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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

301
HealthNewly addedModerate evidence · 1 source

AI models using diffusion tensor imaging to detect white matter changes show potential for early Alzheimer's diagnosis with clinical value.

A 2026 review in Reviews in the Neurosciences examined 98 studies from 2010 to 2026 on diffusion tensor imaging-based AI for Alzheimer's disease, using an Input-Model-Fusion framework to assess modality choice, algorithm paradigms, and fusion strategies.

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

Updated Oct 2, 2026 · TRV-2026-1244

68Index score
302
HealthNewly addedModerate evidence · 1 source

In multicenter retrospective validation, greater concordance between clinician ventilator settings and VentPilot AI recommendations was associated with more ventilator-free days within 28 days, lower 28-day mortality, and shorter ICU length of stay.

Researchers developed VentPilot, an AI-based recommendation system for mechanical ventilation, and tested it retrospectively in cohorts from Seoul National University Hospital and Mayo Clinic totaling 4296 mechanically ventilated adults. Using fitted Q-evaluation and concordance analyses, they estimated higher returns for VentPilot than observed clinician behavior and found higher concordance linked to more ventilator-free days.

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

Updated Oct 2, 2026 · TRV-2026-1243

68Index score
303
ScienceNewly addedModerate evidence · 1 source

Interpretable CatBoost-based machine learning framework predicted GenX-induced biphasic dose-responses in zebrafish with high fidelity, enabling mechanistic ecological risk assessment of PFAS alternatives.

On 2026-10-01, a peer-reviewed study described an interpretable machine learning framework to identify and predict non-monotonic dose-response relationships for GenX, a PFAS alternative, in zebrafish. Using 263 records across 11 biological categories, the CatBoost-based Master Model reached AUC 0.977 and 91.25% accuracy, with SHAP analysis highlighting effect measurement and biological category as key drivers.

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

Updated Oct 2, 2026 · TRV-2026-1242

68Index score
304
HealthNewly addedModerate evidence · 1 source

A LightGBM-based multimodal model integrating ultrasound radiomics, hemodynamic signatures and clinical data achieved high diagnostic accuracy on BI-RADS 4a nodules, supporting safer reduction of unnecessary biopsies.

Researchers developed and validated a multimodal AI model to resolve the diagnostic ambiguity of BI-RADS 4a breast nodules, which have a 2% to 10% malignancy rate and often lead to benign biopsy results. Using data from 350 pathologically confirmed nodules across three hospitals, the LightGBM model combined ultrasound radiomics, hemodynamic features, and clinical profiles, achieving an AUC of 0.95 on a temporally independent test set of 90 nodules as of the October 2026 publication.

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

Updated Oct 1, 2026 · TRV-2026-1239

68Index score

AI problems · 771

301
HealthStableModerate evidence · 1 source

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications.

Large language models (LLMs) may help organize clinical information, but their use in perioperative settings requires careful evaluation because errors may have immediate safety implications. This study aimed to describe the feasibility and perceived usefulness of a single-centre, expert-corrected LLM workflow for preparing preoperative anesthesia assessment drafts for complex consultation cases.

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

Updated Aug 23, 2026 · TRV-2026-0857

68Index score
302
HealthStableModerate evidence · 1 source

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial: Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs.

Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited.

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

Updated Aug 23, 2026 · TRV-2026-0855

68Index score
303
HealthStableModerate evidence · 1 source

These results suggest that to avoid automation bias, maintain accuracy, and achieve efficiency gains, AI deployment requires careful local adaptation.

To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without AI and with each of four AI algorithms.

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

Updated Aug 23, 2026 · TRV-2026-0854

68Index score
304
EducationStableModerate evidence · 1 source

AI-assisted systems may provide real-time guidance for stricture characterization, dilator selection, and procedural endpoints, whereas simulation platforms may facilitate skill acquisition in a risk-free environment.

Although endoscopic training has traditionally relied on mentorship and procedural volume to assess proficiency, the major gastroenterology societies have increasingly adopted competency-based assessment tools, including the Assessment of Competency in Endoscopy (ACE) and Direct Observation of Procedural Skills (DOPS), to provide a more objective evaluation of technical and cognitive skills. Despite these advances, mentor-based assessment remains susceptible to subjectivity.

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

Updated Aug 23, 2026 · TRV-2026-0853

68Index score

Recomputed live from the record · Oct 12, 2026, 2:31 PM