+ HEALTH Rationale and objectives To propose a new imaging biomarker, the pulmonary artery-to-vein volume difference (…± HEALTH Rationale and objectives The international application of artificial intelligence (AI) for lesion detection b…± HEALTH Rationale and objectives To address the challenge of preoperative prediction of synchronous liver metastasis…± HEALTH Objectives To conduct a nationwide survey among professionals working in oncology departments in China to inv…+ HEALTH The utilization of deep convolutional neural networks for the purpose of diagnosing diseases in the skin area…± HEALTH BACKGROUND . Gadolinium-based contrast agents remain essential for MRI but carry risks. Deep learning (DL) me…+ SCIENCE Gas Chromatography is a versatile separation technique widely used in analytical chemistry for constituent de…+ HEALTH Background Intracardiac echocardiography (ICE) facilitates left atrial (LA) reconstruction during atrial fibr…
TruaceTracing the truth around AIThursday, August 27, 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.

1,168 results
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AI gains · 649

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

Deployment of Magic Note AI-assisted recording in a Scottish Local Authority social work department provided administrative relief and efficiency for practitioners.

Researchers analyzed baseline evaluation data from the rollout of Magic Note, an AI-assisted recording tool, in a Scottish Local Authority social work department, involving surveys, focus groups and free-text responses from practitioners.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0172

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

Hybrid framework using Binary iHow for feature selection and iHow for hyperparameter tuning of a Multi-Scale Attention Network reduced forecasting error for wind and solar generation and improved computational scalability for smart-grid management.

Researchers developed a hybrid deep learning-optimization framework for renewable forecasting that pairs a Multi-Scale Attention Network with cognitively inspired metaheuristics. The Binary iHow algorithm selects features and the continuous iHow algorithm tunes hyperparameters, targeting high-dimensional inputs and sensitivity issues in wind and solar time series.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0168

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

Integrating Shuffled Frog Leaping Algorithm optimization with Boosted Trees and QSVM classifiers improved discrimination of argillic, phyllic, propylitic and iron oxide alteration zones from ASTER and Sentinel-2 data, raising AUC and achieving strong field validation.

Researchers developed a metaheuristic-optimized machine learning workflow that integrates Boosted Trees and Quadratic Support Vector Machines with the Shuffled Frog Leaping Algorithm to map hydrothermal alteration from multispectral satellite imagery. Tested in the Shahr-e-Babak district of Iran's Urumieh-Dokhtar Magmatic Belt, the method used ASTER and Sentinel-2 bands to discriminate argillic, phyllic, propylitic and iron oxide zones.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0167

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

Agentic AI systems demonstrated autonomous, goal-directed behavior with high accuracy in cancer diagnosis, treatment planning, alert generation, coaching, and workflow optimization across emergency medicine, oncology, radiology, and rehabilitation pilots.

A March 2026 scoping review in npj Digital Medicine examined agentic AI in healthcare, defined as systems capable of operating autonomously to achieve defined clinical goals. Across five databases, seven studies met criteria, spanning emergency medicine, oncology, radiology, and rehabilitation, with features including autonomous operation, goal-directed behavior, action initiation, and multi-agent collaboration.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0166

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

Provincial panel analysis for 2020-2023 found digitalization significantly increased labor productivity for micro, small and medium enterprises in Indonesia.

A peer-reviewed study of Indonesian provinces from 2020 to 2023 examined how digitalization and AI adoption relate to MSME labor productivity, using fixed-effects panel estimation and machine learning methods and framing results with Ibn Khaldun's institutional ideas.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0165

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

The AI Scientist pipeline autonomously executed the entire research lifecycle and produced a manuscript that cleared initial peer review at a selective machine learning workshop.

Researchers built The AI Scientist, an agentic system using foundation models to automate conception, coding, experimentation, data analysis, manuscript writing, and peer review. By March 2026 they reported that a manuscript fully generated by the system passed first-round review for a workshop at a top-tier machine learning conference.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0163

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

Students reported significantly lower anxiety during AIvaluate-mediated performance-based assessments compared to traditional face-to-face assessments, with usability rated in the good range.

Researchers compared traditional face-to-face performance-based assessments with sessions mediated by AIvaluate, an LLM-augmented emotionally intelligent conversational agent, using 35 pre-university students in a counterbalanced within-subjects design measuring emotional state, usability and qualitative perceptions.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0162

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

AI use was associated with strong perceived gains in educational support through enhanced and personalized learning environments, and in emotional and mental well-being.

On 2026-06-04 a peer-reviewed study reported results from 150 participants surveyed about AI's influence on behavior and well-being. Using a Likert-scale questionnaire analyzed in SPSS version 25, the authors found educational support had the highest mean at 4.89 and emotional and mental well-being had the highest mean difference at 4.9, with all T-tests significant at p < 0.000.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%91

Updated Jul 13, 2026 · TRV-2026-0161

AI problems · 519

No problems on this page.

Recomputed live from the record · Aug 27, 2026, 6:22 AM