+ HEALTH Aims Intramuscular fat (IMF) is increasingly recognized as a marker of ectopic adiposity and adverse cardiome…+ HEALTH Periapical lesions are challenging to detect on intraoral radiographs because of anatomical superimposition a…± HEALTH Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inher…+ HEALTH Objectives We adapted the individualized polysocial risk score (iPsRS), a machine learning model originally d…± HEALTH Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagno…+ HEALTH Classification is a core machine learning task, and ensemble voting methods are widely used to improve predic…+ CLIMATE Microplastic (MP) pollution poses escalating environmental risks, demanding efficient and reproducible tools…± HEALTH Medicinal chemistry sits at the center of modern drug discovery, yet translating molecular designs into appro…
TruaceTracing the truth around AIWednesday, August 26, 2026
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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.

1,155 results
Show filters and sorting

AI gains · 641

78
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%91

Updated Jul 13, 2026 · TRV-2026-0185

78
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%91

Updated Jul 13, 2026 · TRV-2026-0183

78
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%91

Updated Jul 13, 2026 · TRV-2026-0164

78
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%91

Updated Jul 13, 2026 · TRV-2026-0150

AI problems · 514

73
ProblemHealth· Newly added· 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%98

Updated Aug 14, 2026 · TRV-2026-0760

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

Clinical use of AI is limited by hallucinations, algorithmic bias, data protection requirements, and regulatory considerations that require continuous human oversight.

A July 2026 review in Die Urologie describes artificial intelligence moving from research into everyday clinical practice and hospital care, with generative AI and large language models now used alongside established image-analysis tools for documentation, knowledge management, patient communication, and workflow optimization, plus AI-assisted radiological and pathological interpretation and risk stratification.

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

Updated Jul 31, 2026 · TRV-2026-0601

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

Implementation of AI in waste management faces challenges due to financial and personnel constraints.

A peer-reviewed study published October 4, 2025 examined AI for municipal waste management in Industry 4.0. Based on a 2024 online survey of 78 respondents mainly from Europe with experience or interest in AI, logistics, and ecology, authors reported that 78% saw AI reducing waste management costs, 59% saw greatest benefits in sorting and recycling, and 51% saw effectiveness in optimizing collection routes.

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

Updated Jul 29, 2026 · TRV-2026-0585

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

Digital mental health tools are hampered by engagement challenges, industry setbacks, methodological critiques, and gaps in evidence and scaling that limit real-world applicability.

As of May 2025, this review in World Psychiatry examined how smartphone apps, virtual reality, and generative AI including large language models are being applied to mental health, evaluating evidence across well-being, depression, anxiety, schizophrenia, eating disorders and substance use, and outlining advances in digital phenotyping and generative outputs.

Impact 30%49
Evidence 25%95
Scale 20%60
Confidence 15%87
Recency 10%93

Updated Jul 24, 2026 · TRV-2026-0523

Recomputed live from the record · Aug 27, 2026, 3:52 AM