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

Human-AI co-creation with ChatGPT can function as a collaborative partnership that fosters creativity and innovation in educational settings.

On Dec 7 2024, two teacher-education academics reported an autoethnographic experiment in which each co-produced a creative work with ChatGPT, a poem and a multimodal narrative, and analyzed the process through Don Ihde's alterity relations to explore posthuman creative partnership.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 16, 2026 · TRV-2026-0234

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

A systematic review and bibliometric analysis of 627 articles produced a conceptual framework with two dimensions and four paradigms that advances theoretical understanding of hybrid decision-making and provides actionable insights for organizations in AI-driven environments.

On 2026-04-03, a peer-reviewed article reported a systematic review and bibliometric analysis of 627 articles on human-AI decision-making. The authors identified two critical dimensions, AI-human dynamics and decision typologies, and proposed a novel conceptual framework comprising four paradigms: adaptive intuitive, programmed algorithmic, interpretive analytical, and integrative hybrid decision-making.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 15, 2026 · TRV-2026-0226

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

AI-optimized monoclonal antibody GB-0669 was safe and well-tolerated in healthy adults with dose-proportional pharmacokinetics and a 54-day half-life and dose-dependent serum live virus neutralization.

Researchers conducted pre-clinical studies in cynomolgus monkeys and a first-in-human trial of GB-0669, described as a half-life-extended monoclonal antibody optimized using artificial intelligence targeting the conserved SARS-CoV-2 spike S2 stem helix. Fifty-one healthy adults aged 18-55 received single intravenous doses from 100 to 2400 mg or placebo and were followed for 43 weeks for safety, pharmacokinetics, and serum live virus neutralization.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 15, 2026 · TRV-2026-0225

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

Machine learning screening identified MMP1, CXCR7, and TPST1 and built a NET-inflammation-associated model that predicted prognosis for idiopathic pulmonary fibrosis patients.

On July 14, 2026, a peer-reviewed study reported using machine learning to screen neutrophil extracellular traps-associated inflammatory genes in idiopathic pulmonary fibrosis. The analysis identified three prognostic genes and built a NET-inflammation-associated risk model that predicted patient prognosis, supported by Mendelian Randomization, single-cell expression data, and validation in a bleomycin-induced mouse model with staining, PCR, and immunofluorescence.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 15, 2026 · TRV-2026-0224

AI problems · 625

Recomputed live from the record · Sep 14, 2026, 2:46 PM