+ BUSINESS Abstract This study presents a comprehensive literature review using a systematic approach to explore the rol…+ SCIENCE This study investigates the impact of a novel application of generative artificial intelligence (AI) in physi…− HEALTH The rapid integration of artificial intelligence (AI) into everyday life has created new intersections among…+ HEALTH Introduction Stereotactic Radiotherapy plays a main role in Brain Metastases treatment. Radiomics and Dosiomi…+ HEALTH Background Internet Gaming Disorder (IGD) poses significant psychological and social risks for adolescents. I…± HEALTH Background Major depressive disorder (MDD) is associated with high relapse rates, with around 20% of inpatien…± HEALTH Despite a decade in, immunotherapy (IO) treatment selection in non-small cell lung cancer (NSCLC) remains lar…+ HEALTH Rib fractures are a common type of chest injury, and the estimation of the fracture age mainly relies on clin…
TruaceTracing the truth around AITuesday, September 15, 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.

1,419 results
Show filters and sorting

AI gains · 788

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

A convolutional neural network using standard 12-lead ECGs can estimate elevated NT-proBNP levels with strong correlation and good discrimination in internal and external validation.

Investigators built and validated an AI model that estimates serum NT-proBNP levels from routine 12-lead ECGs, training on nearly 85,000 ECG-lab pairs and testing internally in over 8,500 patients and externally in 679 patients at two tertiary centers.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%94

Updated Aug 15, 2026 · TRV-2026-0773

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

The review proposes an integrated dual-layer governance model that aligns AI-specific risk identification with ISO 14971 processes and EU AI Act obligations, giving regulators and manufacturers a clearer actionable pathway for lifecycle monitoring.

A peer-reviewed review published August 13, 2026 examined how AI-enabled medical devices challenge traditional safety-risk management. Drawing on 19 academic and regulatory sources, it found ISO 14971, AAMI CR34971 and the EU AI Act each cover parts of device safety and algorithmic governance but remain fragmented in practice.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%94

Updated Aug 15, 2026 · TRV-2026-0771

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

A delta-ML graph neural network using GFN2-xTB geometries and DFT single-points as low-fidelity inputs predicted PBE0-D3BJ and PBE-D3BJ level properties of transition metal complexes with higher accuracy and better data efficiency than a conventional benchmark.

Researchers presented a delta-ML approach for transition metal complexes that uses GFN2-xTB geometry optimizations combined with DFT single-point calculations to produce low-fidelity inputs, which are converted to graph representations for a graph neural network trained to predict high-fidelity quantum properties from the tmQMg dataset.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%94

Updated Aug 15, 2026 · TRV-2026-0770

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

ML-based phenomapping of 106,490 Danish KC patients identified 7 distinct subgroups differentiated by disease burden, comorbidities and socioeconomic status, providing a basis for tailored clinical pathways.

On 2026-08-13, a peer-reviewed study reported machine-learning-based phenomapping of 106,490 keratinocyte carcinoma patients from the Danish Skin Cancer Registry (2014-2022). The model derived seven clusters ranging from young, well-educated, high-income, medically noncomplex females with low-risk BCCs to highly comorbid patients with more SCCs and immunosuppressive drug exposure.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%94

Updated Aug 15, 2026 · TRV-2026-0768

AI problems · 631

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

Human-in-the-loop AI faces persistent concerns about algorithmic accountability, interpretability, and safety, plus challenges with workflow integration and regulatory gaps for adaptive systems.

A February 2026 narrative review in the International Journal of Medical Informatics synthesized studies from 2018 to 2025 on human-in-the-loop AI in healthcare. It found HITL approaches applied across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis, with evidence of improved diagnostic accuracy, reduced medical errors, and increased clinician trust versus automated or traditional care.

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

Updated Jul 20, 2026 · TRV-2026-0379

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

AI systems designed to enhance flourishing may systematically erode the human capacities needed to achieve wellbeing

Published February 20 2026, this peer-reviewed paper argues that while AI offers scalable mental health support, emerging patterns suggest those same systems may undermine wellbeing. It introduces the AI-IARA framework naming six capacities - Awareness, Interpretation, Intention, Action, Relational Agency and Autonomy - as essential for wellbeing when AI mediates experience.

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

Updated Jul 20, 2026 · TRV-2026-0376

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

Routine reliance on generative AI chatbots can lead people to develop inaccurate beliefs and delusional self-narratives, with extreme cases described as AI-induced psychosis.

A peer-reviewed philosophy paper published February 11, 2026 argues that false outputs from systems like ChatGPT, Claude, Gemini, DeepSeek and Grok should be understood not only as AI hallucinating at users, but as humans hallucinating with AI. Through distributed cognition theory, it describes how routine reliance on chatbots to think, remember and narrate can embed errors and reinforce users' own distorted beliefs.

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

Updated Jul 20, 2026 · TRV-2026-0375

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

Misuse of LLMs in manuscript preparation and peer review risks hallucinations, confidentiality breaches, erosion of critical skills, and proliferation of low-quality manuscripts.

By February 2026, 53 editors-in-chief or delegates in anaesthesiology and pain medicine completed a three-round modified Delphi to produce 59 statements on responsible LLM use. The group agreed LLMs may help with limited editorial and authorial tasks if fully disclosed and human-verified, but must not create original ideas, data, references, conclusions, full manuscripts, or make editorial or peer-review decisions.

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

Updated Jul 20, 2026 · TRV-2026-0374

Recomputed live from the record · Sep 15, 2026, 2:29 PM