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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 929 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.

929 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 · 929

29
HealthStableModerate evidence · 1 source

XGBoost model predicted PMOS status from detailed body-composition measures with AUC 0.701 in testing, with SHAP highlighting regional fat masses as top predictors.

Using GBD 2021 data, Mendelian randomization, and a clinical cohort, the study quantified global PMOS burden and tested adiposity as a causal determinant, then built machine-learning models from body-composition measures to predict PMOS, with XGBoost reaching AUC 0.701 and SHAP highlighting left-leg and trunk fat mass.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
94

Updated Sep 9, 2026 · TRV-2026-1034

78Index score
30
HealthStableModerate evidence · 1 source

AI models improve cardiovascular prevention by providing more precise, dynamic and personalized risk stratification than traditional scores and by enabling early detection of subclinical atrial fibrillation, left ventricular dysfunction and coronary disease through AI-enabled ECG and opportunistic imaging.

This peer-reviewed review examines AI, including machine learning and deep learning, for cardiovascular prevention in Italy and globally, where cardiovascular diseases remain the leading cause of mortality and morbidity. It surveys evidence that AI can deliver more precise, dynamic and personalized risk stratification than traditional scores and support early detection of subclinical disease via AI-enabled electrocardiography and opportunistic imaging.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0955

78Index score
31
HealthStableModerate evidence · 1 source

AI-based support systems could enhance safety and make xenotransplantation more reproducible when combined with gene-edited donors and refined immunosuppression.

On September 1, 2026, a peer-reviewed roadmap in Xenotransplantation outlined how artificial intelligence could be integrated into early clinical xenotransplantation to address organ shortage. It prioritizes digital pathology, machine perfusion monitoring, multimodal graft injury detection, and xenozoonotic infection surveillance, emphasizing clinician-supervised, auditable systems combined with gene-edited donors.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
92

Updated Sep 1, 2026 · TRV-2026-0953

78Index score
32
HealthStableModerate evidence · 1 source

Among Chinese oncology professionals, higher trust in system reliability and higher effort expectancy were associated with stronger behavioral intention to adopt medical AI.

A nationwide cross-sectional survey of 610 oncology professionals in China (188 physicians, 422 nurses) examined attitudes toward medical AI using the UTAUT model. Only 17.7% had both heard of and used medical AI while 67.7% had heard but never used it, indicating an awareness-usage gap.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0906

78Index score

AI problems · 770

29
Media & ArtsStableModerate evidence · 1 source

The same system showed substantially weaker longer-range referential and narrative continuity, lacking the sustained plotting and thematic organisation observed in Orwell's novel.

Published 2026-08-04, this peer-reviewed comparative case study examines passages from George Orwell's Nineteen Eighty-Four and Ross Goodwin's 2018 sensor-driven LSTM book 1 the Road to assess how generative systems handle literary coherence and creative agency.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
88

Updated Aug 8, 2026 · TRV-2026-0694

77Index score
30
HealthStableModerate evidence · 1 source

Medical AI systems built on Western biomedical traditions may inflict ontological harm and diminish trust in patient-clinician relationships by conflicting with relational and Indigenous understandings of health.

Published 6 August 2026 in the South African Medical Journal, this peer-reviewed essay examines data science and medical AI through four lightly fictional but reality-informed case studies from Bamenda to Mthatha to Toronto. It argues that AI tools largely built on Western biomedical traditions may conflict with relational, spiritual and Indigenous understandings of health, manifesting as epistemic friction and diminished trust.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
88

Updated Aug 8, 2026 · TRV-2026-0691

77Index score
31
HealthStableModerate evidence · 1 source

High algorithmic accuracy in AI models for ADHD has not yet translated into routine clinical utility without advances in explainability and multimodal fusion.

A bibliometric review of 722 Scopus-indexed papers from 2011 to 2024 tracked how artificial intelligence has been applied to ADHD prediction. Using Python and VOSviewer, the authors found exponential growth peaking in 2023, a concentration of output in the United States and China, and a technological shift from support vector machines to deep learning with EEG becoming the favored data modality.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
87

Updated Aug 3, 2026 · TRV-2026-0630

77Index score
32
HealthStableModerate evidence · 1 source

AI-driven modeling for antimicrobial resistance target discovery continues to face persistent challenges around experimental validation and genomic variability, limiting confirmation of predicted functions.

By July 30 2026, a narrative review in Journal of Computer-Aided Molecular Design described the use of structural modeling and artificial intelligence for functional prediction of proteins encoded by multidrug-resistant bacterial genomes. It reported that tools such as AlphaFold and RoseTTAFold have enabled high-accuracy three-dimensional structure prediction, facilitating annotation of hypothetical proteins and identification of conserved domains and catalytic sites.

Impact 30%
49
Evidence 25%
95
Scale 20%
85
Confidence 15%
87
Recency 10%
86

Updated Jul 31, 2026 · TRV-2026-0599

77Index score

Recomputed live from the record · Oct 11, 2026, 3:26 AM