+ 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
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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.

1,419 results
Show filters and sorting

AI gains · 788

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

Literature shows machine learning models are applied to detect financial fraud, with credit card fraud detection models most widely used.

Published September 3, 2024, this peer-reviewed literature review applied PRISMA and Kitchenham methods to 104 articles from 2012-2023 obtained from Scopus, IEEE Xplore, Taylor & Francis, SAGE, and ScienceDirect to map how machine learning is used to detect financial fraud and its types.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0366

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%89

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%89

Updated Jul 20, 2026 · TRV-2026-0360

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

AI and machine learning enable data-driven optimization and predictive modeling for nanoparticle synthesis and characterization, improving targeted therapeutic delivery and accelerating translation.

Published March 17, 2026, this peer-reviewed review in BioNanoScience examines how artificial intelligence and machine learning are used to design and characterize nanoparticles for medical use. It describes AI models that predict physicochemical attributes, optimize synthesis conditions, and analyze characterization data to improve targeted therapeutics.

Impact 30%49
Evidence 25%100
Scale 20%35
Confidence 15%100
Recency 10%89

Updated Jul 20, 2026 · TRV-2026-0356

AI problems · 631

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

AI lesion detection models trained on Western or Eastern DBT data showed reduced performance when applied to the other population due to differences in lesion types.

A study tested YOLO-based AI models for breast lesion detection on digital breast tomosynthesis using a 94-patient Western database from the Cancer Imaging Archive and a 157-patient Eastern database from a single medical center, with lesions grouped into six types.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 27, 2026 · TRV-2026-0908

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

The linear LDA model showed unstable performance and poor discrimination in lymph-node-negative and pancreatic head tumor subgroups despite overall validation AUC.

In a retrospective study of 340 pancreatic ductal adenocarcinoma patients, investigators developed machine learning models combining CT radiomics and clinical predictors to predict synchronous liver metastasis preoperatively. In an independent validation cohort of 102 patients, the best linear model (LDA) reached AUC 0.828 and the nonlinear model (MLP) reached AUC 0.822, both showing good calibration with Hosmer-Lemeshow P values of 0.551 and 0.682.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 27, 2026 · TRV-2026-0907

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

Clinical translation is limited by inconsistent evaluation, substantially lower performance on pathology-specific regions, and reliance on single-institution data with few reader studies or external validation.

Researchers systematically reviewed 41 studies through January 2025 that used deep learning to generate synthetic postcontrast T1-weighted MRI from precontrast images alone, aiming to reduce gadolinium use. Most work was in neuroimaging, using GANs and CNNs, and a targeted meta-analysis of 15 brain tumor studies reported high whole-image similarity metrics.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 27, 2026 · TRV-2026-0904

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

In extended testing, the three-marker panel misclassified one febrile control, and the overall pilot size limits generalizability for rapid diagnosis.

By the publication date of 2026-08-25, a pilot study had collected breath samples from 17 melioidosis patients and 8 febrile controls and used two-dimensional gas chromatography mass spectrometry combined with machine learning feature selection to identify volatile signatures of infection and treatment course.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%96

Updated Aug 26, 2026 · TRV-2026-0896

Recomputed live from the record · Sep 16, 2026, 12:59 AM