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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.

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AI gains · 935

405
ScienceStableModerate evidence · 1 source

Data-efficient exploration of atomic clusters via uncertainty quantification on complex potential energy surfaces: Furthermore, active learning driven by MCD significantly reduces the computational overhead of first-principles calculations while maintaining high predictive accuracy.

Machine learning interatomic potentials have become an effective method for exploring complex potential energy surfaces; however, their application to atomic clusters is frequently hindered by the high cost of sampling diverse isomer spaces and the difficulty in ensuring model generalizability across complex energy landscapes. While uncertainty quantification (UQ) offers a pathway to mitigate data scarcity, its efficacy in capturing continuous potential energy surface features and guiding active learning within the complex landscape of clusters remains systematically unverified.

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

Updated Sep 2, 2026 · TRV-2026-0959

68Index score
406
HealthStableModerate evidence · 1 source

AI-enabled Doubly Robust Machine Learning IV analysis adjusted for nonrandom enrollment among Medicare beneficiaries with cancer and showed that apparent higher inpatient and outpatient use under PDP was explained by selection, supporting more accurate evaluation of benefit integration.

Using Medicare Current Beneficiary Survey data linked to claims from 2019 to 2022, researchers studied 3,140 cancer patients aged 65 and older representing 22.2 million beneficiaries to estimate the causal effect of stand-alone Prescription Drug Plans versus integrated Medicare Advantage Prescription Drug plans. They compared conventional regression, two-stage residual inclusion instrumental variables, and an AI-enabled Doubly Robust Machine Learning IV approach using county-level PDP penetration and white-collar worker percentage as instruments.

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

Updated Sep 1, 2026 · TRV-2026-0958

68Index score
407
HealthStableModerate evidence · 1 source

A pharmacist-overseen AI system for provider organizations could reduce prescriber workload and increase first-pass approval rates by automating routine data extraction and submissions while routing complex cases to pharmacists.

Published September 1, 2026 in Journal of Managed Care & Specialty Pharmacy, this viewpoint proposes a pharmacist-overseen, AI-enabled prior authorization model for provider organizations and health systems. It outlines a conceptual workflow where AI automates routine data extraction and submissions and routes complex cases to pharmacists for clinical verification.

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

Updated Sep 1, 2026 · TRV-2026-0956

68Index score
408
HealthStableModerate evidence · 1 source

Artificial intelligence-based algorithms improve quantification of breast arterial calcifications detected on screening mammography and strengthen their prognostic value for cardiovascular risk in women.

This narrative review examined recent evidence on breast arterial calcifications found incidentally on screening mammography as a potential biomarker of systemic cardiovascular risk in women. It covered observational studies, cohorts, meta-analyses, and studies using artificial intelligence for automated quantification of calcific burden.

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

Updated Sep 1, 2026 · TRV-2026-0954

68Index score

AI problems · 771

405
HealthStableModerate evidence · 1 source

Integration of AI-augmented Digital Twins into healthcare requires addressing ethical, regulatory, safety, privacy, clinical validation and scalability constraints due to sensitive nonlinear human data

This peer-reviewed review from July 2025 examines how Digital Twins that are continuously updated by real-world data, when coupled with Artificial Intelligence, are being applied to healthcare, with emphasis on movement rehabilitation over the past seven years. It reports that this combination is reshaping care by streamlining diagnostic workflows, improving disease management, and enabling experimentation and predictive modeling without direct patient risk.

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

Updated Jul 22, 2026 · TRV-2026-0498

67Index score
406
ClimateStableModerate evidence · 1 source

Machine learning models for water quality forecasting still face persistent challenges in data quality, model interpretability, and integration of spatio-temporal and fuzzy logic techniques.

As of July 28 2025, this peer-reviewed review synthesized machine learning and statistical approaches for forecasting and classifying water quality, focusing on hybrid models that combine multiple methods. It assessed their application to rivers in Malaysia facing pollution from industrialisation, agriculture, and urban expansion, and reviewed standards and interpretability techniques.

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

Updated Jul 22, 2026 · TRV-2026-0497

67Index score
407
CrimeStableModerate evidence · 1 source

Employing machine learning for healthcare fraud detection is limited by poor data quality, scalability issues, regulatory compliance requirements, and resource constraints.

Published August 25, 2025, this peer-reviewed review synthesized current machine learning methods for healthcare fraud detection, covering supervised, unsupervised, deep learning, and hybrid approaches like SMOTE-ENN, explainable AI, federated learning, and ensemble learning, and noted Medicare, LEIE, and Kaggle as common evaluation datasets.

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

Updated Jul 22, 2026 · TRV-2026-0494

67Index score
408
PolicyStableModerate evidence · 1 source

Integration of AI into government decision-making introduces algorithmic bias, transparency deficits, and accountability challenges that raise fairness and privacy concerns.

A systematic review of 43 studies from 2020-2025 examined how artificial intelligence is transforming government decision-making, finding benefits in efficiency and data-driven service delivery alongside drawbacks including bias and transparency deficits.

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

Updated Jul 22, 2026 · TRV-2026-0493

67Index score

Recomputed live from the record · Oct 13, 2026, 4:51 AM