± HEALTH Colorectal Cancer (CRC) is a common cause of cancer death and prognostic factors are used to determine manage…+ HEALTH This multicenter study developed and validated an interpretable machine learning model integrating granular n…+ HEALTH Multiple stakeholders need to locate results of registered clinical trials but frequently struggle to find th…+ HEALTH Accurate preoperative glioma grading and molecular subtyping are important for treatment. The vascular microe…+ HEALTH Gastrointestinal endoscopy generates extensive high-resolution video data, posing significant challenges for…+ HEALTH Non-invasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP) and invasive encapsulat…+ POLICY Artificial intelligence (AI) has the potential to strengthen real-world evidence (RWE) for regulatory decisio…± HEALTH Echocardiography remains the cornerstone of cardiovascular imaging. However, traditional workflows including…
TruaceTracing the truth around AIFriday, August 28, 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.

1,182 results
Show filters and sorting

AI gains · 658

68
GainHealth· Stable· Evidence: Moderate (1 source)

START-AI improved prediction of inpatient admission from the emergency department from AUROC 0.78 to 0.90 when ensemble and transformer features were added.

In a single-centre study using two years of ED electronic records, investigators evaluated the Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) by adding features one by one and calculating permutation importance and SHAP values for inpatient admission prediction.

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

Updated Aug 1, 2026 · TRV-2026-0618

68
GainHealth· Stable· Evidence: Moderate (1 source)

AI systems can provide simple, clear, well-documented answers to clinical questions about managing patients with hypertension to assist practicing physicians.

By August 2026, peer-reviewed discussion in Giornale Italiano di Cardiologia described AI as entering hypertension care, able to give simple and well-documented answers to management questions for practicing physicians, while also being explored in research to identify secondary hypertension and predict future hypertension and complications such as heart failure.

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

Updated Aug 1, 2026 · TRV-2026-0617

68
GainHealth· Stable· Evidence: Moderate (1 source)

Integrating large-scale transcriptomic profiling with machine learning identified a three-gene blood signature that enables early sepsis diagnosis and risk stratification across severity levels.

By August 1, 2026, researchers reported integrating large-scale transcriptomic profiling with machine learning to identify TLR5, HMGB2, and C19orf59 as a blood-based diagnostic signature for sepsis. They mapped expression to myeloid cells and tested the panel across SOFA-defined severity strata, then validated it in sham-controlled CLP mice, LPS-stimulated cells, and sepsis patient serum.

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

Updated Aug 1, 2026 · TRV-2026-0615

68
GainEducation· Stable· Evidence: Moderate (1 source)

AI-generated subtitles for ECFS e-learning modules enabled quick and affordable creation of multilingual education packages in Ukrainian, Romanian, and Turkish that achieved high accuracy after expert editing.

Researchers piloted AI translation to subtitle ECFS peer-reviewed e-learning modules for cystic fibrosis care, creating six-module packages in Ukrainian, Romanian, and Turkish. Each AI draft was reviewed and edited by two native-speaking CF healthcare experts, and an online survey of users in two countries collected 18 responses on quality.

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

Updated Aug 1, 2026 · TRV-2026-0614

AI problems · 524

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

AI integration in radiologic practice may paradoxically increase workload and contribute to radiologist burnout when poorly implemented, with automation bias and over-reliance compromising clinical judgment

This narrative review from June 2026 synthesized evidence on AI integration in radiology, finding that by that date AI systems had shown diagnostic performance approaching or exceeding radiologists in chest imaging and breast cancer screening and had improved triage and reduced report turnaround times in practice.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0311

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

Adoption of AI in fragile humanitarian environments creates substantial risk of security breaches from human errors and unregulated data management, and risks reinforcing existing power imbalances for workers and communities.

A peer-reviewed commentary from April 2026 describes how AI tools are being adopted in the humanitarian cooperation sector to improve health diagnostics, service quality, and efficiency of analysis and data management for emergency responses in conflict areas with limited resources.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0310

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

GPT-Reviewer showed near-zero agreement with human QUIPS ratings for study participation and outcome measurement, with kappa 0.001.

Researchers nested a two-part methodological study within two PROSPERO-registered reviews to test customized GPT models on complex rheumatology evidence synthesis. Fifteen SLE metabolomics studies were used to compare human and GPT data extraction, and nineteen rheumatology prognostic studies were reappraised in 2025 with GPT-Reviewer against adjudicated human QUIPS ratings using weighted kappa.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0308

68
ProblemHealth· Stable· Evidence: Moderate (1 source)

Translational progress of AI applied to bioink formulation and bioprinting for bone regeneration remains constrained by small datasets, limited cross-platform validation, and weak links between computational predictions and biological outcomes.

By July 10 2026, a peer-reviewed review in Tissue Engineering Part B: Reviews evaluated experimentally validated AI uses in scaffold-based bone regeneration, from materials design to fabrication control and biological assessment. It reported that physics-informed models tend to be more robust and generalizable than purely data-driven models, while transfer learning is hampered by variability in cellular responses and fabrication conditions.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%93

Updated Jul 20, 2026 · TRV-2026-0288

Recomputed live from the record · Aug 28, 2026, 9:51 AM