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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

501
HealthStableModerate evidence · 1 source

Integrating pathology foundation models and multimodal AI to connect histology, genomics, spatial biology and longitudinal monitoring enables evolution-aware prediction of lymph-node metastasis and recurrence risk in colorectal cancer.

Published August 14, 2026, this peer-reviewed synthesis argues that lymph-node metastasis prediction in colorectal cancer should move beyond static histology to clonal ecology, integrating computational pathology with evolutionary oncology and AI-enabled tracking of dominant and dormant subclones.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0782

67Index score
502
HealthStableModerate evidence · 1 source

AI provides supervised decision support for fracture triage, preliminary jaw and tooth segmentation, planning preparation, and postoperative measurement in occlusion-oriented maxillofacial fracture reconstruction.

This narrative review from August 2026 summarizes AI applications across occlusion-oriented digital reconstruction of maxillofacial fractures, where treatment must address stable occlusion, mandibular movement, temporomandibular joint position, facial contour, and fixation as interdependent targets. It evaluates tasks from CT/CBCT screening and segmentation to model repair, shape completion, planning assistance, and postoperative deviation analysis.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0781

67Index score
503
HealthStableModerate evidence · 1 source

A random forest model using nine routine blood-based predictors can screen for erectile dysfunction risk with high external validation performance, enabling early non-invasive detection during health check-ups.

Researchers developed and validated a machine learning model to predict erectile dysfunction risk from routine blood test data, using 4116 NHANES participants for training and internal validation and 489 NPTR-confirmed patients for independent external validation. The random forest model achieved the best results in external validation.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 16, 2026 · TRV-2026-0778

67Index score
504
HealthStableModerate evidence · 1 source

Deep learning models applied to standard ECGs can accurately identify arrhythmias, ventricular dysfunction, and congenital heart disease in pediatric populations, supporting earlier detection and risk stratification.

This review from August 2026 summarizes how artificial intelligence applied to standard electrocardiograms has been tested in pediatric and congenital heart disease. It reports that deep learning models have been shown to identify arrhythmias, ventricular dysfunction, and CHD, and are being extended to predict future risk and to analyze wearable and telemetry data.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
89

Updated Aug 15, 2026 · TRV-2026-0774

67Index score

AI problems · 772

502
HealthStableModerate evidence · 1 source

Realizing multi-omics personalized medicine is hindered by complexity of integrating different omics layers, high cost of data generation, and unresolved issues of data privacy, standardization, and validation across diverse populations.

Published November 30, 2024, this peer-reviewed article reviews how combining genomics, transcriptomics, proteomics and metabolomics with machine learning and high-throughput sequencing is being used to tailor therapies to individual genetic and molecular profiles.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0274

67Index score
503
LaborStableModerate evidence · 1 source

AI adoption in organizations can create cultural misalignment and trigger employee resistance alongside ethical concerns.

A systematic literature review published November 28, 2024 examined how artificial intelligence is transforming organizational landscapes. It found AI reshapes work practices through automation and changes to decision making and employee roles, while driving cultural shifts toward innovation, agility, and continuous learning.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0273

67Index score
504
HealthStableModerate evidence · 1 source

Most studies were retrospective and may not reflect real-world performance and access constraints, and CT segmentation remains constrained by subtle early ischemic changes and poor generalization, limiting equitable clinical deployment.

By July 2026, a narrative review of 40 studies from 2020-2025 found deep learning, led by U-Net variants with residual and attention mechanisms and standardized pipelines like nnU-Net, increasingly achieved high Dice scores on MRI DWI/ADC, with many reports above 0.80 and recent transformer and ensemble multisite models approaching 0.90, while CT performance was lower and more variable.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
83

Updated Jul 19, 2026 · TRV-2026-0268

67Index score

Recomputed live from the record · Oct 13, 2026, 3:03 PM