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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

305
HealthNewly addedModerate evidence · 1 source

VoxelMorph achieved comparable deformable registration accuracy to clinical algorithms on thoracic 4D-CT, reaching 0.98 median lung Dice and 36.83 HU median MAE with shorter processing times in the evaluated implementation.

Researchers retrospectively evaluated VoxelMorph deep learning deformable registration on thoracic 4D-CT from 64 lung cancer patients, training on 50 cases and testing on 10 against Demons, modified Demons, and ANACONDA using end-inhalation to end-exhalation registration.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1237

68Index score
306
HealthNewly addedModerate evidence · 1 source

AIxTHY-assisted digital cytology increased sensitivity for higher-risk Bethesda categories and improved agreement with expert consensus while cutting slide review time.

In a 100-slide archival study, two cytopathologists and one cytologist reviewed thyroid fine-needle aspiration cases using microscopy and two AI-assisted whole-slide imaging modes powered by the AIxTHY algorithm. By the October 2026 publication date, the authors reported improved agreement with expert consensus for higher-risk Bethesda categories and a 40% to 65% reduction in review time.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1235

68Index score
307
HealthNewly addedModerate evidence · 1 source

A Super Learner ensemble and derived 17-feature point-based scorecard predicted 1-year pneumonia hospitalization among community-dwelling Japanese adults aged 75+ with good discrimination and calibration on internal test data.

Researchers developed and internally validated a machine learning-based risk scorecard for 1-year pneumonia hospitalization among 1,098,404 community-dwelling Japanese adults aged 75 years or older who completed the QMCOO frailty screening between April 2020 and March 2024. Using Late-Stage Medical Care System claims, a 20-learner Super Learner ensemble and a 17-feature 0-21 point scorecard were trained and tested, with the scorecard achieving AUC 0.786 and good calibration.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1234

68Index score
308
HealthNewly addedModerate evidence · 1 source

Using a Super Learner ensemble to estimate propensity scores, shifting each patient's odds of receiving abciximab during PCI reduced 6-month mortality, with a strong pro-treatment incremental policy showing significant benefit.

Researchers illustrated incremental propensity score interventions using 996 patients undergoing percutaneous coronary intervention, estimating propensity scores and outcomes with a Super Learner ensemble and 10-fold splitting to evaluate shifting each patient's probability of receiving abciximab on six-month mortality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1233

68Index score

AI problems · 771

305
HealthStableModerate evidence · 1 source

OpenEvidence and ChatGPT-5 demonstrated greater agreement with the expert reference standard, although clinically significant diagnostic errors were observed across all evaluated systems.

Purpose The purpose of this study was to evaluate and compare four AI software programs-ChatGPT-5, Microsoft Copilot, Google Gemini (V 2.5), and OpenEvidence-in generating comprehensive dental treatment plans for minimally destructed and severely mutilated teeth using identical clinical inputs. Material and methods Ten anonymized clinical cases, each consisting of 1 intraoral photograph and 1 corresponding periapical radiograph, were independently submitted to each AI software program using a standardized prompt.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 23, 2026 · TRV-2026-0852

68Index score
306
HealthStableModerate evidence · 1 source

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust.

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 22, 2026 · TRV-2026-0847

68Index score
307
Media & ArtsStableHigh evidence · 2 sources

Game studios attempting to replace human writers with AI large language models risks losing human storytelling in games.

What happened: the co-creator and lead writer of Zombies, Run! bought back the studio behind the game, transferring funds via phone to a Singapore company, motivated by a belief that human storytelling must be protected as AI tools spread.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 12, 2026 · TRV-2026-0076

68Index score
308
HealthStableHigh evidence · 2 sources

Predict+ was recalled because the AI-enabled clinical decision support tool for shoulder replacement outcomes lacked required pre-market clearance/approval.

On 2026-02-05, Blue Ortho initiated a recall of Predict+, an AI-enabled clinical decision support tool that uses machine learning to predict outcomes after shoulder replacement surgery, because the device lacked pre-market clearance/approval.

Impact 30%
49
Evidence 25%
100
Scale 20%
35
Confidence 15%
87
Recency 10%
82

Updated Jul 12, 2026 · TRV-2026-0044

68Index score

Recomputed live from the record · Oct 12, 2026, 3:38 PM