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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

369
HealthStableModerate evidence · 1 source

AI and complementary digital health technologies improved travel healthcare by enhancing pre-travel risk prediction, enabling earlier outbreak detection during travel, and increasing diagnostic accuracy after travel.

This narrative review examined how AI and complementary digital health tools are being used across pre-travel, peri-travel, and post-travel care, covering machine learning risk models, decision-support systems, large language models, wearables, telemedicine, and outbreak surveillance infrastructure.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1045

68Index score
370
HealthStableModerate evidence · 1 source

Undergraduate medical students who used LLM-powered virtual standardized patients as extracurricular self-practice achieved higher end-of-term history-taking performance at an OSCE with real standardized patients compared to routine instruction.

In a 2026 prospective cohort study, 168 third-year medical students were grouped by voluntary use of an LLM-powered virtual standardized patient system for extracurricular history-taking practice versus routine instruction alone. After propensity score matching to 40 pairs, the LLM-VSP group scored higher on an end-of-term Objective Structured Clinical Examination with real standardized patients.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1044

68Index score
371
HealthStableModerate evidence · 1 source

AI tools are entering hand surgery practice to read scaphoid and distal radius radiographs and to predict outcomes after carpal tunnel release.

On September 9, 2026, a peer-reviewed article in The Journal of Hand Surgery described how neural networks, machine learning models, and large language models are entering hand surgery for radiograph reading, outcome prediction, and chart drafting, while noting that adoption has outpaced validation and proposing a four-part stewardship framework for surgeons.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1043

68Index score
372
HealthStableModerate evidence · 1 source

A five-attribute rubric for AI-generated patient-friendly radiology reports showed almost-perfect agreement between lay and radiologist team members and may provide a standardized safeguard before patient distribution.

From February to December 2025, researchers developed a 5-attribute rubric  clarity, content, certainty, tone, verbosity  to grade AI-generated patient-friendly radiology reports, using survey-workshop cycles with 19 participants and testing with ChatGPT-4.1 and Claude-4.0 outputs from public radiology impressions. Evaluation involved six research-team members and 111 additional participants, plus AI evaluation with ChatGPT-5, comparing rubric grades to prespecified reference standards and to subjective decisions about withholding unsafe reports.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1041

68Index score

AI problems · 771

369
HealthStableModerate evidence · 1 source

Deep learning models for knee osteoarthritis progression showed limited generalizability, with performance degradation on external validation and heavy reliance on a single training dataset without rigorous multi-site validation.

This PRISMA systematic review evaluated 33 peer-reviewed studies (2019-2026) comprising 50 deep learning models that predict knee osteoarthritis progression from medical imaging. It extracted AUC as primary outcome, categorized nine different progression definitions, and assessed bias with PROBAST-AI, finding median internal AUCs of 0.87 for surgery, 0.78 for structural, and 0.79 for symptomatic endpoints.

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

Updated Aug 4, 2026 · TRV-2026-0638

67Index score
370
HealthStableModerate evidence · 1 source

Model performance dropped when satisfaction was excluded and estimates from the small same-center temporal validation cohort with only 14 unwilling patients were considered preliminary and potentially imprecise.

Researchers retrospectively analyzed 306 patients who had microscopic root canal treatment with rubber dam isolation between May and November 2025, defining willingness to reuse at 1-week follow-up as the outcome, with 246 willing and 60 unwilling. They trained six models on 26 variables and found the LightGBM model retained 12 predictors and achieved the highest exploratory AUCs of 0.939 and 0.983.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
86

Updated Aug 3, 2026 · TRV-2026-0631

67Index score
371
HealthStableModerate evidence · 1 source

Federated learning models showed modest performance losses compared with centralized models trained on pooled patient data across AUC, F1, sensitivity and PRAUC.

A systematic review and meta-analysis of 13 studies covering 247 sites and 158,435 patient samples evaluated federated learning models, mostly using FedAvg, against local and centralized models on diagnostic performance metrics.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
86

Updated Aug 3, 2026 · TRV-2026-0627

67Index score
372
PolicyStableModerate evidence · 1 source

The same AI electoral systems created concerns about unexplained data anomalies, opaque algorithmic operations, inconsistent security practices, and potential undermining of democratic fairness.

By October 2025, a peer-reviewed study examined AI use in electoral management in Indonesia, Thailand, Philippines and Myanmar between 2019 and 2024, finding that biometric voter identification, cyber-based registration, and real-time result monitoring streamlined administration and improved list accuracy, particularly amplifying coordination in Thailand.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
86

Updated Aug 2, 2026 · TRV-2026-0623

67Index score

Recomputed live from the record · Oct 13, 2026, 12:26 AM