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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 932 sourced gains and 770 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

932 gains
770 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

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AI gains · 932

101
HealthStableModerate evidence · 1 source

Adding 913 conditional-GAN simulated ganglionic tiles to 461 real ganglionic and 1374 real aganglionic FCM tiles improved a CNN's ability to discriminate ganglionic bowel on an independent real-image test set of 590 aganglionic and 198 ganglionic tiles, raising accuracy from 75% to 84% and sensitivity from 78% to 91% .

Researchers evaluated whether synthetic fluorescence confocal microscopy images generated by a conditional generative adversarial network could improve deep-learning detection of ganglionic bowel during surgery for Hirschsprung Disease. Using real intraoperative FCM tiles collected from November 2024 to November 2025, they compared CNNs trained on real data alone versus real data plus cGAN-simulated ganglionic tiles versus real data plus conventional augmentation, testing all models on an independent set of real tiles.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
95

Updated Sep 13, 2026 · TRV-2026-1066

74Index score
102
HealthStableModerate evidence · 1 source

FedMediFormer-XAI combined federated learning, multimodal transformers, diffusion augmentation and GNN drug recommendation to achieve 94.2% accuracy for diabetes prediction and NDCG 0.91 for personalized drug recommendation while enabling privacy-preserving training without sharing raw patient data.

Researchers described FedMediFormer-XAI, a framework that combines federated learning, multimodal transformers, diffusion-based data augmentation, graph neural networks for drug recommendation, and explainable AI to address fragmented diabetes data, privacy concerns, and lack of personalized guidance. It was tested on diverse inputs including clinical records, glucose monitoring, retinal fundus images, wearable sensors, and pharmacological information.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1052

74Index score
103
HealthStableModerate evidence · 1 source

In a 20-case test of congenital anomalies and tumors, ChatGPT, Gemini and Copilot achieved 80-95% diagnostic accuracy with detailed anatomical descriptions, suggesting potential as supplementary radiological diagnostic support.

On September 9, 2026, a peer-reviewed comparative study reported testing ChatGPT, Gemini, and Microsoft Copilot on 20 radiological cases split between congenital anomalies and tumors. Each system received the same questions and images and was scored for diagnostic accuracy and explanatory completeness, with ChatGPT scoring 19 correct, Gemini 17, and Copilot 16.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 10, 2026 · TRV-2026-1047

74Index score
104
HealthStableModerate evidence · 1 source

An ensemble deep learning model combining AP and lateral skull X-rays detected skull fractures in neonates and infants with 91.6% accuracy and 0.938 AUC on external validation, improving diagnostic accuracy while reducing need for CT.

By September 7, 2026, researchers reported developing an AI-assisted ensemble model to detect skull fractures in neonates and infants from plain radiographs. Using a retrospective set of 1,184 patients from 2010-2021, they preprocessed images with CLAHE and trained three CNNs, with DenseNet-121 performing best, then combined AP and lateral views into an ensemble that achieved 0.938 AUC and 91.6% accuracy on an external set of 460 images.

Impact 30%
69
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
94

Updated Sep 9, 2026 · TRV-2026-1031

74Index score

AI problems · 770

101
HealthStableModerate evidence · 1 source

Clinical use of AI is limited by hallucinations, algorithmic bias, data protection requirements, and regulatory considerations that require continuous human oversight.

A July 2026 review in Die Urologie describes artificial intelligence moving from research into everyday clinical practice and hospital care, with generative AI and large language models now used alongside established image-analysis tools for documentation, knowledge management, patient communication, and workflow optimization, plus AI-assisted radiological and pathological interpretation and risk stratification.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
86

Updated Jul 31, 2026 · TRV-2026-0601

72Index score
102
ClimateStableModerate evidence · 1 source

Implementation of AI in waste management faces challenges due to financial and personnel constraints.

A peer-reviewed study published October 4, 2025 examined AI for municipal waste management in Industry 4.0. Based on a 2024 online survey of 78 respondents mainly from Europe with experience or interest in AI, logistics, and ecology, authors reported that 78% saw AI reducing waste management costs, 59% saw greatest benefits in sorting and recycling, and 51% saw effectiveness in optimizing collection routes.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
86

Updated Jul 29, 2026 · TRV-2026-0585

72Index score
103
LifestyleStableModerate evidence · 1 source

Deployment of ML for food quality control is constrained by data scarcity, domain coverage biases, and challenges integrating with legacy systems while meeting regulatory compliance and cost-benefit requirements.

On 2025-10-04, a peer-reviewed review in Foods synthesized 25 studies selected from 124 Scopus records from 2005-2025 to map machine learning use for quality control in food production. It organized findings into six domains covering quality applications, defect detection and visual inspection, ingredient optimization, packaging sensors and predictive QC, supply chain traceability, and Industry 4.0 models.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 22, 2026 · TRV-2026-0481

72Index score
104
HealthStableModerate evidence · 1 source

Deployment of AI in healthcare raises ethical challenges and risks related to data privacy and algorithmic bias that must be mitigated.

This peer-reviewed review from March 2024 surveys how artificial intelligence is being integrated across hospitals and clinics, covering clinical decision support, operational management, medical image analysis, and patient monitoring with AI-powered wearables, drawing on case studies of domain-specific transformation.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0454

72Index score

Recomputed live from the record · Oct 11, 2026, 11:47 AM