+ 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…± HEALTH Abstract The integration of artificial intelligence (AI) into laboratory medicine has undergone a remarkable…
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.

1,418 results
Show filters and sorting

AI gains · 787

77
GainLabor· Stable· Evidence: Moderate (1 source)

Among 12,562 Kazakhstan civil servants, access to modern digital tools and managerial position increased active AI adoption in public-sector HRM.

In June 2025 researchers surveyed 12,562 civil servants in Kazakhstan to examine determinants of AI adoption in public-sector HRM. They validated internal and external HR quality indices and estimated OLS, logistic, and path models to link HR quality, perceived effectiveness, and AI readiness.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0185

77
GainHealth· Stable· Evidence: Moderate (1 source)

LLM-based conversational agents offer 24/7 availability as a scalable way to help address the mental health treatment gap.

A systematic review of 27 studies including more than 22,000 participants across 12 countries examined barriers and facilitators to using LLM-based conversational agents in mental healthcare. Using CFIR, the authors found 24/7 availability was the most reported facilitator in 26 of 27 studies, while inadequate crisis detection was the most reported barrier in 21 of 27 studies.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0183

77
GainLifestyle· Stable· Evidence: Moderate (1 source)

Socially active young adults experiencing social-interaction burnout use AI companions as an algorithmic sanctuary to avoid judgment costs in collectivist culture.

A June 2026 peer-reviewed survey of 1,200 young adults in Palembang, Indonesia examined why socially active youth turn to large-language-model AI companions. Using validated scales and mediation-moderation analysis, it found burnout, loneliness, and parasocial interaction strongly predicted emotional attachment to AI, with judgment apprehension amplifying the loneliness effect.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0164

77
GainScience· Stable· Evidence: Moderate (1 source)

The integrated ACME framework increased ship fuel consumption prediction accuracy and generalisation, outperforming mainstream models and providing data-driven tools for energy efficiency management and decarbonisation in maritime transport.

By April 2026, researchers had developed and tested an integrated framework combining advanced optimisation with adaptive ensemble learning for ship fuel consumption prediction. The system fused noon reports, AIS, and meteorological and oceanographic reanalysis data, applied SHAP-weighted feature selection and hierarchical parameter search, and used cluster-based multi-ensemble learning to adapt to different operational conditions.

Impact 30%49
Evidence 25%95
Scale 20%85
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0150

AI problems · 631

73
ProblemHealth· Newly added· Evidence: Moderate (1 source)

There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models.

There is a growing literature on the prediction of risk of deterioration in hospital settings, including by leveraging artificial intelligence (AI) models. However, this literature has focused on acute-care hospitals, rather than post-acute facilities, where the risk of deterioration remains high.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%98

Updated Sep 4, 2026 · TRV-2026-0982

73
ProblemCrime· Stable· Evidence: Moderate (1 source)

Despite increased modal yields for rice and wheat, regional yield gaps continue to widen and the share of high-vulnerability districts has risen, particularly in resource-stressed regions such as the Indo-Gangetic Plains, driven by socioeconomic inequality and climate variability.

Researchers developed a Yield Gap Vulnerability framework for India that integrates agricultural, hydrological, meteorological and socioeconomic indicators with observed yield gaps for rice, wheat, maize and millet at district level, using an integrated Machine Learning approach with XGBoost, Random Forest and Artificial Neural Network models.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%97

Updated Aug 31, 2026 · TRV-2026-0939

73
ProblemMedia & Arts· Stable· Evidence: Moderate (1 source)

Integration of AI into music creation enables new forms of infringement that complicate protection of musical works through illegal copying and digital remixing.

The peer-reviewed article analyzes how digital technologies have transformed protection of musical works, moving from analog media to online platforms. It identifies streaming piracy, illegal copying, AI use, and digital remixing as emerging threats and reviews legal and technical tools including registration, DRM, watermarks, and blockchain, alongside international and Russian practice.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%96

Updated Aug 27, 2026 · TRV-2026-0910

73
ProblemHealth· Stable· Evidence: Moderate (1 source)

Use of generative AI in healthcare introduces challenges including lack of professional expertise in decision making, risk to patient data privacy, difficulty integrating with existing healthcare systems, and data bias.

This January 2024 IEEE Access review surveys generative AI in healthcare, describing models including ChatGPT, DALL-E, Bard, and seven healthcare-customized LLMs such as Med-PaLM, BioGPT, and DeepHealth. It catalogs applications from medical imaging and drug discovery to personalized treatment, simulation and training, clinical trial optimization, and medical chatbots, and details four real-world scenarios employing GAI: visual snow syndrome diagnosis, molecular drug optimization, medical education, and dentistry.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%94

Updated Aug 14, 2026 · TRV-2026-0760

Recomputed live from the record · Sep 15, 2026, 1:40 PM