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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 sourced gains and 771 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.

935 gains
771 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.

1,706 results
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AI gains · 935

413
HealthStableModerate evidence · 1 source

An integrated ligand-based machine learning and structure-based docking funnel screened 22,823 compounds and identified HY-18,623 as a potent dual CDK4/6 inhibitor with low-nanomolar IC50s.

Researchers built a virtual screening funnel that combined a Bayesian Ridge regressor trained on ChEMBL CDK4/6 inhibitor data with structure-based docking and molecular dynamics. The ML model using ECFP4 fingerprints achieved cross-validated R2 around 0.73 for CDK4 and 0.72 for CDK6 and was used to prioritize a 22,823-compound library down to three hits.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0940

68Index score
414
CrimeStableModerate evidence · 1 source

The integrated ML-based Yield Gap Vulnerability framework provides a data-driven decision-support tool that can support sustainable agricultural management, spatial planning and risk reduction by identifying vulnerability hotspots for region-specific measures in India.

Researchers developed a Yield Gap Vulnerability framework for India that integrates agricultural, hydrological, meteorological and socioeconomic indicators with observed yield gaps for rice, wheat, maize and millet at district level, using an integrated Machine Learning approach with XGBoost, Random Forest and Artificial Neural Network models.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0939

68Index score
415
HealthStableModerate evidence · 1 source

AI models Gemini and FaceApp generated post-denture facial images rated as esthetically comparable to actual clinical outcomes by patients and experts.

A prospective study of 14 edentulous patients compared actual post-denture facial photographs with AI-generated predictions from pretreatment images using Gemini and FaceApp, assessing patient preference, expert esthetic ratings, and quantitative anthropometric measurements.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0938

68Index score
416
HealthStableModerate evidence · 1 source

A multimodal deep learning model analyzing resting 12-lead ECG and clinical data was feasible for pre-participation cardiovascular screening and achieved moderate discrimination for fitness for competitive sports.

In a prospective single-center study of 526 competitive athletes undergoing routine pre-participation cardiovascular screening, researchers tested a multimodal deep learning model that combined resting 12-lead ECG with clinical variables to predict clinical fitness classification. Using stratified 10-fold cross-validation, the model achieved test accuracy of 0.64 b1 0.08 and AUC 0.72 (0.66-0.78).

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
92

Updated Aug 31, 2026 · TRV-2026-0937

68Index score

AI problems · 771

413
ScienceStableModerate evidence · 1 source

Frontier Large Reasoning Models face a complete accuracy collapse beyond certain puzzle complexities and exhibit a counterintuitive scaling limit where reasoning effort declines despite adequate token budget.

Published September 23, 2025, this peer-reviewed study systematically tested frontier Large Reasoning Models that generate detailed thinking processes before answering. Using controllable puzzle environments to vary compositional complexity, the authors analyzed final accuracy and internal reasoning traces and compared LRMs to standard LLMs under equivalent inference compute.

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

Updated Jul 22, 2026 · TRV-2026-0487

67Index score
414
HealthStableModerate evidence · 1 source

Integration into routine care is constrained by limited explainability, data bias, lack of prospective trials, regulatory hurdles, and mixed real-world outcome evidence for decision support tools.

A September 2025 peer-reviewed review in Clinics and Practice synthesized 150 studies of AI in clinical medicine after screening 2047 PubMed records. It found strong diagnostic imaging performance with expert-level cancer detection, promise for CDSS in predicting sepsis and atrial fibrillation, and advances in surgical guidance, pathology diagnosis, and drug discovery via protein structure prediction.

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

Updated Jul 22, 2026 · TRV-2026-0486

67Index score
415
CrimeStableModerate evidence · 1 source

Generative AI enables fraud attacks that erode Zero-Trust Architecture by using synthetic identities and context manipulation to increase false-negative rates, extend dwell time, bypass policies, and evade audit trails.

In a peer-reviewed survey published October 15, 2025, researchers analyzed 10 recent Zero-Trust Architecture surveys and 136 primary studies from 2022-2024 and found most controls lacked real-world validation. They argue generative AI attacks exploit those gaps and propose a seven-stage Cyber Fraud Kill Chain that maps synthetic identities, context manipulation, and adversarial telemetry to NIST SP 800-207 components.

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

Updated Jul 22, 2026 · TRV-2026-0483

67Index score
416
LifestyleStableModerate evidence · 1 source

Popular AI chatbots exhibit class-based regularities in how they portray the lifestyle and tastes of fictional personas across different occupations.

In a peer-reviewed study published October 2025, researchers conducted 39 interviews with ChatGPT, Gemini and Replika, prompting each to impersonate people in six occupational groups ranging from highly skilled professionals and humanities professors to blue-collar workers, construction workers, computer scientists and hairdressers. The qualitative analysis identified regularities in how the chatbots described everyday tastes and lifestyles that aligned with class distinctions.

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

Updated Jul 22, 2026 · TRV-2026-0482

67Index score

Recomputed live from the record · Oct 13, 2026, 5:43 AM