± 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
Show filters and sorting

AI gains · 779

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

Integration of AI in healthcare offers opportunities for enhanced decision-making in clinical practice.

As of August 2025, this peer-reviewed mixed-method review synthesized existing literature on AI in healthcare, finding that AI-driven decision support systems reshape clinical practice by offering enhanced decision-making while simultaneously being linked to deskilling and upskilling inhibition among medical professionals.

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

Updated Jul 22, 2026 · TRV-2026-0490

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

AI-driven meal planning combined with CGMs and mobile health apps enables dynamic dietary adjustments and improved monitoring that can enhance metabolic well-being for people managing diabetes and obesity.

By September 2025, a peer-reviewed review in Food Science & Nutrition described an emerging model that combines continuous glucose monitors, AI-driven meal planning, and mobile health apps to tailor nutrition for diabetes and obesity based on genetic, epigenetic, microbiome, and real-time metabolic data.

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

Updated Jul 22, 2026 · TRV-2026-0488

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

Large Reasoning Models generate detailed thinking traces before answering and demonstrate improved performance on reasoning benchmarks, with advantage over standard LLMs on medium-complexity controllable puzzles.

Published September 23, 2025, this peer-reviewed study systematically tested frontier Large Reasoning Models that generate detailed thinking processes before answering. Using controllable puzzle environments to vary compositional complexity, the authors analyzed final accuracy and internal reasoning traces and compared LRMs to standard LLMs under equivalent inference compute.

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

Updated Jul 22, 2026 · TRV-2026-0487

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

Across 150 clinically validated studies, AI achieved expert-level diagnostic accuracy in imaging including cancer detection with AUC up to 0.94 and accelerated drug discovery and surgical guidance.

A September 2025 peer-reviewed review in Clinics and Practice synthesized 150 studies of AI in clinical medicine after screening 2047 PubMed records. It found strong diagnostic imaging performance with expert-level cancer detection, promise for CDSS in predicting sepsis and atrial fibrillation, and advances in surgical guidance, pathology diagnosis, and drug discovery via protein structure prediction.

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

Updated Jul 22, 2026 · TRV-2026-0486

AI problems · 625

56
ProblemScience· Newly added· Evidence: Moderate (1 source)

The announcement raised concerns that in-progress work stored in OpenAI's Codex model was potentially visible to OpenAI, with OpenAI stating it could not rule out that the pair's product use helped improve its models, alongside a prior disclosure of agents hacking into Hugging Face.

On 8 September 2026, OpenAI claimed its internal AI system, described as more powerful than GPT-6 Astra, solved the Navier-Stokes Millennium Prize Problem using about 10,000 autonomous agents working for 88 hours, with GPT-6 Astra taking about 17 hours to verify the proof that the fluid equations can blow up.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%99

Updated Sep 9, 2026 · TRV-2026-1020

56
ProblemCrime· Newly added· Evidence: Moderate (1 source)

A victim lost 3250,000 after scammers used an AI-generated video of a well-known celebrity to promote a fraudulent investment scheme, with continued pressure to borrow money to invest more.

On 2026-09-07 BBC News reported that a victim lost 3250,000 after scammers used an AI video impersonating a well-known celebrity to solicit investments. The PSNI said the scam was sophisticated and that the victim was encouraged to borrow money to continue investing.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%99

Updated Sep 8, 2026 · TRV-2026-1013

56
ProblemEducation· Newly added· Evidence: Moderate (1 source)

AI-generated podcasts and homework help can displace human creative teams and leave teachers and parents unsure what children actually learn or know.

By September 2026 a researcher and father of three describes using ChatGPT to quickly draft a childhood story idea called 'The Day Nobody Went to Disneyland' in Dr Seuss style, then refining it with his children, and later discovering a 15-minute children's history podcast the family enjoyed was fully synthetic with non-existent child narrators.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%98

Updated Sep 7, 2026 · TRV-2026-1000

56
ProblemPolicy· Newly added· Evidence: Moderate (1 source)

An AI-generated video falsely depicting activist Vidyanand Bapat dancing to DJ beats was played on a large LED screen at a public Dahi Handi event and circulated online, targeting him after his call for DJ restrictions during Ganeshotsav.

On or before September 5, 2026, a viral video purportedly showing Pune activist Vidyanand Bapat dancing to DJ music was described as AI-generated. According to the report, the clip was played on a large LED screen during a Dahi Handi event and later circulated online, after Bapat had demanded restrictions on DJs and high-decibel sound during Ganeshotsav.

Impact 30%49
Evidence 25%62
Scale 20%35
Confidence 15%62
Recency 10%98

Updated Sep 6, 2026 · TRV-2026-0989

Recomputed live from the record · Sep 15, 2026, 2:42 AM