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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

417
LifestyleStableModerate evidence · 1 source

Nine machine learning models using baseline frailty index predicted motoric cognitive risk syndrome, with higher frailty linked to increased MCR risk in adults aged 45 and older.

Researchers analyzed 3388 CHARLS participants aged 45 and older across 2011 to 2015 to examine frailty and motoric cognitive risk syndrome. They built nine machine learning models using baseline frailty index to predict MCR risk and tracked changes in frailty status. By follow-up end, 127 participants (3.74%) developed MCR, with higher baseline FI, total FI, and change in FI associated with increased risk.

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

Updated Aug 31, 2026 · TRV-2026-0936

68Index score
418
HealthStableModerate evidence · 1 source

Models trained on routinely collected social care records predicted future hospital admissions and all-cause mortality with meaningful discriminative performance reaching AUROC 0.893.

On 2026-08-29, a peer-reviewed study reported machine learning models trained on pseudonymised social care records from 27,590 adults in Oxfordshire to predict future care plan needs, hospital admissions, and all-cause mortality across multiple horizons, finding meaningful discrimination for clinical outcomes.

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

Updated Aug 31, 2026 · TRV-2026-0935

68Index score
419
ClimateStableModerate evidence · 1 source

Machine learning can map potentially toxic element concentrations from environmental covariates and produce exceedance-probability maps aligned with regulatory thresholds for soil-contamination management.

Published August 29, 2026, this peer-reviewed synthesis reviews 2020-2025 literature on machine learning for mapping potentially toxic elements in soils. It finds growing use of ML with environmental covariates but persistent use of spatially naive validation, limited interpretation, and incomplete uncertainty reporting.

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

Updated Aug 31, 2026 · TRV-2026-0932

68Index score
420
HealthStableModerate evidence · 1 source

Unsupervised machine learning applied to bloodstream infections identifies reproducible clinical subphenotypes with different mortality, supporting bedside tools for rapid phenotype assignment and personalized antimicrobial therapy.

By August 2026, a review in Clinical Microbiology and Infection summarized evidence on using unsupervised machine learning to derive clinical subphenotypes of bloodstream infections. The strongest data were in Staphylococcus aureus bacteraemia, where latent class and cluster analyses identified distinct subgroups with different mortality across cohorts, with emerging work in ICU mixed-pathogen and transplant populations and early bedside calculators for phenotype assignment.

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

Updated Aug 30, 2026 · TRV-2026-0927

68Index score

AI problems · 771

417
ScienceStableModerate evidence · 1 source

Adoption is limited by data scarcity, poor model interpretability, and absence of standardized validation protocols for composite predictions.

Published October 17 2025, this peer-reviewed review summarizes how machine learning techniques are being used to predict mechanical behavior of composite materials from experimental and simulation data.

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

Updated Jul 22, 2026 · TRV-2026-0480

67Index score
418
SportsStableModerate evidence · 1 source

Model was developed and tested on a single Kaggle dataset of 800 Chinese university players without external validation, limiting generalizability and preventing immediate clinical deployment.

Researchers built an 18-feature model across basic information, training, fitness and lifestyle dimensions for 800 Chinese university football players from a Kaggle dataset, comparing 10 algorithms and finding SVM best at 95.6% accuracy with SHAP highlighting stress, sleep and balance as key predictors.

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

Updated Jul 22, 2026 · TRV-2026-0476

67Index score
419
HealthStableModerate evidence · 1 source

Mood-tracking and predictive AI systems may erode introspection and foster over-reliance on algorithmic feedback, causing anxiety from hyper-monitoring and weakening intrinsic coping.

The peer-reviewed article examines how AI has become an intimate presence in mental health through mood-tracking apps, emotion wearables, and therapeutic chatbots like Woebot and Wysa. It argues these systems enable cognitive offloading by aggregating biometric and self-report data and delivering CBT-based prompts, while simultaneously risking cognitive overload.

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

Updated Jul 22, 2026 · TRV-2026-0475

67Index score
420
HealthStableModerate evidence · 1 source

AI-driven multi-omics integration in precision oncology faces translational challenges including data harmonization, batch correction, missing data imputation, computational scalability, and limited model generalizability.

By November 2025, this peer-reviewed review synthesized how artificial intelligence bridges the cancer multi-omics data deluge to clinical decisions, integrating genomics, transcriptomics, proteomics, metabolomics and radiomics using deep learning, graph neural networks, transformers, and explainable AI.

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

Updated Jul 22, 2026 · TRV-2026-0473

67Index score

Recomputed live from the record · Oct 13, 2026, 5:41 AM