± HEALTH Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and gener…+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…
TruaceTracing the truth around AIMonday, September 14, 2026
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The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,404 results
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AI gains · 779

67
GainScience· Stable· Evidence: Moderate (1 source)

Data-driven machine learning models can rapidly generate biomolecular structures and propose conformational ensembles for recognition events with high predictive performance.

Published July 17, 2026, this perspective argues that quantitative prediction of biomolecular recognition requires moving beyond static structures to ensemble-based thermodynamic and kinetic observables. It reviews physics-based sampling under approximate Hamiltonians and modern machine learning models that learn from structural and bioactivity data.

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

Updated Jul 17, 2026 · TRV-2026-0244

67
GainScience· Stable· Evidence: Moderate (1 source)

MLFFs provide high accuracy with high efficiency for atomic-level studies of inorganic crystalline materials, overcoming traditional limits in structure prediction, properties, defects, and phase transitions.

On 2026-07-17, a review in Physical Chemistry Chemical Physics summarized machine learning force fields for inorganic crystalline materials, describing how they combine first-principles accuracy with classical force-field efficiency to enable atomic-level studies across structural prediction, physical properties, defects and interfaces, and phase transitions.

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

Updated Jul 17, 2026 · TRV-2026-0243

67
GainHealth· Stable· Evidence: Moderate (1 source)

Deep learning CT segmentation using Comp2Comp enabled compartment-specific measurement of visceral adipose tissue, subcutaneous fat, and muscle, revealing sustained visceral fat reduction after metabolic and bariatric surgery that BMI alone does not capture.

Researchers retrospectively analyzed prospectively collected abdominal CTs using Comp2Comp, a validated deep learning pipeline that automatically segments visceral adipose tissue, subcutaneous adipose tissue, and skeletal muscle. They studied 435 adults with BMI >=25 for baseline BMI-VAT relationships and 39 metabolic and bariatric surgery patients with 151 scans followed up to 89 months to track compartment changes.

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

Updated Jul 17, 2026 · TRV-2026-0236

67
GainHealth· Stable· Evidence: Moderate (1 source)

First-year medical students showed a modest net improvement in accuracy after reviewing ChatGPT-generated answers, because incorrect-to-correct changes exceeded correct-to-incorrect changes.

In a July 2026 peer-reviewed study, 57 first-year medical students completed 24 paired clinical and foundational questions during a pediatric nephrology and urology case-based session, answering individually, then viewing a ChatGPT-generated answer that was deliberately correct or incorrect, and re-answering.

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

Updated Jul 17, 2026 · TRV-2026-0235

AI problems · 625

51
ProblemLifestyle· Stable· Evidence: Moderate (1 source)

Almost 50 years after he first got his hands on a computer, the Oxford professor still believes in the power of technology. Can his beloved game theory explain why Silicon Valley’s entrepreneurs consistently misuse it?

Machine-ingested summary: the claims above reflect a single primary source and have not been weighed against contradicting evidence by a Truvace editor yet.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%34
Recency 10%88

Updated Jul 11, 2026 · TRV-2026-0034

Recomputed live from the record · Sep 14, 2026, 4:15 PM