The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 sourced gains and 770 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.

933 gains

770 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.

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AI gains · 933

225

EducationStableHigh evidence · 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%: 84

Updated Jul 20, 2026 · TRV-2026-0357

70Index score

226

HealthStableHigh evidence · 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%: 84

Updated Jul 20, 2026 · TRV-2026-0356

70Index score

227

HealthStableHigh evidence · 5 sources

AI-powered systems improved healthcare delivery efficiency and accuracy through personalized medicine, early disease detection, and predictive analytics, while telemedicine expanded access in underserved areas.

A peer-reviewed review published October 1 2024 describes how AI, machine learning, big data analytics, telemedicine, wearables, and 3D printing are being integrated into healthcare. It reports AI-powered solutions are enhancing efficiency and accuracy in personalized medicine and early detection, and that remote monitoring has overcome geographical constraints in underserved areas.

Impact 30%: 49
Evidence 25%: 100
Scale 20%: 35
Confidence 15%: 100
Recency 10%: 84

Updated Jul 20, 2026 · TRV-2026-0349

70Index score

228

HealthStableHigh evidence · 5 sources

AI chatbots used as supplementary mental health support reduced psychological distress among adolescents aged 10-24 and helped overcome stigma-related barriers to care access.

A March 2026 comprehensive literature review in the Journal of Multidisciplinary Healthcare analyzed 80 studies on AI chatbots for adolescents aged 10-24. It found moderate effectiveness in reducing psychological distress with effect sizes from g = -0.46 to -0.10 and noted promise in lowering stigma-related barriers to care.

Impact 30%: 49
Evidence 25%: 100
Scale 20%: 35
Confidence 15%: 100
Recency 10%: 84

Updated Jul 20, 2026 · TRV-2026-0348

70Index score

AI problems · 770

225

HealthStableModerate evidence · 1 source

Most studies of AI-enabled orthodontic care were conducted in urban or institutional environments, leaving a significant gap in real-world longitudinal data for rural or low-resource settings where access remains a major challenge.

This scoping review examined 23 studies published between January 2000 and September 2025 on AI in orthodontic diagnosis, treatment planning, appliance design, and teledentistry. It found AI-assisted systems can improve diagnostic precision and reduce clinical workload, and that remote monitoring platforms can cut in-person appointments while maintaining standards and improving compliance.

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

Updated Sep 14, 2026 · TRV-2026-1078

68Index score

226

HealthStableModerate evidence · 1 source

Algorithmic opacity and lack of interpretability frameworks tailored to resource-constrained environments have impeded clinical adoption of ML for breast cancer, contributing to diagnostic delays in settings with limited pathology capacity.

On September 11, 2026, a peer-reviewed study in PLOS Digital Health reported an explainable AI framework for breast cancer diagnosis designed for underserved settings. Using 569 fine-needle aspirate specimens from the Wisconsin dataset, the authors benchmarked eight supervised classifiers with 10-fold cross-validation and a hold-out test set, then applied SHAP analysis to surface global and individual-level feature contributions.

Impact 30%: 49
Evidence 25%: 95
Scale 20%: 35
Confidence 15%: 87
Recency 10%: 94

Updated Sep 13, 2026 · TRV-2026-1071

68Index score

227

HealthStableModerate evidence · 1 source

LLM-generated answers to patient questions about robotic-assisted total knee arthroplasty remained above recommended patient-education reading levels and should be regarded as supplementary rather than standalone sources of information.

A September 2026 peer-reviewed study in The Knee compared four large language models on 30 common patient questions about robotic-assisted total knee arthroplasty, evaluating responses with DISCERN, QAMAI, a 5-point clinical accuracy scale, and PEMAT and Flesch-Kincaid readability measures.

Impact 30%: 49
Evidence 25%: 95
Scale 20%: 35
Confidence 15%: 87
Recency 10%: 94

Updated Sep 13, 2026 · TRV-2026-1069

68Index score

228

HealthStableModerate evidence · 1 source

Apparent superiority of ML over logistic regression for trauma mortality prediction may be inflated by convergent practices including comparing best-of-several ML models to a single LR comparator, reliance on internal validation, selective reporting, and AUC-only synthesis.

A systematic review and meta-research appraisal examined 20 studies comparing machine learning and logistic regression for trauma mortality prediction, with 17 studies (243,324 patients) in primary synthesis. The pooled within-study AUC difference favoring the best ML model was 0.026 (95% CI 0.009-0.043), 0.017 in co-primary analysis of studies reporting CIs, with extreme heterogeneity and a prediction interval crossing zero.

Impact 30%: 49
Evidence 25%: 95
Scale 20%: 35
Confidence 15%: 87
Recency 10%: 94

Updated Sep 13, 2026 · TRV-2026-1068

68Index score

Recomputed live from the record · Oct 12, 2026, 4:25 AM