+ BUSINESS Abstract This study presents a comprehensive literature review using a systematic approach to explore the rol…+ SCIENCE This study investigates the impact of a novel application of generative artificial intelligence (AI) in physi…− HEALTH The rapid integration of artificial intelligence (AI) into everyday life has created new intersections among…+ HEALTH Introduction Stereotactic Radiotherapy plays a main role in Brain Metastases treatment. Radiomics and Dosiomi…+ HEALTH Background Internet Gaming Disorder (IGD) poses significant psychological and social risks for adolescents. I…± HEALTH Background Major depressive disorder (MDD) is associated with high relapse rates, with around 20% of inpatien…± HEALTH Despite a decade in, immunotherapy (IO) treatment selection in non-small cell lung cancer (NSCLC) remains lar…+ HEALTH Rib fractures are a common type of chest injury, and the estimation of the fracture age mainly relies on clin…
TruaceTracing the truth around AITuesday, September 15, 2026
Subscribe
The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,419 results
Show filters and sorting

AI gains · 788

70
GainScience· Stable· Evidence: High (2 sources)

Local journalists in Germany reported willingness to use AI-supported tools to process data and discover stories to help maintain efficiency.

By April 13 2026, researchers reported results from 21 semi-structured interviews with local journalists in Germany examining use of data and AI, challenges in interaction, and perceived opportunities for AI-supported reporting systems.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%99
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0156

70
GainLabor· Stable· Evidence: High (3 sources)

US workers would gain workplace protection from AI through union-backed policies requiring a human to remain the final decision maker on issues affecting individuals, with broad survey support for such rules.

On 2026-05-12, The Guardian reported on a new poll released by the AFL-CIO, the largest federation of labor unions in the US. The poll found overwhelming support among US workers for pro-worker policies on artificial intelligence, including a specific proposal that a human be the final decision maker on issues affecting individuals, with 95% support for that requirement and more than nine out of ten supporting union-backed AI policies generally.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%87

Updated Jul 12, 2026 · TRV-2026-0091

70
GainScience· Rising· Evidence: High (2 sources)

Artificial neural networks have achieved practical deployment for pattern recognition tasks within manufacturing industries.

The source is a review of artificial neural network applications to pattern recognition. It describes an early era of simplified ANN use that expanded into multiple domains and reports progress surveyed in the literature, while highlighting persistent technical obstacles that prompted a call for state-of-the-art updates.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%99
Recency 10%87

Updated Jul 12, 2026 · TRV-2026-0066

70
GainScience· Rising· Evidence: High (5 sources)

The development and release of ethics guidelines provides normative principles intended to help harness disruptive potentials of AI systems in research, development and application.

This paper conducts a semi-systematic evaluation that analyzes and compares 22 ethics guidelines released in recent years following advances in research, development and application of AI systems. The guidelines are described as comprising normative principles and recommendations.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%87

Updated Jul 12, 2026 · TRV-2026-0056

AI problems · 631

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems.

Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt.

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

Updated Aug 23, 2026 · TRV-2026-0852

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust.

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions.

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

Updated Aug 22, 2026 · TRV-2026-0847

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

Multi-omics biomarker approaches face persistent challenges in data heterogeneity, reproducibility, and clinical validation across diverse patient populations.

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%95

Updated Aug 18, 2026 · TRV-2026-0831

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

Oncology AI systems can reproduce or amplify existing disparities across patient populations, and efforts to enforce fairness definitions often conflict with overall predictive performance.

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%94

Updated Aug 18, 2026 · TRV-2026-0829

Recomputed live from the record · Sep 16, 2026, 3:06 AM