± 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 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.

1,404 results
Show filters and sorting

AI gains · 779

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

AI and NLP models trained on text data can detect and analyze patterns across multiple categories of online fraud.

On June 13 2025, Crime Science published a systematic literature review of AI and NLP for online fraud detection. The authors screened 2457 records and analyzed 223 studies, mapping data sources, algorithms, and evaluation metrics across 16 fraud types and summarizing best-performing methods for detecting scams in text.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%90

Updated Jul 24, 2026 · TRV-2026-0521

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

A proposed taxonomy for automated decision-making could improve identification of fundamental rights at stake in public migration, asylum and mobility decisions.

Published March 2024, this peer-reviewed article analyzes automated systems used in public decision-making for migration, asylum and mobility. It finds that GDPR and AI Act definitions centered on fully automated decisions miss common practices where systems assist human decision-makers, and it proposes a taxonomy to support fundamental rights analysis.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%90

Updated Jul 23, 2026 · TRV-2026-0519

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

AI collected patient histories more effectively than general practitioners during initial consultations, with stronger performance in older patients and for allergies and family history of cancer.

In an online cross-sectional study in France with 204 general practitioners and 942 patient histories, researchers compared AI to physicians in recording histories during initial consultations across categories such as chronic disease, surgical, obstetric, occupational, allergies, and family cancer history.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%90

Updated Jul 23, 2026 · TRV-2026-0518

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

Large language models, clustering and sentiment analysis demonstrated in a precision oncology project and integrated in ImpleMATE platform enable continuous knowledge extraction and decision support to improve implementation of evidence-based interventions in routine health care.

The paper reviews persistent bottlenecks in moving evidence-based interventions into routine care and describes how data science and AI methods can help extract and synthesize implementation materials, analyze context, and support stakeholder engagement and adaptation, illustrated with a live precision oncology project and the ImpleMATE platform.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%90

Updated Jul 23, 2026 · TRV-2026-0517

AI problems · 625

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

AI support for robot-assisted distal pancreatectomy falsely recognized the dissection line, resulting in pancreatic tissue damage reported to FDA.

On 2026-06-23, an FDA device event record for Davinci xi by Intuitive surgical, inc. cited a literature case report on AI for surgical support in robot-assisted distal pancreatectomy. The record states the dissection line on the lower margin of the pancreas was falsely recognized and pancreatic tissue was damaged during the procedure.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%89

Updated Jul 19, 2026 · TRV-2026-0271

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

Lower-extremity amputees received Rheo Knee AI prosthetics that had been released without completing the full assembly process, triggering a recall for acceptance testing

Ossur recalled its Rheo Knee bionic prosthetic after an internal audit found units were released for distribution without fully going through the assembly process. The device is described as using Artificial Intelligence to adapt to walking style and environment for lower extremity amputations, and customers were told on February 26, 2015 to return units for acceptance testing.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0042

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

Customer concern that the Reveal LINQ implantable monitor's AI adjudication classified true pause episodes as false, following undersensing and false pause episodes

The FDA received a malfunction report for the Reveal LINQ implantable cardiac monitor made by Medtronic europe sarl. The report describes false pause episodes due to undersensing and a customer concern that the device's AI algorithm adjudicated episodes the customer considered true pauses as false. The device remains implanted.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0041

61
ProblemHealth· Stable· Evidence: Moderate (1 source)

The Accurhythm za420 pause monitor's AI algorithm rejected true pause episodes as false, causing a detection failure

The FDA received a malfunction report for the Accurhythm za420 (pause) device made by Medtronic, inc. The report stated that the device's artificial intelligence algorithm rejected true pause episodes as false. The source notes that no patient complications have been reported as a result of this event.

Impact 30%49
Evidence 25%85
Scale 20%35
Confidence 15%62
Recency 10%88

Updated Jul 12, 2026 · TRV-2026-0040

Recomputed live from the record · Sep 15, 2026, 4:04 AM