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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

425
HealthStableModerate evidence · 1 source

A prespecified EfficientNet plus Random Forest model classified gross pathology photographs to distinguish NIFTP from IEFVPTC with pooled AUC 0.788, sensitivity 0.588 and specificity 0.944 in 87 patients.

On August 26, 2026, a peer-reviewed study reported a proof-of-concept deep learning approach to distinguish two diagnostically challenging thyroid neoplasms, NIFTP and IEFVPTC, using gross pathology photographs from 87 patients. Using frozen pretrained CNN backbones and traditional classifiers with nested cross-validation, the prespecified EfficientNet plus Random Forest model achieved a pooled AUC of 0.788.

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

Updated Aug 28, 2026 · TRV-2026-0915

68Index score
426
PolicyStableModerate evidence · 1 source

Machine learning and generative AI combined with causal inference frameworks may strengthen oncology real-world evidence used for regulatory decisions.

A 2026 review in Therapeutic Innovation & Regulatory Science assessed artificial intelligence for real-world evidence in oncology from a statistical perspective for regulatory decisions. It described RWE as limited by data quality, population selection, treatment characterization, outcome assessment, and statistical methodology, and discussed machine learning and generative AI combined with causal inference frameworks as approaches that may address those challenges.

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

Updated Aug 28, 2026 · TRV-2026-0914

68Index score
427
HealthStableModerate evidence · 1 source

AI integration in echocardiography workflows reduces examination time and automates measurements, enabling more comprehensive data collection while reducing sonographer fatigue.

A review published August 26, 2026 describes echocardiography workflows transitioning from manual acquisition and measurement to AI-driven interpretation, citing prospective evaluations where AI reduces examination time, automates measurements, and supports integrated assessments of ejection fraction, myocardial texture, and Doppler hemodynamics.

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

Updated Aug 28, 2026 · TRV-2026-0913

68Index score
428
HealthStableModerate evidence · 1 source

Naive CD4+ TCR repertoire features enabled moderate machine-learning classification of celiac disease status and high-accuracy prediction of HLA-DQ2.5 status by publication date.

The study used machine learning on naive CD4+ TCR and naive BCR AIRR-seq repertoires to test classification of celiac disease versus controls, building on prior work linking germline HLA variation to naive repertoire composition and earlier BCR-based classification attempts.

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

Updated Aug 28, 2026 · TRV-2026-0912

68Index score

AI problems · 771

425
EducationStableModerate evidence · 1 source

The same RAG constraints that ensured accuracy limited broader inquiries, creating tension between reliability and comprehensiveness that shaped study routines.

In a medical school basic science course, researchers deployed a retrieval-augmented teaching assistant that limited large language model outputs to instructor-curated materials across two consecutive cohorts. They tracked when and how students used it and what they asked, finding strategic, context-dependent adoption with heavier use during high-stakes assessments and after hours.

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

Updated Jul 20, 2026 · TRV-2026-0459

67Index score
426
HealthStableModerate evidence · 1 source

There is no standardized approach or consensus on AI competencies and ethical frameworks for undergraduate medical education, and no studies assessed impact on critical thinking or clinical reasoning.

A November 2025 scoping review in BMC Medical Education screened 3,238 records and included 310 publications on AI in undergraduate medical education from 2020 to April 2024, finding 52% of the included literature appeared in just eight months after the prior general review. Reported uses span autonomous tutoring, self-assessment, simulation-based learning, assessment generation and grading, clinical assessment, procedural skills evaluation, and predictive analytics in both basic and clinical courses.

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

Updated Jul 20, 2026 · TRV-2026-0458

67Index score
427
LaborStableModerate evidence · 1 source

For software engineers in early-stage integration, expected adoption drivers such as perceived usefulness, social factors, and personal innovativeness had less pronounced impact than conventional technology acceptance theories predict.

In a study published March 28 2024, researchers examined generative AI tool adoption among software engineers using surveys of 100 engineers and validation with 183 engineers, developing and testing the Human-AI Collaboration and Adaptation Framework.

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

Updated Jul 20, 2026 · TRV-2026-0455

67Index score
428
PolicyStableModerate evidence · 1 source

Deepfakes that falsely announce surrender or truce declarations can place soldiers and civilians at greater risk and may constitute violations of international humanitarian law.

A peer-reviewed discussion published 5 November 2025 examines whether AI-generated deepfakes used to deceive an enemy during war comply with international humanitarian law. It notes commanders may seek tactical advantage by making opposing forces and civilian populations believe X when Y is true, including fabricated surrender or truce announcements.

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

Updated Jul 20, 2026 · TRV-2026-0452

67Index score

Recomputed live from the record · Oct 13, 2026, 6:25 AM