Truace
Subscribe
The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

939 gains
772 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,711 results
Show filters and sorting

Download every matching row, not just this page:Export CSVExport JSON

AI gains · 939

441
ClimateStableModerate evidence · 1 source

Domain-specific EfficientNet-B0 classifier and Claude Vision API with human-in-the-loop guidance automated morphological characterization of microplastics from optical microscope images, achieving high F1-scores for shape/type, color and texture.

Researchers compared a domain-specific EfficientNet-B0 multi-task deep learning classifier trained on about 700 annotated optical microscope images against a zero-shot Claude Vision API augmented with expert human-in-the-loop guidance. Both were tested on the same independent test set for predicting microplastic shape/type, color, and surface texture, with the DL model reaching F1-scores of 91.2%, 88.5% and 85.1% and the VLM improving from 72-81% to 84-89% after refinement.

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

Updated Aug 26, 2026 · TRV-2026-0894

68Index score
442
HealthStableModerate evidence · 1 source

AI integration of chemical, biological and clinical data is supporting more informed rational drug design and has contributed to a small but growing number of AI-guided molecules entering clinical development.

This peer-reviewed review from August 2026 examines how machine learning, deep learning, NLP, and generative modeling are being used across medicinal chemistry, including target discovery, virtual screening, property prediction, de novo design, fragment optimization, ADMET assessment, and clinical trial design, with emphasis on multimodal data fusion and human-AI collaboration.

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

Updated Aug 26, 2026 · TRV-2026-0893

68Index score
443
HealthStableModerate evidence · 1 source

When applied to reduce administrative burden, AI can optimize workflow and clinical precision and theoretically free nurses to deepen the therapeutic bond with patients.

This discursive paper from the Journal of Advanced Nursing analyzes AI integration in nursing through the Fundamentals of Care framework. It reports that AI offers benefits for workflow optimization and clinical precision via predictive analytics and automated documentation, and argues that reducing administrative burden could theoretically release time for relational care.

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

Updated Aug 26, 2026 · TRV-2026-0892

68Index score
444
HealthStableModerate evidence · 1 source

Adding individual-level social determinants of health and clinical features to machine learning models improved suicide risk prediction over demographic-only baselines in a Maryland sample of 1214 suicide deaths and 815,544 living patients.

A retrospective study in the Maryland Suicide Data Warehouse tested whether social determinants of health improve machine learning suicide prediction. Using 1214 suicide deaths and 815,544 living patients linked to census tract data, three algorithms were trained and validated across demographic, clinical, and individual and geographic SDoH inputs.

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

Updated Aug 26, 2026 · TRV-2026-0891

68Index score

AI problems · 772

441
HealthStableModerate evidence · 1 source

Emotional AI systems using CBT face challenges with complex emotional understanding, inaccurate emotion recognition, ethical and privacy risks, and inability to truly empathize.

By December 2025, a peer-reviewed review in Discover Psychology examined how Cognitive Behavioral Therapy has been integrated into emotional AI systems, including chatbots and large language models, to detect mental health issues and deliver guided self-reflection and cognitive restructuring.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0429

67Index score
442
PolicyStableModerate evidence · 1 source

Over-reliance on AI tools is associated with diminished analytical skills, reduced critical thinking and engagement, and ethical risks including academic dishonesty and data privacy issues.

Researchers surveyed 226 undergraduate and postgraduate students at major universities in Mogadishu to examine how over-reliance on AI relates to learning outcomes, and how ethical concerns and institutional policies moderate that relationship.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0428

67Index score
443
ScienceStableModerate evidence · 1 source

Bias in training data and other issues including ethics, replication, environmental impact, and proliferation of low-quality research can negatively impact social science research using generative AI.

This PNAS perspective from May 2024 argues that generative AI capable of realistic text and image generation could enhance social science research methods. It points to survey research, online experiments, automated content analysis, and agent-based models as areas where such tools might improve study of human behavior.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0426

67Index score
444
EducationStableModerate evidence · 1 source

Same review identifies persistent risks in those settings including data privacy violations, algorithmic bias, unequal access, and erosion of relational and cultural aspects of teaching, described as an empathy gap in AI tools.

A December 2025 systematic review of 40 articles from 2015-2025 examined how AI is used in bi/multilingual education, focusing on personalized learning, intelligent tutoring systems and chatbots, and automated assessment. It reported that adaptive feedback and real-time analytics were associated with higher student engagement, learning performance and teaching efficiency in multiliteracy language learning.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0423

67Index score

Recomputed live from the record · Oct 13, 2026, 7:57 AM