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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 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.

939 gains
772 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.

1,711 results
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AI gains · 939

433
HealthStableModerate evidence · 1 source

Transfer learning enhanced AI lesion detection performance when models were adapted from one regional DBT database to another.

A study tested YOLO-based AI models for breast lesion detection on digital breast tomosynthesis using a 94-patient Western database from the Cancer Imaging Archive and a 157-patient Eastern database from a single medical center, with lesions grouped into six types.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0908

68Index score
434
HealthStableModerate evidence · 1 source

Integrated machine learning models using CT radiomics and clinical predictors achieved accurate preoperative prediction of synchronous liver metastasis in pancreatic ductal adenocarcinoma in validation, intended to assist decisions on surveillance versus biopsy or neoadjuvant therapy for indeterminate subcentimeter CT-

In a retrospective study of 340 pancreatic ductal adenocarcinoma patients, investigators developed machine learning models combining CT radiomics and clinical predictors to predict synchronous liver metastasis preoperatively. In an independent validation cohort of 102 patients, the best linear model (LDA) reached AUC 0.828 and the nonlinear model (MLP) reached AUC 0.822, both showing good calibration with Hosmer-Lemeshow P values of 0.551 and 0.682.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0907

68Index score
435
HealthStableModerate evidence · 1 source

Systematic review and meta-analysis of 15 brain tumor studies found deep learning synthesis of postcontrast T1-weighted MRI from precontrast sequences alone is technically feasible with high whole-image similarity.

Researchers systematically reviewed 41 studies through January 2025 that used deep learning to generate synthetic postcontrast T1-weighted MRI from precontrast images alone, aiming to reduce gadolinium use. Most work was in neuroimaging, using GANs and CNNs, and a targeted meta-analysis of 15 brain tumor studies reported high whole-image similarity metrics.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0904

68Index score
436
ScienceStableModerate evidence · 1 source

Adaptive boosting support vector regression trained on GC parameters predicts retention times with high accuracy, enabling optimization of capillary gas chromatography to separate coeluted C1-C12 hydrocarbon isomers.

Researchers built a machine learning pipeline to predict retention times in capillary gas chromatography, training six algorithms on 608 data points covering 73 instrument settings and 61 C1-C12 hydrocarbons from RESTEK chromatograms and literature. The adaptive boosting support vector regression model achieved R2 scores of 0.992-0.993 on validation and testing sets.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
91

Updated Aug 27, 2026 · TRV-2026-0903

68Index score

AI problems · 772

433
LaborStableModerate evidence · 1 source

Digital HR transformation faces common implementation pitfalls and unresolved gaps in ethical AI governance and longitudinal employee well-being.

Published 19 November 2025, this peer-reviewed review in Administrative Sciences consolidates recent literature on technology-driven change in human resource management. It examines AI, automation and data analytics as drivers and assesses their impact on talent acquisition, development and retention and on organizational design.

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

Updated Jul 20, 2026 · TRV-2026-0446

67Index score
434
LaborStableModerate evidence · 1 source

Integration of AI and automation is reshaping the workforce in ways that create new demands for continuous reskilling, agility, and ethical AI governance to protect employee well-being and maintain competitiveness.

Published November 11, 2025, this peer-reviewed paper examines how AI, RPA, blockchain, and immersive technologies are redefining strategic human resource management. Drawing on a systematic literature review, institutional reports, and illustrative cases from IBM, Walmart, Unilever, and UiPath, it argues human capital has shifted from passive input to strategic enabler and proposes a conceptual model linking emerging technologies, SHRM practices, and competitiveness.

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

Updated Jul 20, 2026 · TRV-2026-0445

67Index score
435
HealthStableModerate evidence · 1 source

Integration of large language models into medical practice creates ethical and regulatory risks including compromised data privacy and rights of use, unclear data provenance, and intellectual property contamination.

A Viewpoint published April 23, 2024 in The Lancet Digital Health examines ethical and regulatory challenges of large language models in medicine, arguing their architecture and emergent abilities set them apart from prior AI and NLP tools.

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

Updated Jul 20, 2026 · TRV-2026-0440

67Index score
436
LaborStableModerate evidence · 1 source

AI recruitment systems risk reproducing bias and discrimination that disproportionately harms vulnerable job applicants.

Published April 8 2024, this peer-reviewed scoping review examines how AI is being adopted in recruitment and selection to enhance HR efficiency, and how that adoption raises concerns about algorithmic decision-making for job seekers.

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

Updated Jul 20, 2026 · TRV-2026-0439

67Index score

Recomputed live from the record · Oct 13, 2026, 7:08 AM