− MEDIA & ARTS The article is devoted to a comprehensive analysis of modern problems of protecting musical works in the cont…+ 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…
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.

1,169 results
Show filters and sorting

AI gains · 649

71
GainHealth· Stable· Evidence: High (5 sources)

Large language models have enabled new applications for knowledge retrieval and processing in the medical field.

Published August 20, 2024, this peer-reviewed perspective examines multimodal large language models in medicine. It notes that clinical work depends on diverse data types from MRI and CT scans to EHR time-series, audio, text, video, and omics, while most current LLMs process only text.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0371

71
GainClimate· Rising· Evidence: High (5 sources)

Artificial intelligence applied to planning and operation of distributed energy systems enhances the efficiency and reliability of electrical grids.

This 2024 peer-reviewed review paper surveys how artificial intelligence techniques including machine learning, optimization, and cognitive computing are being applied to the planning and operation of distributed energy systems in smart grids, covering prediction, optimization, resource coordination, renewable integration, and demand response.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0361

71
GainScience· Stable· Evidence: High (5 sources)

Researchers developed a checklist for comprehensive assessment of ethical discussions in GenAI research that can be integrated into peer review to enhance research and support ethics disclosures for health-care applications.

Published September 17, 2024, this scoping review in The Lancet Digital Health examined ethical discussions surrounding generative AI in health care, including ChatGPT and other models used to synthesise data such as images for research and practical purposes. The authors found that ethical concerns have been widely noted but not translated into operational solutions.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0360

71
GainEducation· Stable· Evidence: High (2 sources)

AIIA system designed to deliver personalized and adaptive learning support in higher education, intended to reduce cognitive load and provide tailored pathways and assessment tools.

As of September 30, 2024, researchers presented a framework called artificial intelligence-enabled intelligent assistant (AIIA) for higher education, detailing its architecture, NLP techniques, and integration with learning management systems to provide interactive, personalized support.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%99
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0357

AI problems · 520

68
ProblemHealth· Newly added· Evidence: Moderate (1 source)

Most AI models for predicting laparoscopic cholecystectomy difficulty carry high risk of bias, rarely undergo external validation, and have significant methodological flaws limiting clinical translation.

By August 13, 2026, a systematic review and meta-analysis of 18 studies found AI models predicted laparoscopic cholecystectomy difficulty with pooled AUCs of 0.848 in training and 0.818 in validation, with ensemble models reaching 0.889 and 0.861. The review searched four databases to March 2, 2026 and used PROBAST and GRADE to assess bias and certainty.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 16, 2026 · TRV-2026-0787

68
ProblemHealth· Newly added· Evidence: Moderate (1 source)

Labor-intensive segmentation and factors like comminution, bilateral injury, loss of anatomic references, and metal artifacts still restrict efficiency and reproducibility of digital reconstruction workflows.

This narrative review from August 2026 summarizes AI applications across occlusion-oriented digital reconstruction of maxillofacial fractures, where treatment must address stable occlusion, mandibular movement, temporomandibular joint position, facial contour, and fixation as interdependent targets. It evaluates tasks from CT/CBCT screening and segmentation to model repair, shape completion, planning assistance, and postoperative deviation analysis.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 16, 2026 · TRV-2026-0781

68
ProblemHealth· Newly added· Evidence: Moderate (1 source)

AI facial image scoring, editing and curation systems converge on a narrow westernized phenotype and are linked to appearance dissatisfaction, perception drift, and Snapchat and Zoom dysmorphia presentations in plastic surgery patients.

This educational review from August 2026 examines how AI systems that score, edit, generate and curate facial images encode explicit quantitative definitions of attractiveness and deliver them through prediction algorithms, AR filters, generative imagery and surgical simulators. It finds independent models converge on a narrow, frequently westernized phenotype and summarizes evidence linking such imagery to appearance dissatisfaction and perception drift.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 16, 2026 · TRV-2026-0780

68
ProblemHealth· Newly added· Evidence: Moderate (1 source)

Because the dataset was split at the image level, correlated frames from the same examination may inflate performance, so results cannot be interpreted as patient-level generalization and require grouped reanalysis and external validation before clinical use.

A comparative study merged SEE-AI and Kvasir-Capsule into a 21-class capsule endoscopy image dataset and fine-tuned a Vision Transformer, DenseNet121, and ResNet50. On an independent test set of 8,696 frames, the transformer achieved 92.2% accuracy and 0.99 AUC, substantially higher than the two CNN baselines under the reported experimental conditions.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%98

Updated Aug 16, 2026 · TRV-2026-0779

Recomputed live from the record · Aug 27, 2026, 3:50 PM