines.

In August 2024 the EU Artificial Intelligence Act entered into force as a legally binding framework governing AI development and use across the EU. The peer-reviewed overview published September 7, 2024 explains that healthcare is a top deployment sector and that the Act will significantly reform national policies and practices on health technology by imposing new obligations on tech developers, healthcare professionals, and public health authorities.

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

Updated Jul 20, 2026 · TRV-2026-0359

67Index score

Recomputed live from the record · Oct 13, 2026, 11:24 AM